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.

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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. 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 an 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 say history repeats itself, they don't mean it repeats exactly. That's especially true in the face of novel challenges, such as 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 adjust them 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. Best practices in model validation Regulatory compliance: With regulations continually evolving, maintaining compliance with risk models can be challenging. Different jurisdictions may have varying requirements, further complicating validation processes.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. 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

Automated debt collection can help you save time and money while increasing customer satisfaction and long-term values.

Explore what multi-factor authentication is, how it works, and why it’s crucial for enhancing your digital security against data breaches.

  Managing digital identities is a necessity, responsibility and privilege. When done right, digital identity management solutions can help consumers feel recognized and safe. In turn, companies can build strong and personalized relationships with their customers while complying with regulatory requirements and combating hydra-like fraud attacks. What is digital identity? The concept and definition of a digital identity have expanded as everyday interactions increasingly happen in digital realms. Today, a digital identity is more than an online account. Identities can be created and depend on all the digital information associated with a unique entity, which may be a person, business or device. A person's digital identity often includes online and offline attributes that fall into one of three categories: Something a user knows, such as a username, password or PIN. Something a user has, such as a mobile phone or security token. Something that's part of the user, such as a fingerprint, iris, voice pattern, behavior or preferences. People are increasingly open to sharing this type of personal information if it serves a purpose. Our Global Identity and Fraud Report found that 57 percent of consumers are willing to share data if it ensures greater security or prevents fraud, and 63 percent of consumers think sharing data is beneficial (up from 51 percent in 2021).1 People can also use these identifiers to verify their identity at a later point. But digital identity verification tools should rely on more than user-provided verification alone. A person may have hundreds or thousands of digital interactions every day, and these can leave digital footprints that you can use to create or expand digital identities. These types of identifiers — such as search queries, geotags, behaviors and device information — can also help you authenticate a user and offer a more customized and seamless experience. However, when focusing on consumers' digital identities, it's important to remember that their identity is more than the sum of data points. A person's digital identity is unique and personal, and it should be managed accordingly. The business side's challenges A discussion of what makes up an identity can quickly turn philosophical. For instance, you can't authenticate identical twins based on a face scan or DNA test, so what is it that makes them unique? In some ways, the example gets to the heart of businesses' challenges today. To create a safe and enjoyable online identity verification experience, you need to be able to distinguish between a real person and an imitator, even when the two look nearly identical. Access to more information can make this easier, but you then need to ensure that you can keep this information secure. It can be a tricky balance, but if you get it right, your efforts will be rewarded. People want to be recognized as they move across channels and devices, and organizations want to be able to quickly and accurately identify users with a friction-right experience that also helps prevent fraud. However, while 84 percent of businesses say recognizing customers is "very" or "extremely" important, only about 33 percent of consumers are confident that they'll be repeatedly recognized online.1 There's a clear gap — and an opportunity to better meet customers' desires. Organizations across industries know they need a customer recognition strategy and 82% already have one in place.2 Some businesses address this challenge with identity platforms that are standardized and interoperable. Standardization allows the platform to gather and store the growing influx of data that it can use as part of a digital identity strategy. Interoperability allows the platform to match different types of data, including physical data, with a person to verify their digital identity and avoid the creation of duplicate identities. In short, the platforms can make sense of increasingly large amounts of internal and external data and easily incorporate new data sources as they become available. Regulatory compliance and digital identity Navigating the regulatory landscape is a significant challenge for organizations dealing with digital identities. Compliance is not only necessary for legal reasons but also critical to maintaining customer trust and safeguarding institutional reputation. Organizations must stay informed about the regulatory frameworks that affect digital identity, such as the General Data Protection Regulation (GDPR) in the European Union, the California Consumer Privacy Act (CCPA), and other pertinent laws in jurisdictions they operate. These regulations dictate how personal data can be collected, stored, used and shared. Staying ahead of regulatory changes: Regulatory landscapes are dynamic, particularly concerning digital data. Organizations should engage with policymakers and participate in industry forums to  stay ahead of changes. By proactively managing compliance, organizations can avoid costly penalties, operational disruptions and reputational damage. The consumer's perspective Some organizations are adopting a consumer-centric approach to digital identity that puts consumers' needs and desires first. These can broadly be broken into four categories: Security: While people want a seamless and personalized experience, security and privacy are listed as top concerns year after year.1 That might not be surprising given that data breaches continually make headlines and there are growing concerns over identity theft. Privacy: Security is related to privacy, but privacy means more than keeping consumers' information safe from hackers. Our April 2022 Global Insight Report found that 90 percent of consumers want some or complete control over how their personal data is used. 3 Recognition: People want to be continually recognized once they share and verify their identity, even if they move between devices or channels. And nearly 70 percent of consumers say it's important for businesses to recognize them across multiple visits.1 Inclusion: Consumers may have varying levels of access to technology, comfort with technology and access to physical identifiers. Creating digital identity solutions for these potential barriers can also increase financial inclusion. While these are all areas of focus, organizations also need to find the right fit for each person and interaction. For instance, consumers may expect and even appreciate a robust verification process when they're opening a new financial account. But they could quickly be turned off by a similar process if they're making a small purchase or trying to play a new online game. What to look for in a digital identity partner Digital identity solutions and services have grown increasingly sophisticated to meet today's challenges. Identity hubs and data orchestration engines can connect with multiple services to help create, resolve, verify and authenticate identities. By moving away from a siloed approach, businesses can offer customers a better experience while minimizing their risk throughout the customer journey. When comparing potential partners, look for a company that: Has a customer-first approach: If your business is customer-first, then you need a partner who has a similar view. Uses multidimensional data: The partner should be able to offer and use offline and digital data sources to resolve, verify and authenticate digital identities. Its capabilities may become increasingly important as new data sources emerge. Isn't afraid to innovate: Look into how the partner is testing and using the latest advancements, such as artificial intelligence, in its digital identity solutions. Protects your brand: Understand how the partner helps detect and prevent fraud while creating a seamless experience for your customers and protecting their data. The right partner can increase your bottom line, help you build trust and improve your brand's reputation. Learn more about Experian Identity, an integrated approach to digital identity that builds on Experian's decades of experience managing and securing identifying information. Learn more 1“2022 Global Identity and Fraud Report: Building digital consumer trust amidst rising fraud activity and concerns," Experian, June 2022 2“2021 Global Identity and Fraud Report: Protecting and enabling customer engagements in the new digital era," Experian, April 2021. https://www.experian.com/content/dam/marketing/na/global-da/pdfs/GIDFR_2022.pdf https://www.experian.co.th/wp-content/uploads/2021/04/Experian-Global-Identity-Fraud-Report-2021.pdf 3"Global Insights Report: April 2022," Experian, April 2022. https://www.experian.com/blogs/global-insights/wp-content/uploads/2022/04/WaveReportApril2022.pdf *This article includes content created by an AI language model and is intended to provide general information.

Third-party fraud involves an identifiable victim that is willing to collaborate in the investigation and resolution.

Account takeover fraud can be costly, but is preventable with the right account takeover fraud prevention solution.

For companies that regularly engage in financial transactions, having a customer identification program (CIP) is mandatory to comply with the regulations around identity verification requirements across the customer lifecycle. In this blog post, we will delve into the essentials of a customer identification program, what it entails, and why it is important for businesses to implement one. What is a customer identification program? A CIP is a set of procedures implemented by financial institutions to verify the identity of their customers. The purpose of a CIP is to be a part of a financial institution’s fraud management solutions, with similar goals as to detect and prevent fraud like money laundering, identity theft, and other fraudulent activities. The program enables financial institutions to assess the risk level associated with a particular customer and determine whether their business dealings are legitimate. An effective CIP program should check the following boxes: Confidently verify customer identities Seamless authentication Understand and anticipate customer activities Where does Know Your Customer (KYC) fit in? KYC policies must include a robust CIP across the customer lifecycle from initial onboarding through portfolio management. KYC solutions encompass the financial institution’s customer identification program, customer due diligence and ongoing monitoring. What are the requirements for a CIP? Customer identification program requirements vary depending on the type of financial institution, the type of account opened, and other factors. However, the essential components of a CIP include verifying the customer's identity using government-issued identification, obtaining and verifying the customer's address, and checking the customer against a list of known criminals, terrorists, or suspicious individuals. These measures  help detect and prevent financial crimes. Why is a CIP important for businesses? CIP helps businesses mitigate risk by ensuring they have accurate and up-to-date information about their customers. This also helps financial institutions comply with laws and regulations that require them to monitor financial transactions for any suspicious activities. By having a robust CIP in place, businesses can establish trust and rapport with their customers. According to Experian’s 2024 U.S. Identity and Fraud Report, 63% of consumers say it's extremely or very important for businesses to recognize them online. Having an effective CIP in place is part of financial institutions showing their consumers that they have their best interests top of mind. Finding the right partner It’s important to find a partner you trust when working to establish processes and procedures for verifying customer identity, address, and other relevant information. Companies can also utilize specialized software that can help streamline the CIP process and ensure that it is being carried out accurately and consistently. Experian’s proprietary and partner data sources and flexible monitoring and segmentation tools allow you to resolve CIP discrepancies and fraud risk in a single step, all while keeping pace with emerging fraud threats with effective customer identification software. Putting consumers first is paramount. The security of their identity is priority one, but financial institutions must pay equal attention to their consumers’ preferences and experiences. It is not just enough to verify customer identities. Leading financial institutions will automate customer identification to reduce manual intervention and verify with a reasonable belief that the identity is valid and eligible to use the services you provide. Seamless experiences with the right amount of friction (I.e., multi-factor authentication) should also be pursued to preserve the quality of the customer experience. Putting it all together As cybersecurity threats are becoming more sophisticated, it is essential for financial institutions to protect their customerinformation and level up their fraud prevention solutions. Implementing a customer identification program is an essential component in achieving that objective. A robust CIP helps organizations detect, prevent, and deter fraudulent activities while ensuring compliance with regulatory requirements. While implementing a CIP can be complex, having a solid plan and establishing clear guidelines is the best way for companies to safeguard customer information and maintain their reputation. CIPs are an integral part of financial institutions security infrastructures and must be a business priority. By ensuring that they have accurate and up-to-date data on their customers, they can mitigate risk, establish trust, and comply with regulatory requirements. A sound CIP program can help financial institutions detect and prevent financial crimes and cyber threats while ensuring that legitimate business transactions are not disrupted, therefore safeguarding their customers' information and protecting their own reputation. Learn more

Fraud analytics can help your business keep up with sophisticated fraud attempts and provide expert security.

Using financial management services to drive user engagement can be an effective strategy to win the race for consumer attention.

Experian Automotive has updated our Electric Vehicles 2022 Year-in-Review Infographic Report with new 2023 Half-Year insights. In the previous report, we shared that over 6% of new, retail registrations were for electric vehicles.  As we evaluated the current state of the Electric Vehicle Market for the first half of 2023 (January-June registrations), the percentage of new, retail registrations for electric vehicles has increased to over 7.5%. There are several factors driving consumer adoption of electric vehicles in the United States, including: Environmental concerns: Consumers are increasingly concerned about the environmental impact of transportation, and EVs produce zero emissions at the tailpipe. Government incentives: Many state and federal governments offer incentives for the purchase of electric vehicles, such as tax credits and rebates. Falling battery costs: The cost of lithium-ion batteries, the key component of EVs, has fallen in recent years, making EVs more affordable for consumers. Increasing availability of EV models: Automakers are releasing a growing number of EV models, giving consumers more choices to fit their needs and budgets. Despite the progress that has been made, there are still some challenges that need to be addressed to accelerate EV adoption in the United States. These challenges include: Lack of charging infrastructure: There is a need for more public charging stations, especially in rural areas and along major highways. High upfront cost: EVs can still be more expensive to purchase than gasoline-powered vehicles, even after factoring in government incentives. Range anxiety: Some consumers are concerned about the range of EVs, which can be limited compared to gasoline-powered vehicles. Despite the challenges, the future of electric vehicles in the United States is bright. Automakers are investing heavily in EV development, and the number of EV models available to consumers is expected to continue to grow. Additionally, state and federal governments are taking steps to support EV adoption, such as investing in charging infrastructure and offering incentives for consumers and businesses to purchase If you’d like to learn more about the current state of the Electric Vehicle market and buyer and how that market is growing and changing, check out our Updated Electric Vehicles Year in Review Infographic.

First-party fraud can be detected and prevented by using robust fraud risk management strategies and solutions.

Providing identity protection services alongside your existing offerings can help your business attract more new customers.

With the rise of digital services, the telecom industry faces the need to mitigate fraud while streamlining the onboarding process.