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This article was updated on January 23, 2024. Sometimes you have to break from tradition and look to modern solutions to address modern problems. As consumers increasingly expect fast-paced digital experiences, lenders are tapping into advances in computing power to enhance their operations. According to a 2022 Experian study, 66% of businesses believe advanced analytics, including machine learning and artificial intelligence, are going to rapidly change the way they do business.1 While some may feel wary about trusting automated systems, remember that you're in control of the strategy. Automation comes in after to help take over monotonous and complex or error-prone tasks. As a result, you can free up resources for work that isn't as well-suited for automation, such as analyzing results and revising strategies. The benefits of automation within loan origination From initial screenings to determining a final decision or credit limit, automation can offer benefits throughout the loan origination process. And lenders of all sizes are exploring opportunities for automation to help them: Manage an overwhelming number of applications:  Lenders may be struggling to respond to an increased demand for credit, particularly if they're also dealing with staffing shortages and rely on manual inputs and reviews. Automation can remove some of the burden from employees and lead to faster decisions. Increase consistency and accuracy:  Transposing information from applications and making calculations by hand can result in errors or inconsistent results. Modern automated systems can help ensure information is accurate, uniform and up to date. Create scalable processes:  Automated processes are easier to scale than a strategy that relies on consistent manual reviews and frequent back-and-forth with customers. Improve customers' experiences:  Fast, accurate and fair decisions make for happy customers. However, 58% don't feel that businesses completely meet their expectations for their online experience.2 What's more, 91% of online applications are abandoned before completion.3 More loans, a consistent scalable process and happy customers can all drive revenue growth. When integrated throughout the underwriting journey, automation can also help you increase conversion rates and expand your lending universe without taking on more risk. What does an optimized and automated loan origination process look like? Modern loan origination software offers flexibility, security, speed and robust integrations. These can be cloud-based systems that vendors create and manage on your behalf, or lenders that have the resources and capabilities may be able to bring (or build) them in house. Strategy first Automating parts of your origination process can save you time and money, but you have to start with a specific strategy. For example, you might consider your model's outputs and decide on denial and approval cut-off points — you can then automate those approvals and denials. You can also test, revise, and optimize strategies based on your desired results. Digital applications Let consumers apply when and how they want, even if it means pausing part-way through and continuing on a different device later. Remove potentially time-consuming steps by letting consumers upload and sign documents digitally, and use AI-driven automated systems to review the documents for accuracy.4 Integration with various data sources You need good data—and lots of it—to get the most out of an automated system. Some platforms can automatically connect and use internal data alongside third-party data sources, such as alternative data, credit bureau data and credit attributes. Identity, income and fraud checks Automated platforms can work with verification tools to quickly confirm the applicant's employment and income, confirm their identity and perform fraud checks. The process can take minutes rather than days or weeks, letting you quickly move applicants through to the next stage of the process. Decisions based on optimized models Automated decision engines use your strategy and the available data to quickly return a decision. Machine learning models can score consumers who aren't scorable by traditional credit models, expanding your potential customer base while furthering financial inclusion goals. They can also more accurately score applicants and narrow the band (and potentially the number of applications) that requires manual reviews.5 Automation in action:  Atlas Credit, a small-dollar lender, wanted to modernize its lending with customized and automated systems. Experian helped them build a custom machine learning credit risk model and optimized their decision strategy and cutoffs. The results exceeded Atlas Credit's goals, and the company  nearly doubled their loan approval rates  while decreasing risk losses by 15 to 20 percent. Explainable results Automated, fast decisions based on machine learning and AI analytics might raise some compliance flags—but we've moved beyond black box models. You need to be aware of and follow all the applicable regulations, and you can use AI and machine learning in precise ways to increase your efficiency while having fully explainable and compliant results. Experian's automated offerings build on a history of success Experian has decades of experience helping lenders make accurate and timely credit decisions, and our flexible loan origination system can help you automate originations while managing risk. It starts with good data. While we're known for our consumer credit database that has information on over 245 million consumers, Experian can also give lenders access to alternative data, including alternative financial services, rental payment data and consumer-permission data. And we know how to incorporate your internal data to create strategies that will further your specific goals. From marketing to collections, our integrated offerings can help you use the data to automate and optimize decisions across the entire customer life cycle. And whether you want to take the reins or tap our data scientists for their expertise, there are options to fit your needs. Learn more about our suite of loan origination software solutions and PowerCurve® Originations Essentials, our automated decision engine. Learn more 1Experian (2022). Explainability: ML and AI in credit decisioning2Experian (2022). North America findings from the 2022 Decisioning Survey 3Experian (2023). eBook: The Ultimate Guide to Competitive Growth 4Ibid.5Experian (2022). Driving Growth During Economic Uncertainty with AI/ML Strategies

In today’s highly competitive landscape, credit card issuers face the challenge of optimizing portfolio profitability while also effectively managing their overall risk. Financial institutions successfully navigating the current market put more focus on proactively managing their credit limits. By appropriately assigning initial credit limits and actively overseeing current limits, these firms are improving profitability, reducing potential risk, and creating a better customer experience. But how do you get started with this important tool? Let’s explore how and why proactive credit limit management could impact your business. The importance of proactive credit limit management Enhanced profitability: Assigning the optimal credit limit that caters to a customer’s spending behavior while also considering their capacity to repay can stimulate increased credit card usage without taking on additional risk. This will generate higher transaction volumes, increase interest income, promote top-of-wallet use, and improve wallet share, all positively impacting the institution’s profitability. Mitigating risk exposure: A proactive review of the limits assigned within a credit card portfolio helps financial institutions assess their exposure to overextended credit usage or potential defaults. Knowing when to reduce a credit limit and assigning the right amount can help financial institutions mitigate their portfolio risk. Minimizing default rates: Accurately assigning the right credit limit reduces the likelihood of customers defaulting on payments. When an institution aligns their credit limits with a cardholder's financial capability, it reduces the probability of customers exceeding their spending capacity and defaulting on payments. Improving the customer experience: A regular review of a credit card portfolio can help financial institutions find opportunities to proactively increase credit limits. This reduces the need for a customer to call in and request a higher credit limit and can increase wallet share and customer loyalty. Strategies for effective credit limit management Utilizing advanced analytics: Leveraging machine learning models and mathematically optimized decision strategies allows financial institutions to better assess risk and determine the optimal limit assignment. By analyzing spending patterns, credit utilization, and repayment behavior, institutions can dynamically adjust credit limits to match evolving customer financial profiles. Regular review and adjustments: As part of portfolio risk management, implementing a system for a recurring review and adjustment of credit limits is crucial. It ensures that credit limits are still aligned with the customer's financial situation and spending habits, while also reducing the risk of default. Customization and flexibility: Personalized credit limits tailored to individual customer needs improve customer satisfaction and loyalty. Proactively increasing limits based on improved creditworthiness or income reassessment can foster stronger customer relationships. Protect profitability and control risk exposure  Using the right data analytics, processing regular reviews, and customizing limits to individual customer needs helps reduce risk exposure while maximizing profitability. As the economic landscape evolves, institutions that prioritize proactive credit limit management will gain a competitive edge by fostering responsible customer spending behavior, minimizing default rates, and optimizing their bottom line. With Experian, automating your credit limit management process is easy Experian’s Ascend Intelligence ServicesTM Limit provides you with the optimal credit limits at the customer level to generate a higher share of plastic spend, reduce portfolio risk, and proactively meet customer expectations. Let us help automate your credit limit management process to better serve your customers and quickly respond to the volatile market. To find out more, please visit our website. Ready for a demo? Contact us now!

It is a New Year and a new start. How about a new job? That is what thousands of employees will consider over the next month. It is also a time for employers to attract new talents, but they must be aware of different types of employment fraud. The rise of remote work has significantly increased the prevalence of remote hiring practices, from the initial job application to the onboarding process and beyond. Unfortunately, this shift has also opened the door to a surge in imposter employees, also known as ‘candidate fraud,’ posing a significant concern for organizations.  How does employment identity theft happen?  Instances of potential job candidates utilizing real-time deepfake video and deepfake audio, along with personally identifiable information (PII), during remote interviews to secure positions within American companies have been on the rise. The Federal Bureau of Investigation (FBI) reports that fraudulent individuals often acquire PII through fake job opening posts, which enable them to gather candidate information and resumes. Surprisingly, the tools necessary for impersonation on live video calls do not require sophisticated or expensive hardware or software. Employment identity theft can occur in several ways. Here are a few examples:  Inaccurate credentials: Employers may inadvertently hire someone with false or stolen credentials if they fail to conduct comprehensive background checks. When the employer discovers the deception, it can be challenging to trace the true identity of the person they unknowingly hired.  Limited-term job offers: Some industries offer temporary job opportunities in distant locations. Individuals with criminal backgrounds may steal victims' identities to apply for these jobs, hoping that their crimes will go unnoticed until after the job is complete.  Perpetrated by colleagues: In rare instances, jealous colleagues or coworkers can commit employment identity theft. They may steal a coworker's information during a data breach and sell it on the dark web or use the victim's credentials to frame them for fraudulent workplace actions.  Preventing employment identity theft  In addition to the reported cases of imposter employee fraud, it is crucial to acknowledge the potential for other scams that exploit new technologies and the prevalence of remote work. Malicious cyber attackers could secure employment using stolen credentials, enabling them to gain unauthorized access to sensitive data or company systems. A proficient hacker possessing the necessary IT skills may find it relatively easy to leverage social engineering techniques during the hiring process. Consequently, the reliability of traditional methods for employee verification, such as face-to-face interactions and personal recognition, is diminishing in the face of remote work and the technological advancements that enable individuals to manipulate their appearance, voice, and identity. To mitigate risks associated with hiring imposters, it is imperative to incorporate robust measures into the recruitment process. Here are some key considerations:  Establish clear policies and employment contracts: Clearly communicate your organization's policies regarding moonlighting in employment contracts, employee handbooks, or other official documents.   Confidentiality and non-compete agreements: Implement confidentiality and non-compete agreements to protect your company's sensitive information and intellectual property.   Monitoring: Automate employment and income verification of your employees.  Provide training on cybersecurity best practices: Educate employees about cyber-attacks and identity scams, such as phishing scams, through seminars and workplace training sessions.  Implement robust security measures: Use firewalls, encrypt sensitive employee information, and limit access to personal data. Minimize the number of employees who have access to this information.  Thoroughly screen new employees: Verify the accuracy of Social Security numbers and other information during the hiring process. Conduct comprehensive background checks, including checking bank account information and credit reports and fight against synthetic identities.  Offer identity theft protection as a benefit: Consider providing identity theft protection services to your employees as part of their benefits package. These services can detect and alert victims of potential identity theft, facilitating a fast response.  The new era of remote work necessitates a fresh perspective on the hiring process. It is crucial to reevaluate HR practices and leverage AI fraud detection technologies to ensure that the individuals you hire, and employ are who they claim to be, guarding against the infiltration of imposters.  Navigating employment fraud with effective solutions  Employment fraud presents significant risks and challenges for employers, including conflicts of interest, reputation damage, and breaches of confidentiality. By taking the right preventative measures, you can safeguard your organization and employees.  Streamlining the hiring process is essential to remain competitive. But how do you balance the need for speed and ease of use with essential ID checks?  By combining the best data with our automated ID verification processes, Experian helps you protect your business and onboard new talents efficiently. Our best-in-class solutions employ device recognition, behavioral biometrics, machine learning and global fraud databases to spot and block suspicious activity before it becomes a problem.  Learn more about preventing employement fraud *This article includes content created by an AI language model and is intended to provide general information.

Model explainability has become a hot topic as lenders look for ways to use artificial intelligence (AI) to improve their decision-making. Within credit decisioning, machine learning (ML) models can often outperform traditional models at predicting credit risk.  ML models can also be helpful throughout the customer lifecycle, from marketing and fraud detection to collections optimization. However, without explainability, using ML models may result in unethical and illegal business practices.  What is model explainability?  Broadly defined, model explainability is the ability to understand and explain a model's outputs at either a high level (global explainability) or for a specific output (local explainability).1  Local vs global explanation: Global explanations attempt to explain the main factors that determine a model's outputs, such as what causes a credit score to rise or fall. Local explanations attempt to explain specific outputs, such as what leads to a consumer's credit score being 688. But it's not an either-or decision — you may need to explain both.  Model explainability can also have varying definitions depending on who asks you to explain a model and how detailed of a definition they require. For example, a model developer may require a different explanation than a regulator.  Model explainability vs interpretability  Some people use model explainability and interpretability interchangeably. But when the two terms are distinguished, model interpretability may refer to how easily a person can understand and explain a model's decisions.2 We might call a model interpretable if a person can clearly understand:  The features or inputs that the model uses to make a decision.  The relative importance of the features in determining the outputs.  What conditions can lead to specific outputs.  Both explainability and interpretability are important, especially for credit risk models used in credit underwriting. However, we will use model explainability as an overarching term that encompasses an explanation of a model's outputs and interpretability of its internal workings below.  ML models highlight the need for explainability in finance  Lenders have used credit risk models for decades. Many of these models have a clear set of rules and limited inputs, and they might be described as self-explanatory. These include traditional linear and logistic regression models, scorecards and small decision trees.3  AI analytics solutions, such as ML-powered credit models, have been shown to better predict credit risk. And most financial institutions are increasing their budgets for advanced analytics solutions and see their implementation as a top priority.4  However, ML models can be more complex than traditional models and they introduce the potential of a “black box." In short, even if someone knows what goes into and comes out of the model, it's difficult to explain what's happening without an in-depth analysis.  Lenders now have to navigate a necessary trade-off. ML-powered models may be more predictive, but regulatory requirements and fair lending goals require lenders to use explainable models.  READ MORE: Explainability: ML and AI in credit decisioning  Why is model explainability required?  Model explainability is necessary for several reasons:  To comply with regulatory requirements: Decisions made using ML models need to comply with lending and credit-related, including the Fair Credit Reporting Act (FCRA) and Equal Credit Opportunity Act (ECOA). Lenders may also need to ensure their ML-driven models comply with newer AI-focused regulations, such as the AI Bill of Rights in the U.S. and the E.U. AI Act.  To improve long-term credit risk management: Model developers and risk managers may want to understand why decisions are being made to audit, manage and recalibrate models.  To avoid bias: Model explainability is important for ensuring that lenders aren't discriminating against groups of consumers.  To build trust: Lenders also want to be able to explain to consumers why a decision was made, which is only possible if they understand how the model comes to its conclusions.  There's a real potential for growth if you can create and deploy explainable ML models. In addition to offering a more predictive output, ML models can incorporate alternative credit data* (also known as expanded FCRA-regulated data) and score more consumers than traditional risk models. As a result, the explainable ML models could increase financial inclusion and allow you to expand your lending universe.  READ MORE: Raising the AI Bar  How can you implement ML model explainability?  Navigating the trade-off and worries about explainability can keep financial institutions from deploying ML models. As of early 2023, only 14 percent of banks and 19 percent of credit unions have deployed ML models. Over a third (35 percent) list explainability of machine learning models as one of the main barriers to adopting ML.5  Although a cautious approach is understandable and advisable, there are various ways to tackle the explainability problem. One major differentiator is whether you build explainability into the model or try to explain it post hoc—after it's trained.  Using post hoc explainability  Complex ML models are, by their nature, not self-explanatory. However, several post hoc explainability techniques are model agnostic (they don't depend on the model being analyzed) and they don't require model developers to add specific constraints during training.  Shapley Additive Explanations (SHAP) is one used approach. It can help you understand the average marginal contribution features to an output. For instance, how much each feature (input) affected the resulting credit score.  The analysis can be time-consuming and expensive, but it works with black box models even if you only know the inputs and outputs. You can also use the Shapley values for local explanations, and then extrapolate the results for a global explanation.  Other post hoc approaches also might help shine a light into a black box model, including partial dependence plots and local interpretable model-agnostic explanations (LIME).  READ MORE: Getting AI-driven decisioning right in financial services  Build explainability into model development  Post hoc explainability techniques have limitations and might not be sufficient to address some regulators' explainability and transparency concerns.6 Alternatively, you can try to build explainability into your models. Although you might give up some predictive power, the approach can be a safer option.  For instance, you can identify features that could potentially lead to biased outcomes and limit their influence on the model. You can also compare the explainability of various ML-based models to see which may be more or less inherently explainable. For example, gradient boosting machines (GBMs) may be preferable to neural networks for this reason.7  You can also use ML to blend traditional and alternative credit data, which may provide a significant lift — around 60 to 70 percent compared to traditional scorecards — while maintaining explainability.8  READ MORE: Journey of an ML Model  How Experian can help  As a leader in machine learning and analytics, Experian partners with financial institutions to create, test, validate, deploy and monitor ML-driven models. Learn how you can build explainable ML-powered models using credit bureau, alternative credit, third-party and proprietary data. And monitor all your ML models with a web-based platform that helps you track performance, improve drift and prepare for compliance and audit requests. *When we refer to “Alternative Credit Data," this refers to the use of alternative data and its appropriate use in consumer credit lending decisions, as regulated by the Fair Credit Reporting Act. Hence, the term “Expanded FCRA Data" may also apply and can be used interchangeably.  1-3. FinRegLab (2021). The Use of Machine Learning for Credit Underwriting  4. Experian (2022). Explainability: ML and AI in credit decisioning  5. Experian (2023). Finding the Lending Diamonds in the Rough  6. FinRegLab (2021). The Use of Machine Learning for Credit Underwriting  7. Experian (2022). Explainability: ML and AI in credit decisioning  8. Experian (2023). Raising the AI Bar 

Meeting Know Your Customer (KYC) regulations and staying compliant is paramount to running your business with ensured confidence in who your customers are, the level of risk they pose, and maintained customer trust. What is KYC?KYC is the mandatory process to identify and verify the identity of clients of financial institutions, as required by the Financial Conduct Authority (FCA). KYC services go beyond simply standing up a customer identification program (CIP), though that is a key component. It involves fraud risk assessments in new and existing customer accounts. Financial institutions are required to incorporate risk-based procedures to monitor customer transactions and detect potential financial crimes or fraud risk. KYC policies help determine when suspicious activity reports (SAR) must be filed with the Department of Treasury’s FinCEN organization. According to the Federal Financial Institutions Examinations Council (FFIEC), a comprehensive KYC program should include:• Customer Identification Program (CIP): Identifies processes for verifying identities and establishing a reasonable belief that the identity is valid.• Customer due diligence: Verifying customer identities and assessing the associated risk of doing business.• Enhanced customer due diligence: Significant and comprehensive review of high-risk or high transactions and implementation of a suspicious activity-monitoring system to reduce risk to the institution. The following organizations have KYC oversight: Federal Financial Institutions Examinations Council (FFIEC), Federal Reserve Board, Federal Deposit Insurance Corporation (FDIC), national Credit Union Administration (NCUA), Office of the Comptroller of the Currency (OCC) and the Consumer Financial Protection Bureau (CFPB). How to get started on building your Know Your Customer checklist 1. Define your Customer Identification Program (CIP) The CIP outlines the process for gathering necessary information about your customers. To start building your KYC checklist, you need to define your CIP procedure. This may include the documentation you require from customers, the sources of information you may use for verification and the procedures for customer due diligence. Your CIP procedure should align with your organization’s risk appetite and be comply with regulations such as the Patriot Act or Anti-money laundering laws. 2. Identify the customer's information Identifying the information you need to gather on your customer is key in building an effective KYC checklist. Typically, this can include their first and last name, date of birth, address, phone number, email address, Social Security Number or any government-issued identification number. When gathering sensitive information, ensure that you have privacy and security controls such as encryption, and that customer data is not shared with unauthorized personnel. 3. Determine the verification method There are various methods to verify a customer's identity. Some common identity verification methods include document verification, facial recognition, voice recognition, knowledge-based authentication, biometrics or database checks. When selecting an identity verification method, consider the accuracy, speed, cost and reliability. Choose a provider that is highly secure and offers compliance with current regulations. 4. Review your checklist regularly Your KYC checklist is not a one and done process. Instead, it’s an ongoing process that requires periodic review, updates and testing. You need to periodically review your checklist to ensure your processes are up to date with the latest regulations and your business needs. Reviewing your checklist will help your business to identify gaps or outdated practices in your KYC process. Make changes as needed and keep management informed of any changes. 5. Final stage: quality control As a final step, you should perform a quality control assessment of the processes you’ve incorporated to ensure they’ve been carried out effectively. This includes checking if all necessary customer information has been collected, whether the right identity verification method was implemented, if your checklist matches your CIP and whether the results were recorded correctly. KYC is a vital process for your organization in today's digital age. Building an effective KYC checklist is essential to ensure compliance with regulations and mitigate risk factors associated with fraudulent activities. Building a solid checklist requires a clear understanding of your business needs, a comprehensive definition of your CIP, selection of the right verification method, and periodic reviews to ensure that the process is up to date. Remember, your customers' trust and privacy are at stake, so iensuring that your security processes and your KYC checklist are in place is essential. By following these guidelines, you can create a well-designed KYC checklist that reduces risk and satisfies your regulatory needs. Taking the next step Experian offers identity verification solutions as well as fully integrated, digital identity and fraud platforms. Experian’s CrossCore & Precise ID offering enables financial institutions to connect, access and orchestrate decisions that leverage multiple data sources and services. By combining risk-based authentication, identity proofing and fraud detection into a single, cloud-based platform with flexible orchestration and advanced analytics, Precise ID provides flexibility and solves for some of financial institutions’ biggest business challenges, including identity and fraud as it relates to digital onboarding and account take over; transaction monitoring and KYC/AML compliance and more, without adding undue friction. Learn more *This article includes content created by an AI language model and is intended to provide general information.

The online gaming industry has experienced tremendous growth in recent years, with millions of players engaging in immersive virtual worlds and competitive gameplay. Unfortunately, this surge in popularity has also sparked an increase in online gaming fraud. Unscrupulous individuals have sought to exploit the industry through fraudulent activities, leading to financial losses and reputational damage for gaming vendors.According to a recent study conducted by Lloyds Bank, children are spending more time playing online games than ever before – over five million children between the ages of three and 15 are now regularly playing games online, up from approximately 4.6 million in 2019.Fraudsters, always ready to take advantage of opportunities presented by new trends, are now increasingly targeting this rising demographic. Gaming vendors have a responsibility to shield minors from fraud in online gaming by implementing robust safety measures, educating young players and their parents, and actively monitoring and addressing fraudulent activities.   A vulnerable target That same study from Lloyds revealed that over a third (36%) of parents are concerned about the possibility of their children falling victim to gaming fraud and losing money. In today's tech-savvy world, the ease of payment authorization has only exacerbated these concerns. All it takes for a child to make a payment is to key in their parents' online store username and password. It is a practice fraught with danger. Parents can only do so much to safeguard their children while gaming, and despite their best efforts, there will always remain a lingering possibility of encountering scammers. Gaming vendors should establish robust age verification processes during account creation to ensure that minors are not exposed to age-inappropriate content. Additionally, they should incorporate comprehensive parental controls that allow parents to regulate their children's online activities, including chat limitations, spending controls, and access to certain features.But contrary to common assumptions, the gaming population is not restricted to teenagers or young adults. With an average age of 35, gamers have significant purchasing power and actively participate in the gaming ecosystem. They spend an average of over six hours per week gaming, dedicating nearly an hour each day to their preferred gaming experiences. This engagement is spread across all age groups and financial profiles, making the gaming community a vast market to attract cybercrime. Types of fraud in online gaming In 2022, the revenue from the worldwide gaming market was estimated at almost 347 billion U.S. dollars, with the mobile gaming market generating an estimated 248 billion U.S. dollars of the total. The gaming market is constantly evolving, and technological advancements are opening new possibilities for game developers to create more immersive and engaging experiences.But alarming reports indicate that scammers have honed in on the younger demographic of gamers, leveraging their innocence to exploit their finances and identities. Identity theft (67%) and hacking (61%) rank as the two most prevalent forms of fraud experienced by young gamers, according to the Lloyds Bank study. Here are some different types of online gaming fraud: Account hacking: Hackers employ various techniques like phishing, keylogging, and credential stuffing to gain unauthorized access to players' accounts. Once compromised, accounts could be used for fraudulent activities, including unauthorized in-game transactions or selling virtual assets for real money. Chargeback fraud: This occurs when players make legitimate purchases within a game using real money and then issue chargebacks, falsely claiming that the transaction was unauthorized or fraudulent. This results in financial losses for gaming vendors as they lose the revenue and virtual goods/services provided to the player. Virtual asset fraud: Virtual assets, such as in-game currency, items, or characters, hold economic value. Fraudsters engage in scams involving fake virtual asset transactions or market manipulation, exploiting players' desires to acquire rare or high-value items. Match-fixing and cheating: Competitive gaming is at the heart of many online games. Fraudsters seek to manipulate matches, exploit glitches, or use cheat software to gain an unfair advantage over others. This undermines the integrity of the gaming experience and discourages fair competition. The game changer for online platforms: fraud prevention strategies  Given the anticipated growth of these threats in the foreseeable future, it is imperative that online platforms prioritize the protection of young gamers and their parents. In line with the enhanced safeguards and anti-fraud initiatives observed in banks and financial institutions, it is high time for game companies to elevate their security and consumer protection measures by adopting the following guidelines: Implement strong account security measures: Encourage players to create unique, complex passwords, and consider implementing multifactor authentication solutions. Regularly educate players about common hacking techniques and promote safe browsing habits to prevent phishing attempts. Utilize fraud detection systems: Invest in advanced fraud detection tools that employ machine learning algorithms and biometrics templates to identify suspicious activities and patterns. These systems can flag potentially fraudulent transactions, allowing you to take appropriate measures promptly. Monitor and analyze user behavior: Keep an eye on players' activities and digital identity, such as unusual login patterns, high-value transactions, or frequent chargebacks. Analyze gameplay data, interactions, and purchasing behavior to identify patterns indicative of fraud or cheating. Secure payment processing systems: Choose reputable payment gateways that prioritize security measures. Employ tokenization and encryption technologies to safeguard players' payment information during transactions. Regularly test and update your payment system's security infrastructure. Raise player awareness: Educate your player community about common fraud techniques and the importance of securing their accounts with identity authentication. Share security tips through newsletters, blog posts, and in-game messaging. Foster a culture of vigilance and encourage players to report any suspicious activities. Foster fair gameplay and zero tolerance policy: Implement robust anti-cheat measures and regularly update your game to address vulnerabilities and exploits. Promote fair competition and enforce a zero-tolerance policy against cheating, match-fixing, and other forms of unfair gameplay. Leveling-up Ultimately, the ability to protect players online could be the ultimate gamechanger for gaming platforms. By embracing identity verification mechanisms that rely on secure and privacy-centric facial recognition, online fraud and identity theft can be significantly curtailed. Moreover, the verification and onboarding processes can be streamlined, simplifying the user experience further. Just as bringing top-tier games on board is crucial, game platforms must ensure their customers engage in a secure gaming environment. Streamlining the onboarding and sign-in process is essential to remain competitive. But how do you balance the need for speed and ease of use with essential ID checks? By combining the best data with our automated ID verification checks, Experian helps you safeguard your business and onboard customers efficiently. Using passive, invisible checks when customers sign into their accounts helps to keep fraudsters at bay and protects legitimate players without the need for irritating security challenges. Experian’s best-in-class solutions employ device recognition, behavioral biometrics, machine learning and global fraud databases to spot and block suspicious activity before it becomes a problem. Learn more *This article leverages/includes content created by an AI language model and is intended to provide general information.

Fraud is a serious concern for everyone, including businesses and individuals. In fact, according to our 2023 U.S. Identity and Fraud Report, nearly two-thirds (64%) of consumers are very or somewhat concerned with online security, and over 50% of businesses have a high level of concern about fraud risk. The fraud landscape is constantly evolving, and staying vigilant against the latest trends is critical to safeguarding your organization and consumers. As we reflect on 2023, let’s look at the top fraud trends and their continued potential impact on your business. The evolution of new fraud trends When economic uncertainty reigns, a rise in fraud often follows. To begin with, consumers tend to be financially stressed in such periods and prone to making risky decisions. In addition, fraudsters are keenly aware of the opportunities inherent in unstable times and develop tactics to take advantage of them. For example, as consumers rein in spending and financial institutions struggle to maintain new account volumes, fraudsters might ramp up their new account and loan activities. Fraud is becoming more sophisticated. For instance, thanks to the rapid rise in the availability of artificial intelligence (AI) tools, fraudsters are increasingly able to impersonate companies and individuals with ease, as well as consolidate data from diverse sources and use it more efficiently. The most impactful fraud trends of 2023 The fraud trends that emerged in 2023 were diverse, though they all had one thing in common: fraudsters' keen ability to take advantage of new technologies and opportunities. And businesses are feeling the repercussions, with nearly 70% reporting that fraud losses have increased in recent years. Here are five trends we forecasted in the fraud and identity space that challenged fraud fighters on the front lines this year. Deposit and checking account fraud With everyone focused on fraud in the on-line channels, it is interesting that financial institutions reported more fraud occurring at brick-and-mortar locations. Preying on the good nature of helpful branch employees, criminals are taking risks by showing up in person to open accounts, pass bad deposits and try to work their way into other financial products.  The Treasury Department reports complaints doubling YoY, after increasing more than 150% between 2020 and 2021.  Synthetic identity fraud Not quite fake, not quite real, so-called synthetic or "Frankenstein" identities mash up real data with false information to create unique customer profiles that can outsmart retailers' or financial institutions' fraud control systems. With synthetic identity (SID) fraud real data is often stolen or purchased on the dark web and combined with other information — even Artificial Intelligence (AI)-created faces — so that fraudsters can build up a synthetic identity's credit score before taking advantage of them to borrow and spend money that will never be paid back. One major risk? As fraud rates rise due to the use of tactics like synthetic identities, it could become more challenging and expensive to access credit. Fake job postings and mule schemes Well-paying remote work was in high demand this year, creating opportunities for fraudsters to create fake jobs to harvest data such as Social Security numbers from unsuspecting applicants. Experian also predicts a continued rise in "mule" jobs, in which workers unknowingly sign on to do illegal work, such as re-shipping stolen goods. According to the Better Business Bureau, an estimated 14 million people get caught in a fake employment scam yearly. Job seekers can protect themselves by being skeptical of jobs that ask them to do work that appears suspicious, requires money, financial details, or personal information upfront. Peer-to-peer payment fraud Peer-to-peer payment tools are increasingly popular with consumers and fraudsters, who appreciate that they're both instant and irreversible. Experian expects to continue to see an increase in fraudulent activity on these payment systems, as fraudsters use social engineering techniques to deceive consumers into paying for nonexistent merchandise or even sharing access credentials. Stay safe while using peer-to-peer payment tools by avoiding common scams like requests to return accidental payments, opting for payment protection whenever possible and choosing other transaction methods like paying with a credit card. Social media shopping fraud Social media platforms are eager to make in-app shopping fun and friction-free for consumers — and many brands and shoppers are keen to get on board. In fact, approximately 58% of users in the U.S. have purchased a product after seeing it on social media. Unfortunately, these tools neglect effective identity resolution and fraud prevention, leaving sellers vulnerable to fraudulent purchases. And while buyers have some recourse when a purchase turns out to be a scam, it's wise to be cautious while shopping on social media platforms by researching sellers, only using credit cards and being cognizant of common scams, like when vendors on Facebook Marketplace ask for payment upfront. Employer text fraud Fraudulent text messages — also known as “smishing,” a mash-up of Short Messaging Service (SMS) and phishing — continues to rise. In fact, according to data security company Lookout, 2022 was the biggest year ever for such mobile phishing attacks, with more than 30 percent of personal and enterprise mobile phone users exposed every quarter. One modern example of these types of schemes? Expect to continue to see a rise in gift card fraud targeting companies. For example, an employee might receive a text from their "boss" asking them to purchase gift cards and relay the numbers. The fraudsters get to shop, and the company is left with the bill. Why fraud prevention and detection solutions matter Nearly two-thirds of consumers say they are "very" or "somewhat concerned" with online security, and more than 85 percent expect businesses to respond to their identity and fraud concerns. Addressing and preventing fraud — and communicating these fraud-prevention actions to customers — is an essential strategy for businesses that want to maintain customer trust, thereby decreasing churn and maximizing conversions on new leads. There's a financial imperative to address fraud as well. Businesses stand to lose a great deal of money without adequate fraud prevention strategies. Account takeover fraud, for example, is an increasing threat to financial institutions, which saw a 90 percent increase in account takeover losses from 2020 to 2021. By making account takeover fraud prevention a priority, financial institutions can alleviate risks and prevent major losses. How to build an effective fraud strategy in 2024 In 2024, fraud management solutions must be even more technically advanced than the fraudulent techniques they're combating. But more than that, they need to be appealing to consumers, who are likely to abandon signup or purchase attempts when they become too onerous. In fact, 37% of consumers have moved their business elsewhere due to a negative account opening experience. Worryingly for businesses, this number was even higher among high-income households and those aged 25 to 39. To succeed, effective fraud strategies must be seamless, low friction, data-driven and customer-focused. That means making use of up-to-date technologies that boost security while prioritizing a positive customer experience.  Concerned about fraud? Let Experian help As we look back at the top fraud trends of 2023, it's clear that scammers are becoming increasingly sophisticated in their methods. Fraud can create huge risks for your business — but there are ways to act. Experian's suite of fraud prevention and identity verification tools can help you detect and combat fraud. Find out more about Experian's fraud risk management strategies and how they can help keep you and your customers safe. Learn more

Financial institutions are under increasing pressure to grow deposits and onboard more demand deposit accounts (DDA). But as demand increases, so do fraud attempts from scammers. While a robust mitigation effort is needed to stop fraud, this same effort can also drive away potential clients. In fact, 37 percent of U.S. adults said that they abandoned opening an account online due to experiencing friction. This leaves institutions in a unique quandary: how do they stop DDA fraud without scaring away potential clients? The answer lies in utilizing robust, machine learning tools that can help you navigate fraud attempts without increasing onboarding friction.  Chris Ryan, Go to Market Lead for Experian Identity and Fraud, shares his thoughts on demand deposit account fraud and which decisioning tools can best combat it.   Q: What is a demand deposit account and how is it used? "Demand deposit is just your basic checking account," Ryan explains." The funds are deposited and held by an institution, which enables you to spend those assets or resources, whether it be through checks, debit cards, person-to-person, Automated Clearing House (ACH) — all the things we do every day as consumers to manage our operating budget."  Q: What is demand deposit account fraud?   "There are two different ways that demand deposit account fraud works," Ryan says. "One is with existing account holders, and the other is with the account opening process.” When fraud affects existing account holders, it typically involves tricking an account holder into sending money to a scammer or using fraudulent actions, like phishing emails or credit card skimmers, to gain access to their accounts. There is also a resurgence in fraud involving duplication, theft and forgery of paper checks, Ryan explains.   Fraud impacting the account opening process occurs when scammers originate new DDAs. This can work in a variety of ways, such as these three examples:  A scammer steals your identity and opens an account at the same bank where you have a home equity loan. They link their DDA to your line of credit, transferring your money into their new account and withdrawing the funds.  A scammer uses a synthetic identity (SID) to open a fraudulent DDA. They will then use this new DDA to open more lucrative accounts that the institution cross-sells to them. A scammer uses a stolen or SID to open “mule” accounts to receive funds they dupe consumers into sending through fake relationship schemes, bogus merchandise sales and dozens of similar scams. While both types of fraud need to be dealt with, account opening fraud can have especially large repercussions for lenders or financial institutions.  Q: What are the consequences of DDA fraud for organizations?   "Fraud hurts in a number of ways," Ryan explains. "There are direct losses, which is the money that criminals take from our financial system. Under most circumstances, the financial institution replaces the money, so the consumer doesn’t absorb the loss, but the money is still gone. That takes money away from lending, community engagement and other investments we want banks to make. The direct losses are what most people focus on."  But there are even more repercussions for institutions beyond losing money, and this can include the attempts that institutions put into place to stop the fraud. "Preventing fraud requires some friction for the end consumer," Ryan says. "The volume of fraudulent attempts is overwhelmingly large in the DDA space. This forces institutions to apply more friction. The friction is costly, and it often drives would-be-customers away. The results include high costs for the institutions and low booking rates. At the same time, institutions are hungry for deposit money right now. So, it's kind of a perfect storm."  Q: What is the impact of DDA fraud on customer experience?  Experian’s 2023 Identity and Fraud Report revealed that up to 37 percent of U.S. adults in the survey had abandoned a new account entirely in the previous six months because of the friction they encountered during onboarding. And 51 percent reported considering abandoning the process because of problems they encountered. Unfortunately, fraud mitigation and deposit fraud detection efforts can end up driving customers away. "People can be impatient," Ryan says, "and in the online world, a competing product is a mouse-click away. So, while it is tempting to ask new applicants for more information, or further proof of identity, that conflicts with their need for convenience and can impact their experience.” Companies looking for cheap and fast mitigation can end up impeding customers trying to onboard to sweep out the bad actors, Ryan explains. "How do you get the bad people without interrupting the good people?" Ryan asks. "That's the million-dollar question."  Q: What are some other problems with how organizations traditionally combat DDA fraud?   Unfortunately, traditional attempts to combat DDA fraud are inefficient due to the fragmentation of technology. Ryan says this was revealed by Liminal, an industry analyst think tank.  "Nearly half of institutions use four-or-more-point solutions to manage identity and fraud-related risk," Ryan explains. "But all of those point solutions were meant to work on their own. They weren't developed to work together. So, there's a lot of overlap. And in the case of fraud, there's a high likelihood that the multiple solutions are going to find the same fraud. So, you create a huge inefficiency."   To solve this challenge, institutions need to shift to integrated identity platforms, such as Experian CrossCore®.  Q: How is Experian trying to change the way organizations approach DDA fraud?   Experian is pushing a paradigm shift for institutions that will increase fraud detection efficiency and accuracy, without sacrificing customer experience. "Organizations need to start thinking of identity through a different lens," Ryan says.   Experian has developed an identity graph that aggregates consumer information in a manner that reaches far beyond what an institution can create on its own. "Experian is able to bring the entire breadth of every identity presentation we see into an identity graph," Ryan says. "It's a cross-industry view of identity behavior." This is important because people who commit fraud manipulate data, and those manipulations can get lost in a busy marketplace.   For example, Ryan explains, if you're newly married, you may have recently presented your identity using two different surnames: one under your maiden name and one under your married name. Traditional data sources may show that your identity was presented twice, but they won’t accurately reflect the underlying details; like the fact that different surnames were used. The same holds true for thousands of other details seen at each presentation but not captured in a way that enables changes over time to be visible, such as information related to IP addresses, email accounts, online devices, or phone numbers.   "Our identity graph is unlocking the details behind those identity presentations," Ryan says. "This way, when a customer comes to us with a DDA application, we can say, 'That's Chris's identity, and he's consistently presenting the same information, and all that underlying data remains very stable.'"   This identity graph, part of Experian's suite of fraud management solutions — also connects unique identity details to known instances of fraud, helping catch fraudulent attempts much faster than traditional methods. "Let's say you and your spouse share an address, phone numbers, all the identity details that married couples typically share," Ryan explains. "If an identity thief steals your identity and uses it along with a brand-new email and IP address not associated with your spouse, that might be concerning. However, perhaps you started a new job, and the email/IP data is legitimate. Or maybe it’s a personal email using a risky internet service provider that shares a format commonly used by a known ring of identity thieves. Traditional data might flag the email and IP information as new, but our identity graph would go several layers deeper to confirm the possible risks that the new information brings.  Q: Why is this approach superior to traditional methods of fraud detection?  "Historically, organizations were interested in whether an identity was real,” Ryan says. "The next question was if the provided data (I.e., addresses, date of birth, Social Security numbers, etc.) have been historically associated with the identity. Last, the question would be whether there’s known risk associated with any of the identity components.” The identity graph turns that approach upside down.   "The identity graph allows us to pull in insights from past identity presentations, " Ryan says. "Maybe the current presentation doesn’t include a phone number. Our identity graph should still recognize previously provided phone numbers and the risks associated with them. Instead of looking at identity as a small handful of pieces of data that were given at the time of the presentation, we use the data given to us to get to the identity graph and see the whole picture."  Q: How are businesses applying this new paradigm?  The identity graph is part of Experian's Ascend Fraud Platform™ and a full suite of fraud management solutions. Experian's approach allows companies to clean out fraud that already occurred and stop new fraudulent actors before they're onboarded. "Ideally, you want to start with cleaning up the house, and then figure out how to protect the front door," Ryan says.  In other words, institutions can start by applying this view to recently opened accounts to identify problematic identities that they missed. The next step would be to bring these insights into the new account onboarding process.  Q: Is this new fraud platform accessible to both small and large businesses?  The Ascend Fraud Platform will support several use cases that will bring value to a broad range of businesses, Ryan explains. It can not only enable Experian experts to build and deliver better tools but can enable self-serve analytical development too. "Larger organizations that have robust, internal data science capabilities will find that it’s an ideal environment for them to work in," Ryan says. "They can add their own internal data assets to ours, and then have a better place to develop analytics. Today, organizations spend months assembling data to develop analytics internally. Our Ascend Fraud Platform will reduce the timeline of the data assembly and analytical development process to weeks, and speed to market is critical when confronting continually changing fraud threats. "But for customers who have less robust analytical teams, we're able to do that on their behalf and bring solutions out to the marketplace for them," Ryan explains.   Q: What type of return on investment (ROI) are businesses experiencing?  "Some customers recover their investment in days," Ryan says. "Part of this is from mitigating fraud risks among recently opened accounts that slipped through existing defenses.”     "In addition to reducing losses, institutions we're working with are also seeing potentially millions of dollars a month in additional bookings, as well as significant cost savings in their account opening processes," Ryan says.  "We're able to help clients go back and audit the people who had fallen out of their process, to figure out how to fine-tune their tools to keep those people in," Ryan says.   “By reducing risks among existing accounts, better protecting the front door against future fraud, and growing more efficiently, we’re helping clients  Q: What are Experian's plans for this service?   "We're working with top-tier financial institutions on the do-it-yourself techniques," Ryan says. "In parallel, we're launching our first offerings that are created for the broader marketplace. That will start with the portfolio review capability, along with making the most predictive attributes available through our integrated identity resolution platform. And while the Ascend Fraud Platform has a strong use case for DDA fraud, its uses extend beyond that to small business lending and other products. In fact, Experian offers an entire suite of fraud management solutions to help keep your DDA accounts secure and your customers happy.   Experian can help optimize your DDA fraud detection  Experian is revolutionizing the approach to combating DDA fraud, helping institutions create a faster onboarding process that retains more customers, while also stopping more bad actors from gaining access. It's a win-win for everyone.   Experian's full suite of fraud management solutions can optimize your business's DDA fraud detection, from scrubbing your current portfolio to gatekeeping bad actors before they're onboarded.  Learn more Speak with a specialist About our expert: Chris Ryan has over 20 years of experience in fraud prevention and uses this knowledge to identify the most critical fraud issues facing individuals and businesses in North America, and he guides Experian’s application of technology to mitigate fraud risk.

In today's fast-paced digital world, the risk of fraud across all industries is a constant threat. The traditional methods of fraud detection are no longer sufficient, as fraudsters become increasingly sophisticated in their attacks. However, with artificial intelligence (AI) and machine learning (ML) solutions, financial institutions can stay one step ahead of fraudsters. AI and machine learning-equipped fraud detection tools have the ability to identify suspicious activity and patterns of fraud that are imperceptible to the human brain. In this blog post, we’ll dive into the significance of AI and machine learning in fraud detection and how these solutions are uniquely equipped to handle the demands of modern-day risk management. Understanding artificial intelligence and machine learning AI and machine learning solutions are transformative technologies that are reshaping the landscape of many industries. AI, at its core, is a field of computer science that simulates human intelligence in machines, enabling them to learn from experience and perform tasks that normally require human intellect. Machine learning, a subset of AI, is the science of getting computers to learn and act like humans do, but with minimal human intervention. They can analyze vast amounts of data within seconds, identifying patterns and trends that would be impossible for a human to recognize. When it comes to fraud detection, this ability is invaluable.  Advantages of fraud detection using machine learning AI and machine learning have several benefits that make them valuable in fraud detection. One significant advantage is that these technologies can recognize patterns that are too complex for humans to identify. By running through a vast set of data points, these solutions can pinpoint anomalous behavior, and thereby prevent financial losses. AI analytics tools are adept at monitoring complex networks, detecting the dispersion of attacks that may involve multiple individuals and entities, and correlating activity patterns that would otherwise be hidden. Machine learning algorithms can take these patterns and turn them into mathematical models that help identify instances of fraud before the damage takes place. Secondly, they continuously learn from new data, which allows them to become more efficient in identifying fraud as they process more data. Thirdly, they automate fraud mitigation processes, which significantly reduces the need for manual interventions that may consume valuable time and resources. Another significant benefit of machine learning is its analytics capabilities, which allow organizations to gain valuable insights into customer behavior and fraud patterns. With AI analytics, they can detect and investigate fraudulent activities in real-time, and combine it with other tools to help detect and mitigate fraud risk. For example, in financial services, AI fraud detection can help banks and financial service providers detect and prevent fraud in their systems, add value to their services and improve customer satisfaction. The future of fraud detection and machine learning The rate at which technology is evolving means that machine learning and AI fraud detection will become increasingly important in the future. In the next few years, we can expect a more sophisticated level of fraud detection using unmanned machine systems, robotics process automation, and more. Ultimately, this will improve the efficiency and effectiveness of fraud detection. AI-based fraud management solutions are taking center stage. Organizations must leverage advanced machine learning and AI analytics solutions to prevent and mitigate cyber risks and comply with regulatory mandates. The benefits extend far beyond the financial bottom line to improving the safety and security of customers. AI and machine learning solutions offer accurate, efficient and proactive routes to managing the risk of fraud in an ever-changing environment. How can Experian® help Integrating machine learning for fraud detection represents a significant advancement in cybersecurity. Fraud management solutions detect, prevent and manage fraud across all industries, including financial services, healthcare and telecommunications. With the advancement of technology, fraud management solutions now integrate machine learning to improve their processes. Experian® provides fraud prevention solutions, including machine learning models and AI analytics, which can help more effectively mitigate fraud risk, streamline fraud investigations and create a more secure digital environment for all. With Experian’s AI analytics, risk mitigation tools and fraud management solutions, organizations can stay one step ahead of fraudsters and protect their brand reputation, customer trustworthiness and corporate data. Embracing these solutions can save organizations from significant losses, reputational damage and regulatory scrutiny. To learn more about how to future-proof your business and safeguard your customers from fraud, check out Experian’s robust suite of fraud prevention solutions. Want to hear what our industry experts think? Check out this on-demand webinar on artificial intelligence and machine learning strategies. Learn more Watch webinar *This article includes content created by an AI language model and is intended to provide general information.

Sometimes logging into an account feels a bit like playing 20 questions. Security is vital for a positive customer experience, and engaging the right identity verification strategies is essential to proactive fraud prevention. For financial institutions and businesses, secure authentication is more important than ever. It is imperative for customer safety – which drives retention and loyalty – and your bottom line – as fraud has determinantal effects on and off the balance sheet. Information sharing has proliferated, as has the number of times consumers are prompted to provide access to sensitive information. While today’s consumer has grown accustomed to providing such information, there’s also a heightened demand for security. According to Experian’s 2023 U.S. Identity and Fraud Report, nearly two-thirds (64%) of consumers say they’re very or somewhat concerned with online safety, listing identity theft, stolen card information and online privacy as top concerns. Customers want to know who they are providing access to and whether that entity will have their safety in mind. From a business perspective, one way to ensure that only the right people can get in is by using (KBA). KBA takes traditional authentication methods, like passwords and Personal Identification Numbers (PINs), one step further by creating an additional layer of security through collecting private facts from each user. In this post, we'll look at how KBA works, what its benefits are as a form of identity verification, and how it can improve customer trust. Introducing Knowledge Based Authentication (KBA): What it is and how it works Knowledge Based Authentication can be part of a multifactor authentication solution and is one way to stay on top of privacy and security for your customers – existing and new. KBA is a feature designed to protect online accounts by verifying the account holder’s identity. It involves answering a series of personal questions, such as mother's maiden name or first pet's name, that only the account holder should know. This system has become increasingly popular due to its effectiveness in preventing fraud and identity theft. With KBA, businesses and individuals can have peace of mind that their information is protected by a reliable authentication system that is difficult for unauthorized users to breach. Benefits of implementing KBA and a multifactor authentication strategy By implementing KBA into your business, customers experience an additional layer of security by verifying the identity of users through personalized questions. This reduces the risk of fraud and enhances customer trust and confidence. Secondly, it improves the customer experience by making the authentication process faster and user-friendly. Lastly, KBA reduces costs by automating the authentication process and reducing the need for manual intervention. However, KBA is just one facet of an ideal strategy. Multifactor authentication provides confidence while reducing friction. Risk-based authentication tools allow organizations to assess risk to apply the appropriate level of security. Factors to consider adding to your authentication processes include: Generating unique one-time passwords (OTPs): By creating a new OTP for each transaction, you can increase the level of security. Confirm device ownership: A multifactored approach applies device intelligence checks to increase confidence that the message is reaching the correct user. Maintain low friction with secondary options: If the OTP fails or can’t be attempted by the user, working with a provider who allows an automatic default to another authentication service, such as a knowledge-based authentication solution, decreases end-user friction. Identifying potential security risks associated with KBA KBA relies on personal information that may easily be discovered via social media and other public records, which makes it vulnerable to fraud and identity theft. This highlights the need for a multilayered fraud and identity solution. The landscape of digital security is constantly changing, leveraging an arsenal of fraud and identity prevention strategies, like document verification, one-time passcode, and various identity authentication and verification measures, is critical for keeping your customers and business safe. Commonly used technologies for enhancing KBA security With the rising need for secure authentication, KBA systems have become increasingly popular. However, cyberthreats evolve at an alarming rate, making it imperative to stay current with the latest fraud schemes and how to enhance and supplement your security. Biometrics, like facial recognition and fingerprint scans, as a tactic is gaining traction, as evidenced by “85% of consumers report physical biometrics as the most trusted and secure authentication method they have recently encountered,” according to Experian’s 2023 U.S. Identity and Fraud Report. Additionally, machine learning algorithms detect patterns and anomalies in user behavior and flag any potential security breaches. Multi-factor authentication is another tool that adds an extra layer of security by requiring users to provide multiple forms of identification before logging in. Keeping up with these and other technological advancements can help ensure your KBA system stays one step ahead of potential cyberattacks. Interestingly, there’s a disconnect between the technologies consumers feel safe with and/or are prepared to use versus the technologies and strategies that organizations implement. According to the U.S. Identity and Fraud Report, biometrics are only currently used by 33% of businesses to detect and protect against fraud. An opportunity for business differentiation and driving customer loyalty through a better customer experience may be tapping into some of these lesser used – but sought after – technologies. Compliance with industry standards regarding KBA Ensuring that your system complies with industry standards regarding KBA is crucial for protecting sensitive information from unauthorized access. By implementing the following tips, you can stay ahead of the game and safeguard your organization's data. Analyze your system's current authentication methods and evaluate if they meet industry standards. Additionally, follow standard guidelines for data storage and encryption, limit access to only authorized personnel, and y current with regulations. Lastly, conduct frequent security audits and perform vulnerability tests to identify and address any potential threats. Knowledge-based authentication offers a robust security solution for businesses of all sizes, and incorporating KBA as part of a multifactor authentication strategy is a winning course of action. It provides an added layer of protection for personal data, encourages user accountability, and safeguards against unauthorized access. By leveraging appropriate KBA technologies and maintaining compliance with industry standards, it is possible to create a secure system for customers that gives you peace of mind for your business and bottom line. Experian can help you with knowledge-based authentication offerings, a multifactor authentication strategy and everything in between to enhance your existing authentication process without causing user fatigue. Increase your pass rates, confirm device ownership and add security to risky or high-value transactions, all while executing identity verification and fraud detection to protect your business from risk. The most important step is getting started. Learn more

It's that magical time of the year! The holiday season is fast approaching, and folks everywhere are gearing up for festive travels and family reunions. Unfortunately, holiday travel can sometimes lead to unforeseen circumstances, such as fraudulent activities orchestrated by scammers who impersonate property owners on well-known vacation rental platforms. These fraudsters employ schemes designed to deceive unsuspecting travelers into making payments through unsecured channels, resulting in significant financial losses for the gullible victims.  Digital identity and hotel fraud Airline and hotel fraud encompasses illicit activities aimed at airlines, hotels, booking platforms, and other travel accommodation services, including car rentals and excursions. These services often utilize loyalty programs to incentivize repeat patronage through point-based rewards. The widespread adoption of such loyalty programs has extended their appeal beyond the travel and hospitality sectors, consequently attracting fraudulent activities. Perpetrators of airline and hospitality fraud employ a range of tactics and different techniques to execute their schemes, leveraging various online forums, marketplaces, shops, and public messaging platforms. Hotels are custodians of valuable guest data, encompassing contact information and payment details. Their operational model involves serving a large pool of potential customers who are making limited visits. Consequently, compromising a hospitality employee's account could grant an identity thief access to millions of consumer records. Moreover, hotel employees are frequent targets of foreign governments aiming to procure confidential travel records to facilitate the tracking of specific individuals and groups. In contrast, restaurants primarily store transaction records with fewer customer details. However, the landscape is evolving as more establishments adopt online ordering capabilities and loyalty programs. At present, cybercriminals typically focus on the high volume of point-of-sale transactions.  As travel booms, fraudsters find new paths According to a recent Deloitte survey, Intent to travel between Thanksgiving and mid-January is up across all age and income groups. While reconnecting with friends and family remains paramount to travel during the holidays, fewer Americans are restricting their travel to visiting loved ones. The share of travelers planning to stay in hotels surged to 56%. Fraudsters will always take advantage of current circumstances, and with more people traveling again, they have taken notice — and action. The following techniques have been identified as the most employed by cybercriminals to target customers of airlines, hotels, and hospitality-related organizations:  Travel-themed phishing and fraudulent travel agency operations, sales, and advertisements of travel fraud-related tutorials.  Sales of compromised networks, user accounts, and databases containing reward/loyalty points and personally identifiable information (PII) that could be utilized for social engineering, money laundering, and other attack vectors.  Since the emergence of cyber-enabled crime, services and activities facilitating travel fraud have been extensively promoted and sought after by threat actors. Cybercriminals mainly leverage stolen card-not-present (CNP) data and reward/loyalty points obtained from compromised bank accounts to procure flights, accommodations, and other travel-related services.  Furthermore, threat actors persistently refine their strategies for harvesting reward/loyalty points through compromised accounts, deceiving victims into disclosing their travel-related documentation and data and circulating updated guidelines for circumventing hotel and airline reservation services, amongst other activities.  Protecting travelers and improving the customer experience   Combatting hospitality and hotel fraud requires collaboration between industry stakeholders, government entities, and financial institutions. Travel professionals should focus on: Enhancing data security: Invest in robust cybersecurity measures to protect guest information, payment systems for CNP, and loyalty programs.  Implementing identity verification: Utilize advanced technologies, such as biometric authentication and behavioral analytics, to verify guests' identities and prevent account fraud.  Educating staff and guests: Provide comprehensive training to employees on recognizing and reporting suspicious activities. Educate guests about potential scams and advise them to book directly through official channels.  Sharing information: Establish platforms to share intelligence and best practices to stay ahead of evolving fraud techniques.  Acting with the right solution As the travel and hospitality industry continues to thrive, so does the risk of hospitality fraud. Travelers and hoteliers alike must remain vigilant to protect their finances from various fraud schemes prevalent today. By staying informed, taking proactive measures, and fostering collaborative efforts, we can create a safer and more secure environment within the travel industry.  Experian’s identity verification solutions power advanced capabilities across the travel lifecycle. With trusted data and advanced analytics, you can gain a complete view of your future guest to improve risk management and offer an enhanced, frictionless customer experience.   Learn more *This article leverages/includes content created by an AI language model and is intended to provide general information.

If you’re a manager at a business that lends to consumers or otherwise extends credit, you certainly are aware that 10-15% of your current customers and prospective future customers are among the approximately 27 million consumers who are now – or will soon be -- fitting another bill into their monthly budgets. Early in the COVID-19 pandemic, the government issued a pause on federal student loan payments and interest. Now that the payment pause has expired, millions of Americans face a new bill averaging more than $200. Will they pay you first? If this is your concern, you aren’t alone: Experian recently held a webinar that discussed how the end of the student loan pause might affect businesses. When we surveyed the webinar attendees,  nearly 3 out of 4 responses included Risk Management as a main concerns now. Another top concern is about credit scores. Lenders and investors use credit scores – bureau scores such FICO® or VantageScore® credit score or custom credit scores proprietary to their institution – to predict credit default risk. The risk managers at those companies want to know to what extent they can continue to rely on those scores as Federal student loan payments come due and consumers experience payment shock. I’ve analyzed a large and statistically meaningful sample (10% of the US consumer population in Experian’s Ascend Sandbox) to shed some light on that question. As background information, the average consumer with student loans had lower scores before the pandemic than the average of the general population. One of my Experian colleagues has explored some of the reasons at https://www.experian.com/blogs/ask-experian/research/average-student-loan-payments). Here are some of the things we can learn from comparing the credit data of the two groups of people. I looked at a period from 2019 and from 2023 to see how things have changed: Average credit scores increased during the pandemic, continuing a long-term trend during which more Americans have been willing and able to meet all their obligations. During the COVID Public Health Emergency, consumers with student loans brought up their scores by an average of 25 points; that was 7 points more than consumers without student loans. Another way to look at it: in 2019, consumers with student loans had credit scores 23 points lower than consumers without. By 2023, that difference had shrunk to 16 points. Experian research shows that there will be little immediate impact on credit scores when the new bills come due. Time will tell whether these increased credit scores accurately reflect a reduction in the risk that consumers will default on other bills such as auto loans or bankcards soon, even as some people fit student loan bills into their budgets. It is well-known that many people saved money during the public health emergency. Since then, the personal savings rate has fallen from a pandemic high of 32% to levels between 3% and 5% this year – lower than at any point since the 2009 recession. In an October 2023 Experian survey, only 36% of borrowers said they either set aside funds or they planned using other financial strategies specifically for the resumption of their student loan payments. Additional findings from that study can be found here. Furthermore, there are changes in the way your customers have used their credit cards over the last four years:   Consumers’ credit card balances have increased over the last four years. Consumers with student loans have balances that are on average $282 (4%) more now than in 2019. That is a significantly smaller increase than for consumers without student loans, whose total credit card debt increased by an average of $1,932 (26%). Although their balances increased, the ratio of consumers’ total revolving debt balances to their credit limits (utilization) changed by less than 1% for both consumers with student loans and consumers without. In 2019, the utilization ratio was 9.8 percentage points lower for consumers with student loans than consumers without. Four years later, the difference is nearly the same (9.6 points). We can conclude that many student loan borrowers have been very responsible with credit during the Public Health Emergency. They may have been more mindful of their credit situation, and some may have planned for the day when their student loan payments will be due. As the student loan pause come to an end, there are a few things that lenders and other businesses should be doing to be ready: Even if you are not a student loan lender, it is important to stay on top of the rapidly evolving student loan environment. It affects many of your customers, and your business with them needs to adapt. Anticipate that fraudsters and abusers of credit will be creative now: periods of change create opportunities for them and you should be one step ahead. Build optimized strategies in marketing, account opening, and servicing. Consider using machine learning to make more accurate predictions. Those strategies should reflect trends in payments, balances, and utilization; older credit scores look at a single point in time. Continually refresh data about your customers—including their credit scores and important attributes related to payments, balances, and utilization patterns. Look for alternative data that will give you a leg up on the competition. In the coming weeks and months, Experian’s data scientists will monitor measures of performance of the scores and attributes that you depend on in your data-driven strategies — particularly focusing on the Kolmogorov-Smirnov (KS) statistics that will show changes in the predictive power of each score and attribute. (If you are a data-driven business, your data science team or a trusted partner should be doing the same thing with a more specific look at your customer base and business strategies.) In future reports and blog posts, we’ll shed light on the impact student loans are having on your customers and on your business. In the meantime, for more information about how to use data and advanced analytics to grow while controlling costs and risks, all while staying in compliance and providing a good customer experience, visit our website.

The gig economy — also called the sharing economy or access economy — is an activity where people earn income by providing on-demand work, services, or goods. Often, it is through a digital platform like an application (app) or website. The gig economy seamlessly connects individuals with a diverse range of services, whether it be a skilled handyman for those long-awaited office shelves, or an experienced chauffeur to quickly drive you to the airport to not miss your flight. However, there are instances when these arrangements fall short of expectations. The hired handyman may send a substitute who’s ill-equipped for the task, or the experienced driver takes the wrong shortcut leaving you scrambling to make your flight on time. On the flip side, there are numerous risks faced by those working in the gig/sharing economy, from irritable customers to dangerous situations. In such cases, trust takes a hit. The gig economy has witnessed a surge in recent years, as individuals gravitate towards flexible, freelance, and contract work instead of traditional full-time employment. This shift has unlocked a multitude of opportunities for both workers and businesses. Nevertheless, it has also ushered in challenges pertaining to security and trust. One such challenge revolves around the escalating significance of digital identity verification within the gig economy.  Digital identity verification and the gig economy  Digital identity verification encompasses validating a person's identity through digital means, such as biometric data, facial recognition, or document verification. Within the gig economy, this process has high importance, as it establishes trust between businesses and their pool of freelance or contract workers. With the escalating number of remote workers and the proliferation of online platforms connecting businesses with gig workers, verifying the identities of these individuals has become more vital than ever before.  Protecting gig users and improving the customer experience   One primary rationale behind the mounting importance of digital identity verification in the gig economy is its role in curbing fraud. As the gig economy gains traction, the risk of individuals misrepresenting themselves or their qualifications to secure work burgeons. This scenario can lead businesses to hire unqualified or even fraudulent workers, thereby posing severe repercussions for both the company and its customers. By adopting digital identity verification processes, businesses can ensure the legitimacy and competence of their workforce, subsequently decreasing the risk of fraudulent activities.  In the digital age, trust and safety are crucial for businesses to succeed. Consumers prioritize brands they can trust, and broken trust can lead to loss of customers.According to Experian's 2023 Fraud and Identity Report, over 52% of US consumers feel they’re more of a target for online fraud than they were a year ago. As such, online security continues to be a real concern for most consumers. Nearly 64% of consumers say that they are very or somewhat concerned with online security, with 32% saying they are very concerned. Establishing trust and safety measures not only protects your brand but also enhances the user experience, fosters loyalty, and boosts your business.    Role of a dedicated Trust and safety team  Trust and safety are the set of business practices for online platforms to follow to reduce the risk of users being exposed to harm, fraud, or other behaviors outside community guidelines. This is becoming an increasingly important function as online platforms look to protect their users while improving customer acquisition, engagement, and retention.  That team also safeguards organizations from security threats and scams. They verify customers' identities, evaluate actions and intentions, and ensure a safe environment for all platform users. This enables both organizations and customers to trust each other and have confidence in the platform. Their role has evolved from fraud prevention to encompass broader areas, such as user-generated content and the metaverse. With the rise of user-generated content, platforms face challenges like fake accounts, imitations, malicious links, and inappropriate content. As a result, trust and safety teams have expanded their focus and are involved in product engineering and customer journey design. Another noteworthy factor contributing to the growing emphasis on digital identity verification for trust and safety teams stems from the necessity to adhere to diverse regulations and laws. Many countries have implemented stringent regulations to safeguard workers and ensure the legal and ethical operations of businesses. In the United States, for instance, businesses must verify the identities and work eligibility of all employees, including freelancers and contractors, as part of the Form I-9 process. By leveraging digital identity verification tools, businesses can streamline these procedures and guarantee compliance with prevailing regulations.  Mitigating risk in online marketplaces  To mitigate risks in online marketplaces, businesses can take several steps, including creating a clear set of user guidelines, implementing identity verification during onboarding, enforcing multi-factor authentication for all accounts, leveraging reverification during high-risk moments, performing link analysis on the user base, and applying automation. Online identity verification plays a pivotal role in safeguarding gig workers themselves. With the surge of online platforms connecting businesses with freelancers and contractors, there comes an augmented risk of workers falling prey to scams or identity theft. By mandating digital identity verification as an integral part of the onboarding process, these platforms can shield workers and ensure they only engage with bona fide businesses.  While automation can be a powerful tool for fraud detection and mitigation, it is not a cure-all solution. Automated identity verification has its strengths, but it also has its weaknesses. While automation can spot risk signals that a human might miss, a human might spot risk signals that automation would have skipped. Therefore, for many companies, the goal should not be full automation but achieving the right ratio of automation to manual review. Manual review takes time, but it's necessary to ensure that all potential risks are identified and addressed. The more efficient these processes can be, the better, as it allows for a quicker response to potential threats. As the number of individuals embracing freelance and contract work surges, and businesses increasingly rely on these workers to carry out vital responsibilities, ensuring the security and trustworthiness of these individuals becomes paramount. By integrating digital identity verification processes, businesses can shield themselves against fraud, comply with regulations, and cultivate trust with their gig workers.  Finding the right partner While trust and safety are concerns for all online marketplaces, there’s no universal solution that will apply to all businesses and in all cases. Your trust and safety policies need to be tailored to the realities of your business. The industries you serve, regions you operate in, regulations you are subject to, and expectations of your users should all inform your processes. Experian’s comprehensive suite of customizable identity verification solutions can help you solve the problem of trust and safety once and for all.  Learn more *This article leverages/includes content created by an AI language model and is intended to provide general information.

With great risk comes great reward, as the saying goes. But when it comes to business, there's huge value in reducing and managing that risk as much as possible to maximize benefits — and profits. In today's high-tech strategic landscape, financial institutions and other organizations are increasingly using risk modeling to map out potential scenarios and gain a clearer understanding of where various paths may lead. But what are risk models really, and how can you ensure you're creating and using them correctly in a way that actually helps you optimize decision-making? Here, we explore the details. What is a risk model? A risk model is a representation of a particular situation that's created specifically for the purpose of assessing risk. That risk model is then used to evaluate the potential impacts of different decisions, paths and events. From assigning interest rates and amortization terms to deciding whether to begin operating in a new market, risk models are a safe way to analyze data, test assumptions and visualize potential scenarios. Risk models are particularly valuable in the credit industry. Credit risk models and credit risk analytics allow lenders to evaluate the pluses and minuses of lending to clients in specific ways. They are able to consider the larger economic environment, as well as relevant factors on a micro level. By integrating risk models into their decision-making process, lenders can refine credit offerings to fit the assessed risk of a particular situation. It goes like this: a team of risk management experts builds a model that brings together comprehensive datasets and risk modeling tools that incorporate mathematics, statistics and machine learning. This predictive modeling tool uses advanced algorithmic techniques to analyze data, identify patterns and make forecasts about future outcomes. Think of it as a crystal ball — but with science behind it. Your team can then use this risk model for a wide range of applications: refining marketing targets, reworking product offerings or reshaping business strategies. How can risk models be implemented? Risk models consolidate and utilize a wide variety of data sets, historical benchmarks and qualitative inputs to model risk and allow business leaders to test assumptions and visualize the potential results of various decisions and events. Implementing risk modeling means creating models of systems that allow you to adjust variables to imitate real-world situations and see what the results might be. A mortgage lender, for example, needs to be able to predict the effects of external and internal policies and decisions. By creating a risk model, they can test how scenarios such as falling interest rates, rising unemployment or a shift in loan acceptance rates might affect their business — and make moves to adjust their strategies accordingly. One aspect of risk modeling that can't be underestimated is the importance of good data, both quantitative and qualitative. Efforts to implement or expand risk modeling should begin with refining your data governance strategy. Maximizing the full potential of your data also requires integrating data quality solutions into your operations in order to ensure that the building blocks of your risk model are as accurate and thorough as possible. It's also important to ensure your organization has sufficient model risk governance in place. No model is perfect, and each comes with its own risks. But these risks can be mitigated with the right set of policies and procedures, some of which are part of regulatory compliance. With a comprehensive model risk management strategy, including processes like back testing, benchmarking, sensitivity analysis and stress testing, you can ensure your risk models are working for your organization — not opening you up to more risk. How can risk modeling be used in the credit industry? Risk modeling isn't just for making credit decisions. For instance, you might model the risk of opening or expanding operations in an underserved country or the costs and benefits of existing one that is underperforming. In information technology, a critical branch of virtually every modern organization, risk modeling helps security teams evaluate the risk of malicious attacks. Banking and financial services is one industry for which understanding and planning for risk is key — not only for business reasons but to align with relevant regulations. The mortgage lender mentioned above, for example, might use credit risk models to better predict risk, enhance the customer journey and ensure transparency and compliance. It's important to highlight that risk modeling is a guide, not a prophecy. Datasets can contain flaws or gaps, and human error can happen at any stage.. It's also possible to rely too heavily on historical information — and while they do say that history repeats itself, they don't mean it repeats itself exactly. That's especially true in the presence of novel challenges, like the rise of artificial intelligence. Making the best use of risk modeling tools involves not just optimizing software and data but using expert insight to interpret predictions and recommendations so that decision-making comes from a place of breadth and depth. Why are risk models important for banks and financial institutions? In the world of credit, optimizing risk assessment has clear ramifications when meeting overall business objectives. By using risk modeling to better understand your current and potential clients, you are positioned to offer the right credit products to the right audience and take action to mitigate risk. When it comes to portfolio risk management, having adequate risk models in place is paramount to meet targets. And not only does implementing quality portfolio risk analytics help maximize sales opportunities, but it can also help you identify risk proactively to avoid costly mistakes down the road. Risk mitigation tools are a key component of any risk modeling strategy and can help you maintain compliance, expose potential fraud, maximize the value of your portfolio and create a better overall customer experience. Advanced risk modeling techniques In the realm of risk modeling, the integration of advanced techniques like machine learning (ML) and artificial intelligence (AI) is revolutionizing how financial institutions assess and manage risk. These technologies enhance the predictive power of risk models by allowing for more complex data processing and pattern recognition than traditional statistical methods. Machine learning in risk modeling: ML algorithms can process vast amounts of unstructured data — such as market trends, consumer behavior and economic indicators — to identify patterns that may not be visible to human analysts. For instance, ML can be used to model credit risk by analyzing a borrower’s transaction history, social media activities and other digital footprints to predict their likelihood of default beyond traditional credit scoring methods. Artificial intelligence in decisioning: AI can automate the decisioning process in risk management by providing real-time predictions and risk assessments. AI systems can be trained to make decisions based on historical data and can adjust those decisions as they learn from new data. This capability is particularly useful in credit underwriting where AI algorithms can make rapid decisions based on market conditions. Financial institutions looking to leverage these advanced techniques must invest in robust data infrastructure, skilled personnel who can bridge the gap between data science and financial expertise, and continuous monitoring systems to ensure the models perform as expected while adhering to regulatory standards. Challenges in risk model validation Validating risk models is crucial for ensuring they function appropriately and comply with regulatory standards. Validation involves verifying both the theoretical foundations of a model and its practical implementation. Key challenges in model validation: Model complexity: As risk models become more complex, incorporating elements like ML and AI, they become harder to validate. Complex models can behave in unpredictable ways, making it difficult to understand why they are making certain decisions (the so-called "black box" issue). Data quality and availability: Effective validation requires high-quality, relevant data. Issues with data completeness, accuracy or relevance can lead to incorrect model validations. Regulatory compliance: With regulations continually evolving, keeping risk models compliant can be challenging. Different jurisdictions may have varying requirements, adding to the complexity of validation processes. Best practices: Regular reviews: Continuous monitoring and periodic reviews help ensure that models remain accurate over time and adapt to changing market conditions. Third-party audits: Independent reviews by external experts can provide an unbiased assessment of the risk model’s performance and compliance. These practices help institutions maintain the reliability and integrity of their risk models, ensuring that they continue to function as intended and comply with regulatory requirements. Read more: Blog post: What is model governance? How Experian can help Risk is inherent to business, and there's no avoiding it entirely. But integrating credit risk modeling into your operations can ensure stability and profitability in a rapidly evolving business landscape. Start with Experian's credit modeling services, which use expansive data, analytical expertise and the latest credit risk modeling methodologies to better predict risk and accelerate growth. Learn more *This article includes content created by an AI language model and is intended to provide general information.

This article was updated on April 23, 2024. Keeping your organization and consumers safe can be challenging as cybercriminals test new attack vectors and data breaches continually expose credentials. Instead of relying solely on usernames and passwords for user identity verification, adding extra security measures like multi-factor authentication can strengthen your defense. What is multi-factor authentication? Multi-factor authentication, or MFA, is a method of authenticating people using more than one type of identifier. Generally, you can put these identifiers into three categories based on the type of information: Something a person knows: Usernames, passwords, and personal information are common examples of identifiers from this category. Something a person has: These could include a phone, computer, card, badge, security key, or another type of physical device that someone possesses. Something a person is: Also called the inherence factor, these are intrinsic behaviors or qualities, such as a person's voice pattern, retina, or fingerprint. The key to MFA is it requires someone to use identifiers from different categories. For example, when you withdraw money from an ATM, you're using something you have (your ATM card or phone), and something you know (your PIN) or are (biometric data) to authenticate yourself. Common types of authenticators Organizations that want to implement multi-factor authentication can use different combinations of identifiers and authenticators. Some authenticator options include: One-time passwords: One-time passwords (OTPs) can be generated and sent to someone's mobile phone via text to confirm the person has the phone or via email. There are also security tokens and apps that can generate OTPs for authentication. (Something you know.) Knowledge-based authentication: Knowledge-based authentication (KBA) identity verification leverages the ability to verify account information or a payment card, “something you have,” by confirming some sequence of numbers from the account. (Something you know.) Security tokens: Devices that users plug into their phone or computer, or hold near the device, to authenticate themselves. (Something you have.) Biometric scans: These can include fingerprint and face scans from a mobile device, computer, or security token. (Something you are.) Why MFA is important It can be challenging to keep your users and employees from using weak passwords. And even if you enforce strict password requirements, you can't be sure they're not using the same password somewhere else or accidentally falling for a phishing attack. In short, if you want to protect users' data and your business from various types of attacks, such as account takeover fraud, synthetic identity fraud, and credential stuffing, you’ll need to require more than a username and password to authenticate users. That’s where MFA comes in. Because it uses a combination of elements to verify a consumer’s identity, if one of the required components in a transaction is missing or supplied incorrectly, the transaction won’t proceed. As a result, you can ensure you’re interacting with legitimate consumers and protect your organization from risk. LEARN MORE: Explore our fraud prevention solutions. How to provide a frictionless MFA experience While crucial to your organization, in-person and online identity verification shouldn’t create so much friction that legitimate consumers are driven away. Experian's 2023 U.S. Identity and Fraud Report found that 96 percent of consumers view OTPs as convenient identity verification solutions when opening a new account. An increasing number of consumers also view physical and behavioral biometrics as some of the most trustworthy recognition methods — 81 and 76 percent, respectively. To create a low friction MFA experience that consumers trust, you could let users choose from different MFA authentication options to secure their accounts. You can also create step-up rules that limit MFA requests to riskier situations — such as when a user logs in from a new device or places an unusually large order. To make the MFA experience even more seamless for consumers, consider adding automated identity verification (AIV) to your processes. Because AIV operates on advanced analytics and artificial intelligence, consumers can verify their identities within seconds without physical documentation, allowing for a quick, hassle-free verification experience. How Experian powers multi-factor authentication Experian offers various identity verification and risk-based authentication solutions that organizations can leverage to streamline and secure their operations, including: Experian’s CrossCore® Doc Capture confidently verifies identities using a fully supported end-to-end document verification service where consumers upload an image of a driver’s license, passport, or similar directly from their smartphone. Experian’s CrossCore Doc Capture adds another layer of security to document capture with a biometric component that enables the individual to upload a “selfie” that’s compared to the document image. Experian's OTP service uses additional verification checks and identity scoring to help prevent fraudsters from using a SIM swapping attack to get past an MFA check. Before sending the OTP, we verify that the number is linked to the consumer's name. We also review additional attributes, such as whether the number was recently ported and the account's tenure. Experian's Knowledge IQSM offers KBA with over 70 credit- and noncredit-based questions to help you engage in additional authentication for consumers when sufficiently robust data can be used to prompt a response that proves the person has something specific in their possession. You can even configure it to ask questions based on your internal data and phrase questions to match your brand's language. Learn more about how our multi-factor authentication solutions can help your organization verify consumer identities and mitigate fraud. Learn about our MFA solutions