In today’s fast-paced world, the telecommunications industry is not just about connecting calls or sending messages. It’s about creating seamless digital experiences, especially when onboarding new customers. However, with the rise of digital services, the industry faces an increasing challenge: the need to mitigate fraud while streamlining the onboarding process.  The digital onboarding revolution Digital onboarding has transformed the way customers join telecommunications services. No longer are people required to visit a physical store or wait for lengthy paperwork. Instead, they can sign up for mobile, internet or TV services from the comfort of their homes, often within minutes. The convenience, however, has opened new doors for fraudsters. As the onboarding process happens online, the risk of identity theft, synthetic identity fraud and other fraudulent activities has surged. So, how can telecom companies provide fritctionless experiences while keeping fraud at bay? Mitigating fraud in telecommunications onboarding Know your customer (KYC) verification: Implement robust KYC solutions to verify the identity of new customers. This may include identity document checks, facial recognition or biometric authentication. Device and location data; and velocity: Analyze the device and location data of applicants. Does the device match the customer’s claimed location? Unusual patterns could signal potential fraud.  Behavioral analysis: Monitor user behavior during the onboarding process. Frequent changes in information or suspicious browsing activity may indicate fraudulent intent.   Machine learning (ML) and artificial intelligence (AI): Leverage AI/ML algorithms to detect patterns and anomalies humans might miss. These technologies can adapt and evolve to stay ahead of fraudsters.   Document verification: Use document verification services to ensure that documents provided by customers are genuine. This can include checks for altered or forged documents. Industry data sharing–consortia: Collaborate with industry databases and share fraud-related information to help identify applicants with a history of fraudulent activity or reveal patterns. The balancing act While it’s crucial to mitigate fraud, telecommunication companies must strike a balance between security and a seamless onboarding experience. Customers demand a hassle-free process, and overly stringent security measures can deter potential subscribers. By combining advanced technology, behavioral analysis and proactive fraud prevention strategies, telecom companies can create a secure digital onboarding journey that minimizes risk without compromising user experience. In doing so, they empower customers to embrace the convenience of digital services while staying one step ahead of fraudsters in today’s interconnected world.  Learn more about Experian and the telecom industry Learn more about our fraud and identity solutions

Authorized Push Payment fraud, also known as APP fraud or APP scams, involves a fraudster persuading a victim to willingly deposit funds to their account or to the account of a complicit third party, also known as a money mule. This type of fraud often includes social engineering of the victim using fake investment schemes, impersonation scams, purchase scams or other schemes. Social engineering clouds victims' judgments and encourages them to make payments willingly to one or more money mules, with funds eventually reaching fraudsters' accounts. This type of fraud has become more attractive to criminals since the advent of real-time payment systems, which are now a reality worldwide. Fraud fueled by real-time payments Authorized push payment fraud is becoming more prevalent, and it is imperative that you know how to detect and prevent it to safeguard your organization. Real-time payment systems, such as Faster Payments in the United Kingdom (UK), PIX in Brazil, the New Payments Platform in Australia, and FedNow in the USA, make real-time payment fraud a reality.  APP fraud is notoriously difficult for banks to prevent because the victim is sending the money themselves, and steps that banks take to authenticate customers are ineffective, as the customer will pass identity checks. The victims cannot reverse a payment once they realize they have been conned, as payments made using real-time payment schemes are irrevocable. APP fraud is particularly prevalent in countries where banks have an infrastructure that facilitates fast or immediate transfers, like the UK. Learn more about the new UK legislation around APP fraud Reimbursment is vital to victims Some common types of authorized push payment fraud include attacks on individuals like romance scams, family emergency swindles, targeting property transactions, and intercepting supplier payments. To protect against APP fraud, it is important to employ layered fraud protection across all products and channels used to manage real-time payments. But that alone is not enough. Reimbursement is vital in reversing the financial distress caused by APP scams, but it cannot reverse the emotional distress these scams cause. Prevention, detection, and awareness measures must be moved up on the agenda for banks, non-traditional lenders, PSPs (Payment Service Providers), and customers alike to ensure that the customer is protected at every stage of the payment journey.  Effective alerts are a key focus area for preventing customers from falling victim to APP scams. An effective warning is one that is dynamic and tailored to the customer’s payment journey. Recent research indicates that minor changes to notifications across banking apps can have the potential to drastically reduce the number of individuals that fall victim to APP fraud. The biggest effects were achieved when a combination of risk-based and Call to Action (CTA) warnings were implemented over a period of time. A collective effort across the banking industry and beyond is crucial to protect customers and tackle the fight against APP fraud. Banks, non-traditional lenders, and PSPs can raise awareness to educate their customers on the signs and risks of APP scams, and work with industry oversight bodies to commit to voluntary standards and codes to ensure good customer outcomes. Online forums, social media platforms, and influential voices also have a role to play in raising awareness of and preventing scams. Customers can also help by being vigilant and reading and acting upon warnings and information presented to them.  Authorized push payment fraud prevention To effectively combat authorized push payment fraud, financial institutions must implement a range of measures, including:  Direct communication with consumers.  Enhanced transaction monitoring.  Effective risk mitigation and management.  Improved employee education.  Public awareness campaigns.   In response to this growing threat, banks have introduced various checks and balances, such as the Confirmation of Payee (CoP) service in the UK, which cross-references bank details with the account holder's name when processing online payments.  Banks are also leveraging sophisticated fraud prevention software stacks, incorporating machine learning and contextual data to identify and flag suspicious transactions. By utilizing AI technologies, financial institutions can process  data points faster and enhance their fraud detection capabilities, mitigating identity risk and safeguarding customer accounts. Clear communication with customers is essential in the fight against APP fraud. Higher-risk companies now include warnings in their communications, advising customers not to act on messages that request payment into new bank accounts.  Financial institutions can also offer cool-off periods before payments are sent, increase due diligence around payment destinations, and monitor accounts that regularly receive high-value payments. Additionally, financial institutions can play a crucial role in educating their customers and promoting awareness around this increasingly common type of fraud. By combining these approaches with robust fraud prevention software, the public can fight against this type of fraudulent attack.  Taking the next steps with the right partner At Experian, we offer rich data sources, advanced analytics capabilities, and the consultancy services needed to rapidly adopt data analytics solutions that mitigate fraud risks. Our solutions are used by PSPs of all types and sizes – including some of the largest banks – to identify potentially fraudulent customers and transactions, and to ensure that action is taken in real time to prevent fraudulent payments being made.  Learn more about our fraud management solutions *This article leverages/includes content created by an AI language model and is intended to provide general information.

Model governance is growing increasingly important as more companies implement machine learning model deployment and AI analytics solutions into their decision-making processes. Models are used by institutions to influence business decisions and identify risks based on data analysis and forecasting. While models do increase business efficiency, they also bring their own set of unique risks. Robust model governance can help mitigate these concerns, while still maintaining efficiency and a competitive edge. What is model governance? Model governance refers to the framework your organization has in place for overseeing how you manage your development, model deployment, validation and usage.1 This can involve policies like who has access to your models, how they are tested, how new versions are rolled out or how they are monitored for accuracy and bias.2 Because models analyze data and hypotheses to make predictions, there's inherent uncertainty in their forecasts.3 This uncertainty can sometimes make them vulnerable to errors, which makes robust governance so important. Machine learning model governance in banks, for example, might include internal controls, audits, a thorough inventory of models, proper documentation, oversight and ensuring transparent policies and procedures. One significant part of model governance is ensuring your business complies with federal regulations. The Federal Reserve Board and the Office of the Comptroller of the Currency (OCC) have published guidance protocols for how models are developed, implemented and used. Financial institutions that utilize models must ensure their internal policies are consistent with these regulations. The OCC requirements for financial institutions include: Model validations at least once a year Critical review by an independent party Proper model documentation Risk assessment of models' conceptual soundness, intended performance and comparisons to actual outcomes Vigorous validation procedures that mitigate risk Why is model governance important — especially now? More and more organizations are implementing AI, machine learning and analytics into their models. This means that in order to keep up with the competition's efficiency and accuracy, your business may need complex models as well. But as these models become more sophisticated, so does the need for robust governance.3 Undetected model errors can lead to financial loss, reputation damage and a host of other serious issues. These errors can be introduced at any point from design to implementation or even after deployment via inappropriate usage of the model, drift or other issues. With model governance, your organization can understand the intricacies of all the variables that can affect your models' results, controlling production closely with even greater efficiency and accuracy. Some common issues that model governance monitors for include:2 Testing for drift to ensure that accuracy is maintained over time. Ensuring models maintain accuracy if deployed in new locations or new demographics. Providing systems to continuously audit models for speed and accuracy. Identifying biases that may unintentionally creep into the model as it analyzes and learns from data. Ensuring transparency that meets federal regulations, rather than operating within a black box. Good model governance includes documentation that explains data sources and how decisions are reached. Model governance use cases Below are just three examples of use cases for model governance that can aid in advanced analytics solutions. Credit scoring A credit risk score can be used to help banks determine the risks of loans (and whether certain loans are approved at all). Governance can catch biases early, such as unintentionally only accepting lower credit scores from certain demographics. Audits can also catch biases for the bank that might result in a qualified applicant not getting a loan they should. Interest rate risk Governance can catch if a model is making interest rate errors, such as determining that a high-risk account is actually low-risk or vice versa. Sometimes changing market conditions, like a pandemic or recession, can unintentionally introduce errors into interest rate data analysis that governance will catch. Security challenges One department in a company might be utilizing a model specifically for their demographic to increase revenue, but if another department used the same model, they might be violating regulatory compliance.4 Governance can monitor model security and usage, ensuring compliance is maintained. Why Experian? Experian® provides risk mitigation tools and objective and comprehensive model risk management expertise that can help your company implement custom models, achieve robust governance and comply with any relevant federal regulations. In addition, Experian can provide customized modeling services that provide unique analytical insights to ensure your models are tailored to your specific needs. Experian's model risk governance services utilize business consultants with tenured experience who can provide expert independent, third-party reviews of your model risk management practices. Key services include: Back-testing and benchmarking: Experian validates performance and accuracy, including utilizing statistical metrics that compare your model's performance to previous years and industry benchmarks. Sensitivity analysis: While all models have some degree of uncertainty, Experian helps ensure your models still fall within the expected ranges of stability. Stress testing: Experian's experts will perform a series of characteristic-level stress tests to determine sensitivity to small changes and extreme changes. Gap analysis and action plan: Experts will provide a comprehensive gap analysis report with best-practice recommendations, including identifying discrepancies with regulatory requirements. Traditionally, model governance can be time-consuming and challenging, with numerous internal hurdles to overcome. Utilizing Experian's business intelligence and analytics solutions, alongside its model risk management expertise, allows clients to seamlessly pass requirements and experience accelerated implementation and deployment. Experian can optimize your model governance Experian is committed to helping you optimize your model governance and risk management. Learn more here. References 1Model Governance," Open Risk Manual, accessed September 29, 2023. https://www.openriskmanual.org/wiki/Model_Governance2Lorica, Ben, Doddi, Harish, and Talby, David. "What Are Model Governance and Model Operations?" O'Reilly, June 19, 2019. https://www.oreilly.com/radar/what-are-model-governance-and-model-operations/3"Comptroller's Handbook: Model Risk Management," Office of the Comptroller of the Currency. August 2021. https://www.occ.treas.gov/publications-and-resources/publications/comptrollers-handbook/files/model-risk-management/pub-ch-model-risk.pdf4Doddi, Harish. "What is AI Model Governance?" Forbes. August 2, 2021. https://www.forbes.com/sites/forbestechcouncil/2021/08/02/what-is-ai-model-governance/?sh=5f85335f15cd

Have you heard about the mischievous ghosts haunting our educational institutions? No, I am not talking about Casper's misfit pals. These are the infamous ghost students! They are not here for a spooky study session, oh no! They are cunning fraudsters lurking in the shadows, pretending to be students who never attend classes. It is taking ghosting to a whole new level. Understanding ghost student fraud Ghost student fraud is a serious and alarming issue in the educational sector. The rise of online classes due to the pandemic has made it easier for fraudsters to exploit application systems and steal government aid meant for genuine students. Community colleges have become primary targets due to slower adoption of cybersecurity defenses. It is concerning to hear that a considerable number of applications, such as in California (where Social Security numbers are not required at enrollment), are fictitious, with potential losses in financial aid meant for students in need. The use of stolen or synthetic identities in creating bot-powered applications further exacerbates the problem. The consequences of enrollment fraud can have a profound impact on institutions and students. The recent indictment of individuals involved in enrollment fraud, where identities were stolen to receive federal student loans, highlights the severity of the issue. Unfortunately, the lack of awareness and inadequate identity document verification processes in many institutions make it difficult to fully grasp the extent of the problem. What is a ghost student? Scammers use different methods to commit ghost student loan fraud, including creating fake schools or enrolling in real colleges. Some fraudsters use deceitful tactics to obtain the real identities of students, and then they use it to fabricate loan applications. Types of ghost loan fraud, include: Fake loan offers: Fraudsters contact students via various channels, claiming to offer exclusive student loan opportunities with attractive terms and low interest rates. They often request personal and financial information including their SSN and bank account information and use it to create ghost loans. Identity theft: Threat actors will steal personal info through data breaches or phishing. They will then forge loan applications using the victim’s identity. Targeting vulnerable individuals: Ghost student loan fraud tends to prey on those already burdened by debt. Scammers may target borrowers with poor credit history, promising loan forgiveness or debt consolidation plans in exchange for a fee. Once the victim pays, the fraudsters disappear. Ultimately, addressing ghost student fraud requires a multi-faceted approach involving collaboration between educational institutions, government agencies, and law enforcement to safeguard the accessibility and integrity of education for all deserving students. Safeguarding the financial integrity of educational institutions One powerful weapon in the battle against ghost student fraudsters is the implementation of robust identity verification solutions. Financial institutions, online marketplaces, and government entities have long employed such tools to verify the authenticity of individuals, and their application in the educational domain can be highly effective. By leveraging these tools, institutions can swiftly and securely carry out synthetic fraud detection and confirm the identity of applicants by cross-referencing multiple credible sources of information. For instance, government-issued IDs can be verified against real-time selfies, email addresses can be screened against reliable databases, and personally identifiable information (PII) can be compared to third-party dark web data to detect compromised identities. Clinching evidence from various sources renders it nearly impossible for fraudsters to slip past the watchful eyes of enrollment officers. Moreover, implementation of identity verification measures can be facilitated through low-code implementation, ensuring seamless integration into existing enrollment workflows without requiring extensive technical expertise or incurring exorbitant development costs. To further fortify security measures, educational institutions may consider incorporating biometric enrollment and authentication solutions. By requiring face or voice biometrics for accessing school resources, institutions can create an additional layer of protection against fraudsters and their ethereal counterparts. The reluctance of fraudsters to enroll their own biometric data serves as a powerful deterrent against their intrusive activities. Taking action By adopting these robust measures, higher educational institutions can fortify their defenses against ghost student fraud and maintain the integrity of their finances. The use of online identity verification methods and biometric authentication systems not only strengthens the enrollment process but serves as a stringent reminder that there is no resting place for fraudsters within the hallowed halls of education. To learn more about how Experian can help you leverage fraud prevention solutions, visit us online or request a call. *The SSN Verification tool, better known as eCBSV is also a tool that can be utilized to verify SSN.  *This article leverages/includes content created by an AI language model and is intended to provide general information.

Debt collectors need to find, contact and work with people to collect on unpaid accounts. That can be challenging enough. But when synthetic identity fraud accounts are mixed into your collection portfolio, you'll waste resources trying to collect from people who don't even exist. What is synthetic identity fraud? Synthetic identity fraud happens when fraudsters mix real and fake identity information — such as a stolen Social Security number (SSN) with a fake name and date of birth — to create an identity. Fraudsters occasionally try to quickly create and use synthetic IDs to commit fraud. But these are often more complex operations, and the fraudsters spend months or years building synthetic IDs. They might then use or sell an identity once it has a thick credit file, matching identification documents and a robust social media presence. The resulting fraud can have a significant impact on lenders. By some estimates, annual synthetic fraud losses for consumer loans and credit cards could be as high as $11 billion.1 Total annual losses are likely even higher because organizations may misclassify synthetic fraud losses — or not classify them at all — and fraudsters also target other types of organizations, such as business lenders and medical care providers Recognizing synthetic identities and fraud losses Organizations can ideally detect and stop synthetic IDs at account opening. If a fraudster slips past the first line of defense, fraud detection tools that aren't tailored for synthetic identity fraud might not flag the account as suspicious. This is especially true when fraudsters make several on-time payments, mirroring a legitimate account holder's behavior, before stopping payments or busting out. Sometimes, these past-due accounts get sent to collections before being written off as a credit loss. That creates new issues. Debt management and collections systems can help collections departments prioritize outreach and minimize charge-offs. But if you add fraudulent accounts to the mix, you wind up throwing away your time and resources. Even when you properly classify these written-off accounts as fraud losses, it can be hard to distinguish between first-party fraud by a legitimate consumer and synthetic identity fraud losses. However, the distinction can be important for optimizing your credit risk strategy. Detection is the key to prevention Keeping synthetic fraud out of collection portfolios requires a multi-layered approach to fraud management. You need systems to help stop synthetic fraud at the front door and ongoing account monitoring throughout the customer lifecycle. You also want fraud solutions that use data from multiple sources to recognize synthetic identities, such as credit bureau, public records, consortium and behavioral data. Experian's industry-leading fraud and identity solutions Experian's synthetic identity fraud and identity resolution solutions make it a leader in the space. These include: Sure Profile™uses credit, public record and identity-specific data to create a composite history of a consumer's identity and generate a risk score. You can automate risk-based decisions based on the score, and you'll have access to the underlying Sure Profile attributes. CrossCore® is a cloud-based identity and fraud management platform that you can connect to Experian, third-party and internal tools to get a 360-degree view of your accounts throughout the customer lifecycle. Experian partners with the Social Security Administration to offer an electronic Consent Based Social Security Number Verification (eCBSV) service, which can help you determine if an SSN, name and date of birth match. It can be an important part of a step-up verification when risk signals indicate that an identity might not be legitimate. View our tip sheet to learn more about keeping fraudulent accounts out of your collection portfolio. Download now 1Experian (2022). Preventing synthetic identity fraud

The 2023 hurricane season is upon us. This year, over 21 named storms were predicted for this year, and we have already seen storms make landfall. One of the biggest dangers that hurricanes pose to the automobile industry is vehicle water and/or flood damage. In 2022, FEMA paid out over $1 billion for flood damage to automobiles in the United States. This damage can have a significant impact on businesses in the automobile industry, including: New car dealerships: Flood damage can destroy new cars and trucks, forcing dealerships to replace them. This can be a costly proposition, especially in a time when supply chains are already disrupted. Used car dealerships: Flood damage can also damage used cars, making them less valuable or even unsalable. This can lead to lost revenue for used car dealerships. Auto repair shops: Auto repair shops may be called upon to repair flood-damaged vehicles. However, some flood-damaged vehicles may be beyond repair. This can lead to lost revenue for auto repair shops. Auto parts suppliers: Auto parts suppliers may also be impacted by flood damage. If factories that produce auto parts are flooded, it can disrupt the supply of auto parts to dealerships and repair shops. In addition, it is important to note that flooded cars may still be on the road. And these vehicles may not be in operation in the geography where the reported water and/or flood damage occurred. To help you stay up to date on the latest insights into flood damaged vehicles we’ve put together a complimentary Vehicle Insights: Water and Flood Reported Events Infographic. You’ll learn: • What percentage of owners repurchase a different vehicle after water or flood damage for their current vehicle • Where was the damage originally reported? • Where are vehicles with water or flood damage currently located? Download the Vehicle Insights: Water and Flood Reported Events Infographic Now! Here is another resource you may find useful to help mitigate the risk of purchasing flood damaged vehicles. Check out our Free AutoCheck Flood Risk Check.

Data-driven machine learning model development is a critical strategy for financial institutions to stay ahead of their competition, and according to IDC, remains a strategic priority for technology buyers.  Improved operational efficiency, increased innovation, enhanced customer experiences and employee productivity are among the primary business objectives for organizations that choose to invest in artificial intelligence (AI) and machine learning (ML), according to IDC’s 2022 CEO survey.   While models have been around for some time, the volume of models and scale at which they are utilized has proliferated in recent years. Models are also now appearing in more regulated aspects of the business, which demand increased scrutiny and transparency.   Implementing an effective model development process is key to achieving business goals and complying with regulatory requirements. While ModelOps, the governance and life cycle management of a wide range of operationalized AI models, is becoming more popular, most organizations are still at relatively low levels of maturity. It's important for key stakeholders to implement best practices and accelerate the model development and deployment lifecycle.   Read the IDC Spotlight Challenges impeding machine learning model development  Model development involves many processes, from wrangling data, analysis, to building a model that is ready for deployment, that all need to be executed in a timely manner to ensure proper outcomes. However, it is challenging to manage all these processes in today’s complex environment.   Modeling challenges include:  Infrastructure: Necessary factors like storage and compute resources incur significant costs, which can keep organizations from evolving their machine learning capabilities.   Organizational: Implementing machine learning applications requires talent, like data scientists and data and machine learning engineers.  Operational: Piece meal approaches to ML tools and technologies can be cumbersome, especially on top of data being housed in different places across an organization, which can make pulling everything together challenging.  Opportunities for improvement are many While there are many places where individuals can focus on improving model development and deployment, there are a few key places where we see individuals experiencing some of the most time-consuming hang-ups.   Data wrangling and preparation   Respondents to IDC's 2022 AI StrategiesView Survey indicated that they spend nearly 22% of their time collecting and preparing data. Pinpointing the right data for the right purpose can be a big challenge. It is important for organizations to understand the entire data universe and effectively link external data sources with their own primary first party data. This way, stakeholders can have enough data that they trust to effectively train and build models.   Model building  While many tools have been developed in recent years to accelerate the actual building of models, the volume of models that often need to be built can be difficult given the many conflicting priorities for data teams within given institutions. Where possible, it is important for organizations to use templates or sophisticated platforms to ease the time to build a model and be able to repurpose elements that may already be working for other models within the business.   Improving Model Velocity Experian’s Ascend ML BuilderTM is an on-demand advanced model development environment optimized to support a specific project. Features include a dedicated environment, innovative compute optimization, pre-built code called ‘Accelerators’ that simply, guide, and speed data wrangling, common analyses and advanced modeling methods with the ability to add integrated deployment.  To learn more about Experian’s Ascend ML Builder, click here.   To read the full Technology Spotlight, download “Accelerating Model Velocity with a Flexible Machine Learning Model Development Environment for Financial Institutions” here.  Download spotlight *This article includes content created by an AI language model and is intended to provide general information. 

Signing new residents is not just about offering the right apartment home at the right price. Granted, that's obviously a huge part of the equation, but operators also need to provide prospective residents with a seamless shopping and leasing experience. If potential renters encounter any friction or hardships during this time, they are likely to take their home search elsewhere. Today's prospective renters want to be able to tour and gather information about apartments on their own time, and they want a quick "yes" or "no" after completing their lease application. With that in mind, automated income and employment verification - among other tools and solutions like self-guided and virtual tools, chatbots, and automated form fills, is one of the main features and technologies operators should consider implementing if they haven't already done so, to ensure we are meeting the renter where they are. Automated verification of identity, income, assets and employment For leasing managers, automated technology eliminates the need to manually collect the documents required to verify a prospect's self-reported information, which can be a tremendously time-consuming task that extends the overall leasing timeline and increases the exposure due to unoccupied units. Automated verification also reduces the opportunity for bad-faith applicants to submit fraudulent documents related to their financials or employment history. The best part about verification is the variety of options available; leasing managers can pick and choose verification options which meet their needs without breaking the tenant screening budget. Experian has multiple verification solutions and use cases to compare which one may work best for your community. The Experian difference To learn more about our suite of rental property solutions and ways we support the tenant screening process with data-driven insights, and verifications, please visit us at  www.experian.com/rental. This article was originally published on MFI. Read more on MFI for a detailed look at additional tools and technologies operators should consider. 

Electric vehicles (EVs) are sustaining prominence throughout the automotive industry, and data from the second quarter of 2023 shows registrations are still on the rise. According to Experian’s Automotive Consumer Trends Report: Q2 2023, 7.50% of new vehicle registrations were EVs, resulting in more than 2.7 million EVs in operation in the US, an increase from the approximate 1.7 million this time last year. Though, despite the continued growth in EV popularity, data found that 85% of EV owners also have a gas-powered vehicle in their household garage and 11% have a hybrid vehicle. It’s possible that majority of consumers prefer to have a secondary vehicle for comfortability, considering charging stations aren’t as accessible in some states and gas operated vehicles offer more miles. That said, it’s important for automotive professionals to have additional insight when helping consumers find a vehicle that fits their lifestyle, such as if they have plans to keep another vehicle in addition to their EV and the type of vehicle they’re interested in. Luxury EVs dominate market share When looking at new EV registrations by vehicle class in the last 12 months, luxury EVs accounted for 77.73%, while non-luxury made up the remaining 22.67%. It’s notable that Tesla led the luxury EV registration market share in Q2 2023 at 81.61%, followed by BMW at 4.42%, Rivian at 3.76%, Mercedes-Benz at 3.27%, and Audi coming in at 2.52%. For non-luxury EVs, Chevrolet accounted for 24.21% of new registration market share this quarter and Ford was not far behind at 24.00%, followed by Volkswagen at 15.77%, Hyundai at 15.22%, and Kia at 9.17%. Breaking the data down further, Tesla made up four of the top five models for luxury EVs in Q2 2023, which explains the dominance in overall luxury EV market share. This quarter, the Model Y came in at 47.36%, followed by the Model 3 at 27.30%, the Model X (4.42%), the BMW i4 (2.82%), and the Model S (2.53%). Meanwhile, the Chevrolet Bolt EUV accounted for 17.67% of the non-luxury EV market share in Q2 2023 and the Volkswagen ID.4 came in second at 15.77%, followed closely by the Ford Mustang Mach-E at 15.74%, and the Hyundai IONIQ 5 at 11.13%. Despite Tesla comprising the majority of luxury EV market share, something professionals should keep in mind is other OEMs making their way into the market, which will give consumers more models to choose from as the gas alternative vehicles continue to grow in popularity. This will be important data to leverage in years to come when helping a consumer find a vehicle. To learn more about EV insights, view the full Automotive Consumer Trends Report: Q2 2023 presentation.

The deprecation of third-party cookies is one of the biggest changes to the automotive digital marketing landscape in recent years. Third-party cookies have long been used to track users across the web, which allows advertisers to target them with relevant ads. However, privacy concerns have led to the deprecation of third-party cookies in major browsers, such as Google Chrome and Safari. This change will have a significant impact on automotive marketers, as it will make it more difficult to track users and target them with ads. However, there are several things that auto marketers can do to prepare for the cookieless future. Here are some marketing tips when the cookie deprecates: Focus on first-party data. First-party data is data that you collect directly from your customers, such as email addresses, contact information, and purchase history. This data is more valuable than third-party data, as it is more accurate and reliable. You can use first-party data to create targeted ad campaigns and personalize your marketing messages. Work with a third-party aggregator. Automotive marketers can tackle a cookie-less world by using other sources of consumer data insights. For instance, a third-party data aggregator, like Experian, has access to numerous sources, platforms, and websites. Beyond that, we have access to a vast range of specific consumer data insights, including vehicle ownership, registrations, vehicle history data, and lending data. We take all that information and help marketers segment audiences and predict what consumers will do next. Leverage Universal Identifiers. Universal Identifiers provide a shared identity to identity across the supply chain without syncing cookies. First-party data (such as CRM data) and offline data can be used to create Universal Identifiers. Use contextual targeting and audience modeling. Contextual targeting involves targeting ads based on the content that a user is viewing. Contextual targeting is a privacy-friendly way to target ads and it can be effective in reaching relevant audiences. Utilize Identity Graphs. An identity graph combines Personally Identifiable Information (PII) with non-PIIs like first-party cookies and publisher IDS. Identity graphs will allow cross-channel and cross-platform tracking and targeting. Experian’s Graph precisely connects digital identifiers such as MAIDS, IPs, cookies, universal IDs, and hashed emails to households providing marketers with a consolidated view of consumers’ digital IDs. The deprecation of third-party cookies will be a challenge for auto marketers, but it's also an opportunity to rethink marketing strategies and focus on building stronger relationships with customers. Here are some additional cookieless marketing tips: Start preparing now. Don't wait until the last minute to start preparing for the cookieless future. Start collecting first-party data from your customers now. Be transparent with your customers. Let your customers know what data you are collecting and how you are using it. Make sure that you have their consent to collect and use their data. Be creative with your marketing campaigns. There are several ways to reach your target audience without relying on cookies. Be creative with your marketing campaigns and experiment with different strategies. Sample audience segments include: Consumers in market Loan status In positive equity Driving a specific year/make/model 1000+ lifestyle events such as new baby, marriage, new home Geography, demographics, psychographics To take it to the next level, we can use predictive analytics to go beyond what cookie data could provide by predicting who is ready to purchase a vehicle. For example, an auto marketer may have used cookie data to find buyers who had shown interest in a hybrid sedan, but that’s where it ended. When combining audience segmentation with a predictive model, marketers can target and identify consumers in-market and most likely ready to purchase a specific model. In this way, the data-driven insights from a third-party data provider specializing in automotive insights can replace the cookie-driven approach and take it a significant step beyond. The cookieless future is coming, but marketers who are prepared will be able to succeed. By focusing on first-party data, contextual targeting, and partnerships, auto marketers can reach their target audiences and achieve marketing goals.  

In financial crime, fraudsters are always looking for new avenues to exploit. The mortgage industry has traditionally been a primary target for fraudsters. But with the 30-year fixed-mortgage rate average above 7.19% for the month of September, it has caused an inherent slowdown in the volume of home purchases. As a result, criminals are turning to other lucrative opportunities in mortgage transactions. They have evolved their techniques to capitalize on unsuspecting homeowners and lenders by shifting their focus from home purchases to Home Equity Line of Credit (HELOC), as they see it as a more compelling option.  Understanding mortgage fraud  Mortgage fraud occurs when individuals or groups intentionally misrepresent information during the mortgage application process for personal gain. The most common forms of mortgage fraud include income misrepresentation, false identity, property flipping schemes, and inflated property appraisals. Over the years, financial institutions and regulatory bodies have implemented robust measures to combat such fraudulent activities.  As the mortgage industry adapts to counter established forms of fraud, perpetrators are constantly seeking new opportunities to circumvent detection. This has led to a shift in fraud trends, with fraudsters turning their focus to alternative aspects of the mortgage market. One area that has captured recent attention is HELOC fraud, also known as home equity loan fraud.  HELOC fraud: An attractive target for fraudsters  What is a HELOC?  HELOCs are financial products that allow homeowners to borrow against the equity in their homes, often providing flexible access to funds. While HELOCs can be a valuable financial tool for homeowners, they also present an attractive opportunity for fraudsters due to their unique characteristics.  HELOC fraud schemes  An example of a home equity loan fraud scheme is a fraudster misrepresenting himself to deceive a credit union call center employee into changing a member’s address and phone number. Three days later, the fraudster calls back to reset the member’s online banking password, allowing the fraudster to login to the member’s account. Once logged in, the fraudster orders share drafts to be delivered to the new address they now control. The fraudster then forges three share drafts totaling $309,000 and funds them through unauthorized advances against the member’s HELOC through online banking platforms.   Why HELOCs are becoming the next target for mortgage fraud  Rising popularity: HELOCs have gained significant popularity in recent years, enticing fraudsters seeking out opportunities with larger potential payouts.  Vulnerabilities in verification: The verification process for HELOCs might be less rigorous than traditional mortgages. Fraudsters could exploit these vulnerabilities to manipulate property valuations, income statements, or other critical information.  Lack of awareness: Unlike conventional mortgages, there may be a lack of awareness among homeowners and lenders regarding the specific risks associated with HELOCs. This knowledge gap can make it easier for fraudsters to perpetrate their schemes undetected.  Home equity loans do not have the same arduous process that traditional first mortgages do. These loans do not require title insurance, have less arduous underwriting processes, and do not always require the applicant to be physically present at a closing table to gain access to cash. The result is that those looking to defraud banks can apply for multiple HELOC loans simultaneously while escaping detection.  Prevention and safeguards  There are several preventive measures and fraud prevention solutions that can be established to help mitigate the risks associated with HELOCs. These include:  Education and awareness: Homeowners and lenders must stay informed about the evolving landscape of mortgage fraud, including the specific risks posed by HELOCs. Awareness campaigns and educational materials can play a significant role in spreading knowledge and promoting caution.   Enhanced verification protocols: Lenders should implement advanced verification processes and leverage data analytics and modeling thorough property appraisals, income verification, and rigorous background checks. Proper due diligence can significantly reduce the chances of falling victim to HELOC-related fraud.  Collaboration and information sharing: Collaboration between financial institutions, regulators, and law enforcement agencies is essential to combat mortgage fraud effectively. Sharing information, best practices, and intelligence can help identify emerging fraud trends and deploy appropriate countermeasures.  Acting with the right solution  Mortgage fraud is a constant threat that demands ongoing vigilance and adaptability. As fraudsters evolve their tactics, the mortgage industry must stay one step ahead to safeguard homeowners and lenders alike. With concerns over HELOC-related fraud rising, it is vital to raise awareness, strengthen preventive measures, and foster collaboration to protect the integrity of the mortgage market. By staying informed and implementing robust safeguards, we can collectively combat and prevent mortgage fraud from disrupting the financial security of individuals and the industry.  Experian mortgage is powering advanced capabilities across the mortgage lifecycle by gaining market intelligence, enhancing customer experience to remove friction and tapping into industry leading data sources to gain a complete view of borrower behavior.   To learn more about our HELOC fraud prevention solutions, visit us online or request a call.  *This article leverages/includes content created by an AI language model and is intended to provide general information.

In today's fast-paced financial landscape, financial institutions must stay ahead of the curve when it comes to account opening and onboarding. Digital account opening, empowering a prospective client to securely and efficiently open a new account, is key to how banks, credit unions and other financial institutions grow their business and expand their portfolio. Regardless of the time, money and other resources a financial institution invests in marketing to the right target prospect and tailoring an attractive offer, it’s worthless if that prospective customer can’t complete the process due to a poor account opening experience. Unhappy customers vote with their feet. A recent Experian study found that of the more 2,000 consumers surveyed who’d opened a new account in the last six months, 37% took their business elsewhere due to a negative account opening experience.   The choice of a reliable partner can make all the difference to your account opening and onboarding experience. The right partner must provide your financial institution with access to the freshest credit data; advanced analytics, scores and models to empower you to say yes to the right customers that meet your lending criteria; and industry-leading decision engines that make the best decisions and enable you to provide a seamless customer experience.  Moreover, the right partner will also help you in maintaining high levels of security without compromising user experience, all while adhering to regulatory compliance.  Recently, Liminal, a leading advisory and market intelligence firm specializing in the digital identity, cybersecurity, and fintech markets, released its highly anticipated Link™ Index Report for Account Opening in Financial Services, which evaluates solution providers in the financial sector, in the areas of compliance and fraud prevention for account opening. The report recognized Experian as a market leader for compliance and fraud prevention capabilities and market execution. Experian’s identity verification and fraud prevention solutions, including CrossCore® and Precise ID®, received the highest score out of the 32 companies highlighted in the report. It found that Experian was recognized by 94% of buyers and 89% identified Experian as a market leader.   “We’re thrilled to be named the top market leader in compliance and fraud prevention capabilities and execution by Liminal’s Link Index Report,” said Kathleen Peters, Chief Innovation Officer for Experian’s Decision Analytics business in North America. “We’re continually innovating to deliver the most effective identity verification and fraud prevention solutions to our clients so they can grow their business, mitigate risk and provide a seamless customer experience.”  You can access the full report here. To learn more about Experian’s award-winning fraud solutions, visit our identity fraud hub.  Download Liminal Link Index Report

In today’s age, where speed and convenience are paramount, lenders must transform their digital income verification experience to meet customer expectations. Leveraging the benefits of instant verification is crucial to delivering a seamless experience. However, there are situations where instant verification may not be available or unable to verify customers. This is where the value of incorporating user-permissioned verification into your workflow becomes evident.   Let’s explore the advantages of using a combination of instant and permissioned verification and how they can synergistically enhance coverage, reduce costs, improve efficiency, and deliver an exceptional customer experience.  Instant verification: The epitome of efficiency and experience  Instant verification technology enables lenders to access real-time customer data, making it the pinnacle of verification efficiency. Its ability to deliver immediate insights facilitates quick decision-making, ensuring a seamless and frictionless experience for lenders and customers. There are several benefits to streamlining your verification process, including:   Speed and efficiency: Eliminate the time-consuming process of manually gathering and analyzing data to expedite loan approvals and reduce customer waiting times.  Enhanced user experience: With real-time results, customers can complete their applications quickly and effortlessly, leading to increased satisfaction and higher conversion rates.  Reduced risk: Assess applicant information promptly, maintaining the security and integrity of lending processes.  Permissioned verification: Expanding coverage and engaging customers  While instant verification technology offers numerous advantages, it may not always be available or suitable for every customer. This is where permissioned verification plays a vital role. By integrating permissioned verification into the verification workflow, lenders can expand coverage and keep customers engaged in a digital channel, reducing abandonment rates. The benefits of leveraging permissioned verification include:  Convenience and speed: By granting permissioned access, customers avoid the hassle of uploading or submitting documents manually. This saves time and effort, resulting in a faster verification process.  Increased coverage and reduced abandonment: Permissioned verification ensures a higher coverage rate by minimizing the potential for customer abandonment during the application process. Since the information is retrieved seamlessly, customers are more likely to complete the application without frustration.  Privacy and control: Customers retain control over their data by explicitly granting permission for access. This enhances transparency and empowers individuals to manage their financial information securely.  Creating a verification "waterfall" for optimal results  To harness the combined power of instant and permissioned verification, lenders can establish a verification "waterfall" approach. This approach involves a cascading verification process where instant verification is the first step, followed by permissioned verification if instant verification is not available or unable to verify the customer.                                              Example of Experian Verify’s automated verification waterfall.  There are numerous advantages to adopting a “waterfall” approach, including:   Cost efficiency: Lenders who prioritize instant verification save on operational costs associated with manual verification processes. The seamless transition to permissioned verification reduces the need for manual intervention, minimizing expenses and improving efficiency.  Improved verification success rate: A verification waterfall ensures that alternative verification methods are readily available if the initial instant verification is unsuccessful. This increases the overall success rate of verifying customer data and reduces the likelihood of losing potential borrowers.  Enhanced customer experience: The combination of instant and permissioned verification creates a streamlined and frictionless customer experience. Customers can progress seamlessly through the verification process, reducing frustration and increasing satisfaction levels.  Propelling your business forward  In the dynamic landscape of lending, a combination of instant and permissioned verification technologies provides significant value to lenders and customers. While instant verification delivers unparalleled efficiency and experience, incorporating permissioned verification ensures expanded coverage, reduced abandonment rates, and a seamless digital journey for customers. By implementing a verification "waterfall" approach, lenders can optimize verification processes, reduce costs, improve efficiency, and ultimately deliver an exceptional customer experience.  Learn more about our solutions The advantages of instant and permissioned verification *This article leverages/includes content created by an AI language model and is intended to provide general information.

Are you looking for ways to make your financial institution more secure without adding unnecessary friction to the customer experience? Automated identity verification is an essential part of this process, safeguarding sensitive consumer information and helping to prevent fraud. This blog post will serve as the ultimate guide to automated identity verification so that you can understand why it's important and how it works. We'll cover all the details, like what automated ID verification is, how authentication software works with identifying documents, why automated identification technology is preferred over manual processes, and tips on implementing automation identity verification solutions into your business practices.   What is automated identity verification?  Automated identity verification is a secure, efficient process for verifying the identity of individuals or entities. This process is integral in various industries, especially the financial sector, to curb identity theft and fraudulent activities. It operates by using advanced analytics and authentication software that cross-references the provided data with a set of stored information. This technology eliminates  manual ID verification, saving time and improving accuracy. ID verification automation uses artificial intelligence and machine learning to compare identifying credentials against various authenticating sources.   Automated identity verification also comes into play for employment and income verification. Experian VerifyTM enables businesses through precise, real-time employment and income verification, ultimately helping businesses reduce risk, accelerate conversion and remove friction.   For a more comprehensive understanding of automated identity verification, you can visit Experian's Identity Verification Solutions webpage, which provides a deep dive into the intricacies of identity verification, including insights on its importance in modern business operations and how it keeps your business secure.  Benefits of automated identity verification for businesses and consumers   Automated ID verification has revolutionized the way businesses conduct their operations and interact with customers. For businesses, AIV offers a range of benefits such as:  Improved efficiency – businesses can automate the time-consuming process of identity verification, freeing up resources (staff) to focus on other critical tasks.  Enhanced security – the technology ensures that customer data is secure and accurate, minimizing fraud risks and/or data breaches.  Reduced costs – with the process being faster and more secure, costs are reduced as a byproduct.  On the other hand, consumers enjoy a hassle-free experience as they can verify their identity within seconds, without  physical documentation. This is essential for today’s consumers who expect frictionless experiences that keep them and their information safe.   Data from Experian’s annual U.S. Identity and Fraud Report reflects these sentiments: 37% of consumers moved a new account opening process to another organization because of a poor experience; 95% of consumers say it's important to be repeatedly recognized online by businesses; and 60% of consumers are concerned about their online privacy. With automated identity verification, businesses can build trust, streamline their processes, and ultimately improve their bottom line.  Furthermore, automated identity verification is a necessary component for businesses to minimize fraud risks in our evolving digital landscape. Living in an era where cybercrime is rampant, AIV safeguards businesses from potential fraudulent attempts and data breaches that could cause significant financial and reputational damage.   From a compliance standpoint, automated identity verification ensures regulatory compliance, which is critical, considering the stringent regulations regarding customer data protection. Non-compliance can lead to severe legal repercussions and financial penalties. For financial institutions, Know Your Customer (KYC) policies must include Customer Identification Programs. Experian can help across the entire customer journey, from onboarding through portfolio management, while reducing risk of non-compliance and providing seamless authentication.  Common challenges of automated identity verification   As more companies turn to artificial intelligence and automation to deliver superior customer service experiences, the challenges businesses face have multiplied. One of the most common issues is ensuring identity proofing and accurate information protection within their networks. Although account takeover prevention has become more advanced, fraudsters still use increasingly sophisticated methods to circumvent it. As such, businesses must continuously develop new strategies to overcome these challenges, ensuring that their AI-powered solutions continue to provide reliable and secure user experiences.  Types of identity verification solutions   As the digital world continues to evolve, automated identity verification solutions have become a crucial part of online interactions. These solutions not only enhance security measures, but also provide faster and more efficient ways of identifying individuals.   For instance, facial recognition is one example. Experian’s CrossCore® Doc Capture enables confident identity verification via facial recognition, which scans a person's face and compares it to their identification documents. Another type is voice recognition, which uses speech patterns to verify an identity. Additionally, document verification scans and validates various identification documents, such as driver's licenses and passports. It's essential to choose the most suitable AIV solution for your organization to ensure robust and reliable security measures.  How to implement an automated ID verification solution   It’s not new news that identity theft and fraud continue to be major concerns, particularly in an increasingly digital-only world.  Implementing automated identity verification solutions to safeguard against such threats can seem daunting, particularly for businesses with limited IT resources. However, the benefits of automated ID verification, such as increased accuracy and efficiency, make it a worthwhile investment. When choosing a solution,  consider factors such as the level of security provided, ease of implementation and integration with existing systems, and the ability to customize rules and settings. With careful planning and the right solution, , organizations can take a significant step towards improving their security posture and protecting their customers.  Best Practices for automated identity verification  Automated identity verification presents one way that financial institutions can increase automation. In doing so, organizations can improve accuracy, speed, and security in the verification process. One technique that has proven effective is the use of biometric technology, such as facial recognition and fingerprint scanning, to verify a person's identity. Additionally, utilizing various data sources, such as credit bureaus like Experian and government agencies, can increase the accuracy of verification. Implementing these best practices can not only save time and resources but also enhance customer experience by providing a seamless and secure verification process.  In summary, automated identity verification is a vital tool for businesses and consumers to enhance their safety and security when engaging with customers. Automated identity verification streamlines customer processes across the lifecycle by eliminating manual checks and lengthy delays. As technology continues to evolve, it’s important for organizations to remain mindful that the methodologies used within automated identity verification will rapidly change as well. The key is to stay ahead. Automated identity verification solutions offer many advantages for businesses who want to maintain their trustworthiness while staying competitive in an ever-changing market.  To learn more about Experian’s automated identity verification solutions, visit our website.    Learn More *This article includes content created by an AI language model and is intended to provide general information. 

Changes in your portfolio are a constant. To accelerate growth while proactively identifying risk, you’ll need a well-informed portfolio risk management strategy. What is portfolio risk management? Portfolio risk management is the process of identifying, assessing, and mitigating risks within a portfolio. It involves implementing strategies that allow lenders to make more informed decisions, such as whether to offer additional credit products to customers or identify credit problems before they impact their bottom line. Leveraging the right portfolio risk management solution Traditional approaches to portfolio risk management may lack a comprehensive view of customers. To effectively mitigate risk and maximize revenue within your portfolio, you’ll need a portfolio risk management tool that uses expanded customer data, advanced analytics, and modeling. Expanded data. Differentiated data sources include marketing data, traditional credit and trended data, alternative financial services data, and more. With robust consumer data fueling your portfolio risk management solution, you can gain valuable insights into your customers and make smarter decisions. Advanced analytics. Advanced analytics can analyze large volumes of data to unlock greater insights, resulting in increased predictiveness and operational efficiency. Model development. Portfolio risk modeling methodologies forecast future customer behavior, enabling you to better predict risk and gain greater precision in your decisions. Benefits of portfolio risk management Managing portfolio risk is crucial for any organization. With an advanced portfolio risk management solution, you can: Minimize losses. By monitoring accounts for negative performance, you can identify risks before they occur, resulting in minimized losses. Identify growth opportunities. With comprehensive consumer data, you can connect with customers who have untapped potential to drive cross-sell and upsell opportunities. Enhance collection efforts. For debt portfolios, having the right portfolio risk management tool can help you quickly and accurately evaluate collections recovery. Maximize your portfolio potential Experian offers portfolio risk analytics and portfolio risk management tools that can help you mitigate risk and maximize revenue with your portfolio. Get started today. Learn more