This article was updated on January 30, 2024. Income verification is a critical step in determining a consumer’s ability to pay. The challenge is verifying income in a way that’s seamless for both lenders and consumers. While many businesses have already implemented automated solutions to streamline operations, some are still relying on manual processes built on older technology. Let’s take a closer look at the drawbacks of traditional verification processes and how Experian can help businesses deliver frictionless verification experiences. The drawbacks of traditional income verification Employment and income verification provides lenders with greater visibility into consumers’ financial stability. But it often results in high-touch, high-friction experiences when done manually. This can be frustrating for both lenders and potential borrowers: For lenders: Manual verification processes are extremely tedious and time-consuming for lenders as it requires physically collecting and reviewing documents. Additionally, without reliable income data, it can be difficult for lenders to accurately determine a consumer’s ability to pay, leading to higher origination risk. For borrowers: Today’s consumers have grown accustomed to digital experiences that are fast, simple, and convenient. A verification process that is slow and manual may cause consumers to drop off altogether. How can this process be optimized? To accelerate the verification process and gain a more complete view of consumers’ financial stability, lenders must look to automated solutions. With automated income verification, lenders obtain timely income reports to accurately verify consumers’ income in minutes rather than days or weeks. Not only does this allow lenders to approve more applicants quickly, but it also enables them to devote more time and resources toward improving their strategies and enhancing the customer experience. The right verification solution can also capture a wider variety of income scenarios. With the click of a button, consumers can give lenders permission to access their financial accounts, including checking, savings, 401k, and brokerage accounts. This creates a frictionless verification experience for consumers as their income information is quickly extracted and reviewed. Retrieving data directly from financial accounts also provides lenders with a fuller financial picture of consumers, including those with thin or no credit files. This helps increase the chances of approval for underserved communities and allows lenders to expand their customer base without taking on additional risk.1 Learn more 1 Experian Income Verification Product Sheet (2017).

In the fast-paced world of cybersecurity, the ability to anticipate and adapt to emerging threats is not just a competitive advantage—it’s a business imperative. As we release our 11th annual “Experian 2024 Data Breach Industry Forecast,” we invite you to embark on a journey into the future of data breaches, a journey that promises to empower data breach professionals, cyber experts, and industry leaders alike. A Glimpse into Tomorrow’s Threat Landscape Our team of experts has meticulously examined the current cybersecurity landscape to identify the trends that will shape the industry in the coming year. The “Experian 2024 Data Breach Industry Forecast” provides a roadmap for staying ahead of these challenges, arming you with the insights needed to fortify your organization’s defenses. Six Pivotal Predictions: Decoding the Future Within the report, we unveil six pivotal predictions that promise to redefine the landscape of data breaches. While we can’t reveal all the details here, we’ll offer a sneak peek to whet your appetite: Six Degrees of Separation: There’s no question that third-party data breaches this year made headlines. Delve into the intricacies of supply chain security and discover why addressing vulnerabilities in the supply chain is the next frontier in cybersecurity. Little by Little Becomes A Lot: When trying to achieve a goal, it’s said that taking small steps can lead to big results. See how hackers could apply that same rule.  Not a Third Wheel: It’s widely known who the main players are globally that sponsor attacks and a new country in South Asia may join the international stage. No, not Mother Earth! Plutonium, terbium, silicon wafers — these rare earth materials present an intriguing opportunity for hackers looking to disrupt an enemy’s economy. The Scarface Effect: Like drug cartels, cybergangs are forming sophisticated organizations. Winning from the Inside: In 2024, we may see enterprising threat actors target more publicly traded companies, leveraging data extraction and their talents in plain sight as everyday investors. This is just a glimpse into the dynamic and evolving landscape detailed in our full report. Download the complete “Experian 2024 Data Breach Industry Forecast” to explore these predictions in-depth and stay ahead of the curve. Expert Analysis: Navigating Complexity with Confidence Backed by extensive research and the expertise of our seasoned analysts, the report provides more than just predictions; it offers a deep dive into the complexities of the modern cybersecurity landscape. Our experts share their insights on how these predictions will impact organizations and individuals, providing actionable intelligence that goes beyond the theoretical. Whether you’re a CISO, a Compliance Officer, or a Cyber Risk Insurer, the “Experian 2024 Data Breach Industry Forecast” equips you to navigate the challenges of tomorrow with confidence. Empowering You to Lead in Data Breach Response As you read through the report, you’ll find that our approach goes beyond merely highlighting problems; we provide solutions. Each prediction is accompanied by practical recommendations and best practices, ensuring that you not only understand the evolving landscape but also possess the tools to proactively address the challenges that lie ahead.Now, more than ever, it’s crucial to be proactive in your approach to cybersecurity. Download the full “Experian 2024 Data Breach Industry Forecast” to unlock the insights and strategies that will set you apart in the realm of data breach response. Your journey into the future starts here. The Future is Now. Are you ready to take the first step toward a more secure tomorrow? Download the report now and lead the way in data breach response. Read more

This series will dive into our monthly State of the Economy report, providing a snapshot of the top monthly economic and credit data for those in financial services to proactively shape their business strategies.  As 2024 unfolds, the economy is beginning to shift from last year’s trends. Instead of focusing on rate hikes, we’re looking at the potential for rate cuts. Our labor market is beginning to ease, and inflation is moving closer to the Federal Reserve’s 2% mark. Each month’s data gives us a clearer picture of our economic trajectory and the Federal Reserve’s (Fed) policy moving forward, as well as new and direct implications on credit metrics. Data highlights from this month’s report include: The U.S. economy added 216,000 jobs in December, but after November and October levels were revised, three-month average job creation now sits below the pre-pandemic level. While there was no change in November, annual core inflation, which excludes the volatile food and energy components, cooled in December from 4.0% to 3.9%. Consumer sentiment rose 14% in December, reversing the past four monthly declines, driven by increased optimism toward the trajectory of inflation. Check out our report for a deep dive into the rest of this month’s data, including student loans, consumer spending, the housing market, and delinquencies. To have a holistic view of our current environment, we must understand our economic past, present, and future. Keep an eye out for this year’s chartbook for a comprehensive view of the past year and download our latest forecast for a view of what’s to come. Download report View forecast For more economic trends and market insights, visit Experian Edge.

This article was updated on January 26, 2024. Marketers are facing new challenges as third-party cookies crumble, and people use more devices throughout the day. Someone might comparison shop on their laptop in the morning, do more research on a tablet in the afternoon and finally decide to make a purchase on their phone before falling asleep at night. Being able to track these movements and insert yourself where appropriate can be difficult, but it's not impossible. One solution that's becoming increasingly attractive is creating a unified identity for each customer — and matching every piece of data and touchpoint to the single profile. For this to work, you need identity resolution. What is identity resolution? Identity resolution is the ongoing process of linking various identifying elements to create and expand a unique identity. The multi-step process can include: Securely onboarding data into a system Hashing or tokenizing personal information to improve security and privacy Setting aside information that can't be matched to an identity yet Matching or linking identifiers to a known unique identity Verifying that the identities and identifiers are accurate An identity graph (ID graph) is an essential part of identity resolution. It's the proprietary database that can pull in and store data from different sources and link them to a unique identifier — also known as a persistent identification number. Depending on the system and purpose, identity resolution may focus on creating a single identity for a person, household, or business. The information can come from internal sources, including a customer relationship management (CRM) tool, email marketing platforms, event management platforms, social media accounts, point-of-sales systems, and other digital and offline touchpoints. Additionally, third-party data sources, such as credit or demographic data, can contribute to building a more complete identity. And second-party data — information that's shared between brands or companies — can also be helpful. As new digital and offline information is created or found, it's linked to the existing persistent identification number in the ID graph. The process can happen in different ways. The resolution system could accurately match an engagement to a person with deterministic data, such as a hashed email address, assuming they logged in. If the person didn't log in, a probabilistic model may be able to accurately attribute the session to the person's identity based on indicators that it's likely the same person, such as a device ID or behavioral data. A hybrid approach combines deterministic and probabilistic approaches, which could be important for scaling. The goal and end result is often called a holistic, single-unified, or 360-degree view of a customer. READ MORE: Making identities personal Why does identity resolution matter? Identity resolution lets you know with whom you're connecting, which can be important throughout a customer's lifecycle. From marketing to collections, you want to be able to engage the right person on the right channel with the right offer. And that's only possible when you can accurately identify people. Consistent and accurate identity resolution is difficult, though. Experian's 2023 Identity and Fraud Report found that 92% of businesses have a strategy in place for identifying consumers online. But 63% of consumers are either "somewhat confident" or "not very confident" that businesses can accurately recognize them online. What are the benefits of identity resolution? It's a worthy goal to push toward, because you can use identity resolution solutions to: Consolidate your view of customers Companies may have multiple profiles of the same customer — one from an email list, another from their loyalty program and a third from an outdated system. Your customers are also interacting with you in different ways, perhaps logging into an account from their laptop in the morning while visiting your site from a phone at night. Identity resolution lets you connect all these elements to create a single profile. Build targeted and measurable marketing campaigns Once you have a single and consistent view of your customers, you can more accurately segment and target your marketing campaigns. Personalizing messages can increase engagement and effectiveness. And, equally important, knowing to who you don't want to send messages can help you avoid wasting marketing spending. Some identity resolution services can also help you track anonymous visitors and customize your marketing with look-alike models, which can identify people who are likely part of your target audience. You'll also be able to more accurately measure the effectiveness of a campaign. With a single customer view, it's easier to know if and how a targeted social media ad, television spot and emailed coupon worked together to create a sale. Increase customer experiences across brands When implemented throughout an organization, you can also use the single view of a customer to create a consistent experience across brands and business units. Each can benefit from a more holistic understanding of the customer and can contribute to building out customers' profiles.  Seamlessly confirm identities Identity resolution can also create a more frictionless experience for customers who want to create or log into your site, and it can help with detecting fraud and high-risk consumers. But keep data security top of mind. Consumers rank privacy (79%) and security (78%) much higher than login convenience (38%) when considering their online experience. What does an identity resolution solution look like? The need for and type of identity resolution can vary depending on a business' challenges and goals. For instance, large retailers often have a lot of first-party data — so much that it may be overwhelming. For them, an identity resolution solution that can organize internal data while enhancing it with external data points could be a priority. In contrast, a business with infrequent touchpoints might not have as much first-party data and could benefit from a solution that offers as much external information as possible. Some organizations are building their own internal identity resolution services to address these challenges, but many are looking to outside partners for identity resolution. When comparing partners, consider: Flexibility and scalability: Understand which data the solution can onboard and how quickly it can onboard data. Consider whether you'll want to be able to use real-time APIs or batch processing, and the limitations on how much data the provider can process at a time. Additionally, consider whether the ID graph will use persistent IDs that can change as you scale. Matching and analysis: Ask about the solution's approach and success with matching online and offline data and the options to integrate or append second and third-party data. If you want to be able to securely and privately share anonymized identities internally or with partners, make sure that's an option as well. Integration: Research whether the provider can easily integrate your existing services and vendors. Privacy: 73% of consumers say it's a business's responsibility to protect them online. Ask about the provider's experience and approach to storing and anonymizing data. Some solutions also have built-in activation tools. These let you build and launch omni-channel campaigns. They also analyze and report on how well your campaigns are performing. Get started today To learn more about the importance of digital identity and Experian's identity solutions, visit us today. Learn more

In today’s complex business landscape, data-based decision-making has become the norm, with advanced technologies and analytics tools facilitating faster and more accurate modeling and predictions. However, with the increased reliance on models, the risk of errors has also increased, making it crucial for organizations to have a comprehensive model risk management framework. In this blog post, we will dive deeper into model risk management, its importance for organizations, and the key elements of a model risk management framework. What is model risk? First, let's define what we mean by model risk. Many institutions use models to forecast and predict the future performance of investments, portfolios or consumers' creditworthiness. Model risk can happen when the results produced by these models are inaccurate or not fit for the intended purpose. This risk arises due to several factors, like data limitations, model assumptions and inherent complexities in the underlying modeled processes. For example, in the credit industry, an inaccurately calibrated credit risk model may incorrectly assess a borrower's default risk, resulting in erroneous credit decisions and impacting overall portfolio performance. What is a risk management model and why is it important? A risk management model, or model risk management, refers to a systematic approach to manage the potential risks associated with the use of models and, more specifically, quantitative models built on data. Since models are based on a wide range of assumptions and predictions, it's essential to recognize the possibility of errors and acknowledge its impact on business decisions. The goal of model risk management is to provide a well-defined and structured approach to identifying, assessing, and mitigating risks associated with model use. The importance of model risk management for institutions that leverage quantitative risk models in their decisioning strategies cannot be overstated. Without proper risk management models, businesses are vulnerable to significant consequences, such as financial losses, regulatory enforcement actions and reputational damage. Model risk management: essential elements The foundation of model risk management includes standards and processes for model development, validation, implementation and ongoing monitoring. This includes: Policies and procedures that provide a clear framework for model use and the associated risks. Model inventory and management that captures all models used in an organization. Model development and implementation that documents the policies for developing and implementing models, defining critical steps and role descriptions. Validation and ongoing monitoring to ensure the models meet their stated objectives and to detect drift. In addition to these essential elements, a model risk management framework must integrate an ongoing system of transparency and communication to ensure that each stakeholder in model risk governance is aware of the policies, processes and decisions that support model use. Active engagement with modelers, validators, business stakeholders, and audit functions, among other stakeholders, is essential and should be included in the process. How we can help Experian® provides solutions and risk mitigation tools to help organizations of all sizes establish a solid model risk management framework to meet regulatory and model risk governance requirements, improve overall model performance and identify and mitigate potential risk. We provide services for back testing, benchmarking, sensitivity analysis and stress testing. In addition, our experts can review your organization’s current model risk management practices, conduct a gap analysis and assist with audit preparations. Learn more *This article includes content created by an AI language model and is intended to provide general information.

Online identity verification has become a basic necessity for everyday life. Consumers today might expect to upload a picture of their driver's license or answer security questions before creating a new account. And it's crucial to them — 63% say it's extremely or very important for businesses to be able to recognize them online. While many organizations have a consumer recognition strategy, moving from strategy to action and then getting the desired result isn't easy. That's particularly true when you're working to create seamless experiences for customers while fighting increasingly sophisticated fraudsters. Why is online identity verification challenging? Identity verification in the physical world might be as simple as checking a government-issued ID card — and perhaps an additional form of identification (or two) when the stakes are higher. Verification becomes more complicated as you move into the digital realm, especially when you need to automate decisions. There are many specific challenges to overcome, but some of the main ones fall into four categories. Finding the right friction: In an ideal world, every legitimate user will flow through your verification checks with ease. In reality, you may need to introduce some roadblocks to comply with know your customer (KYC) rules and prevent fraud. Finding the friction-right balance can be tricky. Accessing and using data: Using expanded data sources, such as behavior and device info, can improve outcomes without adding friction. But simply having more data isn’t the goal. You need to be able to organize, process and use the data in a compliant manner to quickly and accurately verify identities. Fighting fraud: You’re up against formidable foes who consistently test your systems for weaknesses and share the results with other fraudsters. You have to be able to spot first-party fraud, identity thieves and synthetic identities. Securing the data: Accessing and storing customer data is vital for a successful identity verification system, but it’s your responsibility to securely protect customers’ data. It also may be a legal requirement, and you need to be mindful of all the applicable regulations. These aren't fixed challenges that you can overcome in a single hurdle. Consumer preferences, fraud tactics and regulations are continually evolving, and your identity verification platform needs to keep up. Potential benefits throughout the customer lifecycle Companies that want to create, manage and continuously identify consumers are starting to take an enterprise-wide approach that relies on creating a single-customer view. The idea is to have a single identity that you can expand as you learn more about a person’s preferences and behavior. Otherwise, business units can wind up with fragmented views that lead to jumbled messaging, errors and missed opportunities. While it can be difficult to implement well, the single-view approach can also be powerful in action: Targeting and onboarding: Marketing, acquisition and onboarding aren’t necessarily handled by the same teams, but a smooth process can create a lasting good impression. There are also recent developments that can provide pre-fill capabilities with their identification verification solutions, which can create a nearly friction-free onboarding process. Prevent fraud: The single-view approach also lets you leverage cross-device and real-time data to detect and prevent fraud, and determine the right-size verification method. Using identity graphs to verify identities in real-time can also help you detect fraud, including account takeovers and first-party fraud. Customer experience: Consistently identifying customers can improve their experience — particularly when different departments can easily access and update the same identification material. In turn, this can lead to brand loyalty and the potential to upsell and cross-sell customers. The need for accurate verification is growing as people spend more time living and shopping online. Only 16% of consumers are confident businesses can consistently recognize them online, which also means there’s an opportunity to surprise and delight the skeptics. What do consumers want? Most people want to be recognized as they move throughout their digital lives. But data breaches and identity theft continuously make headlines, and people aren't ignorant of the dangers of sharing their personal information. In fact, consumers ranked identity theft (80%) as their top online security concern, a sizable +20% jump from the previous year. Finding the right balance of privacy, security and due diligence is important for earning customers' trust. However, the best approach to online identity verification may depend on who your customers are and how they interact with your products and services. Finding a great online identity verification partner Knowing how important online identity verification can be for the success of your business, you need to be sure that the digital identity solutions providers you partner with can meet your current and future needs. A good fit can: Give you access to multidimensional data: You can use online and offline data to support your digital identity verification systems. Some vendors can also help you use internal data,deterministic dataand outputs from probabilistic models to improve your results. Scale to meet future challenges: Many businesses are exploring how to use machine learning and artificial intelligencefor identity resolution and verification. These can be especially powerful when combined with robust data sources and may become more important as additional data sources come online. Protect your business: Identity verification solutions need to help you comply with the regulatory requirements and detect fraud with low false-positive rates to protect your business. First and foremost, you want to work with a partner who knows thatidentity is personal. Your customers are more than data points, and putting their needs and wants first will ultimately help you earn their trust and business. Learn more about Experian’s customer-centric identity verification solutions. Learn more

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

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

Financial institutions, merchants, and e-commerce platforms are no strangers to fraud, especially in the realm of payments. With the rise of digital currency, fraudsters are becoming more inventive, making it increasingly difficult to detect and prevent payment fraud. In this blog post, we discuss payment fraud and ways to protect your organization and your customers. What is payment fraud? Payment fraud occurs when someone uses false or stolen payment information to make a purchase or transaction. The most common types of payment fraud include: Phishing: Through emails or text messages, scammers disguise themselves as trustworthy sources to lure recipients into sharing their personal information, such as account passwords and credit card numbers. Card not present fraud: This type of fraud is one of the most challenging forms of payment fraud to detect and prevent. It occurs when a criminal uses a stolen or compromised credit card to make a purchase online, in-person, or by other means where the card is not physically present at the time of the transaction. Account takeover fraud: This type of fraud occurs when fraudsters gain unauthorized access to an individual’s account and carry out fraudulent transactions. They take over accounts by gathering and using personal or financial details to impersonate their victims. The rise of online payment fraud Online payments have become a prime destination for fraudsters as more consumers choose to store card details and make purchases digitally. As a result, consumers believe that it’s the responsibility of businesses to protect them online. If there’s a lack of trust and safety, consumers will have no problem switching providers, leading to declines in customer loyalty and monetary losses for organizations. No matter the type of payment fraud, it can result in devastating consequences for your organization and your customers. According to Experian’s 2024 U.S. Identity and Fraud Report, fraud scams and bank fraud schemes resulted in more than $458 billion in losses globally. On the consumer side, 52 million Americans had fraudulent charges on their credit or debit cards, with unauthorized purchases exceeding $5 billion. Given these findings, it’s more important than ever to implement robust online payment fraud detection and prevention measures. How can payment fraud be detected and prevented? Approaches to payment fraud detection and prevention have evolved over time. Some of the current and emerging trends include: Additional layers of security: Security measures like two-factor authentication, a CVV code, and a billing zip code can help verify a customer’s identity and make it more difficult for fraudsters to complete a transaction. Enhanced identity verification: A credit card owner verification solution, like Experian LinkTM, matches the customer identity with the credit card being presented for payment, allowing businesses to make better decisions, reduce false declines, and protect legitimate customers. Artificial intelligence (AI) and machine learning: AI-powered models and machine learning algorithms can identify patterns consistent with fraudulent activity in real time, resulting in proactive fraud prevention and reduced financial losses. Behavioral analytics: Using behavioral analytics to monitor user behavior, such as how they navigate a website or interact with the payment process, can help identify inconsistencies and potential fraud. Token-based authentication: Tokenization protects card information by replacing sensitive data with a unique identifier (token), which makes data breaches less damaging. How Experian can help As the payments landscape continues to evolve, so do fraudsters. Experian offers a wide range of payment fraud analytics, account takeover fraud prevention and fraud management solutions that allow you to better detect and prevent payment fraud. Your organization’s reputation and your customers’ trust shouldn’t be compromised. To learn more, visit us today. Learn more This article includes content created by an AI language model and is intended to provide general information.

The threat of data breach is constant in our modern, digital world. And as technology advances, so do the strategies and tactics of malicious actors seeking ways to monetize the vulnerabilities of organizations. It’s not a matter of if, but when, a data breach could impact your organization, and it is important for businesses to understand how to operate in it. What is a Data Breach? For many organizations, a data breach is arguably one of the greatest threats to prevent. What is a data breach? Imagine your organization as a fortress, safeguarding a treasure trove of sensitive information—customer data, financial records, proprietary algorithms. A data breach is the unwelcome intrusion into this fortress, where unauthorized individuals gain access to confidential information, often with malicious intent. This can encompass many types of data, including personal identification information (PII), financial data, and intellectual property. Classifications of breaches can vary from intentional cyberattacks to inadvertent exposure due to system vulnerabilities or human error. To grasp the gravity of data breaches, Businesses face tangible consequences when their defenses are breached, and there are no signs of it slowing down. The frequency and severity of data breaches are alarming. According to recent studies¹, the healthcare sector experienced a 55% increase in data breaches in 2022. No business is immune to the evolving threat landscape especially companies that capture customer data and are also inherently the stewards of this data. Understanding the landscape of data breaches will help you better fortify your business against a breach. In the next sections, we’ll explore the causes, impacts, post-breach response strategies, and preventative tactics businesses can employ to safeguard their data. Causes of Data Breaches Human error Even the most well-intentioned employees can become the weak link in an organization’s security chain. According to the “2023 Verizon Data Breach Investigations Report,” 74% of data breaches involve a human element². Investing in comprehensive training programs is essential to foster a culture of cybersecurity awareness and mitigate the risk of employee-related mistakes. Cybersecurity vulnerabilities The digital landscape is rife with potential vulnerabilities, and cybercriminals are adept at exploiting them. Regular cybersecurity assessments, prompt system updates, and the implementation of robust security protocols are recommended proactive measures to fortify against breaches that capitalize on system vulnerabilities. Insider threats Data breaches can originate from within, whether through disgruntled employees with malicious intent or well-meaning staff who inadvertently compromise security. Gurucul’s “2023 Insider Threat Report” highlights that 60% of organizations experienced insider-related incidents in the past year³. Establishing stringent access controls, closely monitoring user activities, and implementing employee education programs are vital steps to mitigate the risks associated with insider threats. Weak and Stolen Passwords Weak and stolen passwords stand as one of the most common gateways for data breaches. Cybercriminals exploit individuals who use easily guessable passwords or recycle them across multiple platforms. This creates a vulnerability that can be easily exploited through automated attacks. Ensuring robust password policies, employing multi-factor authentication, and regularly updating credentials are necessary measures to thwart these breaches and safeguard sensitive information. Malware The insidious world of malware is a persistent threat to data security. Malicious software, often disguised as innocuous files or links, infiltrates systems, and wreak havoc by compromising data integrity and confidentiality. Malware can then swiftly spread, leading to unauthorized access and data exfiltration. Regularly updating antivirus software, conducting thorough system scans, and educating employees about the dangers of clicking on suspicious links are pivotal defenses against malware-driven breaches. Social Engineering Social engineering has emerged as a cunning and effective tactic in data breaches, such as manipulating individuals to divulge confidential information willingly. Whether through phishing emails, deceptive phone calls, or impersonation, cybercriminals exploit human trust to gain unauthorized access. Raising awareness among employees about the dangers of social engineering, implementing rigorous verification processes, and fostering a culture of skepticism can fortify an organization’s defenses against these subtle yet potent attacks. Physical Attacks While the digital realm often takes center stage, physical attacks on data infrastructure remain a tangible and underestimated risk. Breaches can occur through unauthorized access to servers, theft of physical storage devices, or tampering with network equipment. Implementing stringent access controls, employing surveillance systems, and securing physical infrastructure are crucial steps to mitigate the threat of data breaches stemming from physical incursions. Building digital and physical protective measures can help with your defense against the multifaceted landscape of data breaches. Impacts on Businesses Financial repercussions Data breaches are costly to businesses with immediate and enduring consequences. The “Cost of a Data Breach Report 2023” by IBM reported that the average cost of a data breach was $4.45 million per organization⁴. Long-term financial implications include loss of customers, diminished revenue streams, and increased cybersecurity investments to rebuild trust and fortify defenses against future breaches. Reputational damage The fallout from a data breach extends beyond the balance sheet, leaving an indelible mark on a business’s reputation. According to a 2023 survey by Vercara, 66% of U.S. consumers would not trust a company that falls victim to a data breach with their data. Rebuilding trust with transparent communication, swift remediation, and proactive measures to prevent future breaches is essential, demonstrating a commitment to safeguarding sensitive information. Operational disruptions Data breaches causes disruptions in the operations of daily business activities. It takes an average of 73 days to contain a cyber-attack according to the Cost of a Data Breach Report 2023 from IBM⁴. Swift recovery requires a meticulous balance between addressing the breach’s immediate impact and resuming normal operations to minimize further operational strain. Legal and regulatory implications The legal aftermath of a data breach involves navigating a complex landscape of regulations and compliance standards. In the United States, data breaches may trigger legal consequences under various state laws. For instance, the California Consumer Privacy Act (CCPA) allows for fines ranging from $100 to $750 per consumer per incident⁵. Ensuring adherence to data protection laws, promptly reporting breaches to regulatory authorities, and implementing robust security measures become top priorities in avoiding the legal quagmire that often follows a data breach. Notable data breaches Yahoo! (2014): The personal information of 3 billion people was exposed, including names, birth dates, passwords, and phone numbers. Cause: It is believed that the hack originated through a phishing email sent to a Yahoo! employee. Through this phishing email, it’s believed the hackers were able to access user databases and tools.⁶ Cost: $117.5 million in settlements and $350 million off its sale price to Verizon⁷ Marriott International (2018): Information of approximately 500 million guests was compromised, including names, contact details, passport numbers, and travel details. Cause: A cyber-espionage campaign linked to a state-sponsored actor. Attackers gained access to Marriott’s Starwood guest reservation database due to vulnerabilities in the system.⁸ Cost: Over $100 million for remediation efforts and regulatory fines.⁹ Capital One (2019): 106 million customers’ personal information, including credit card applications and Social Security numbers, was exposed. Cause: A misconfigured web application firewall that allowed a hacker to exploit a server-side request forgery vulnerability, leading to unauthorized access and the theft of sensitive customer data.¹⁰ Cost: Estimated between $100 million and $150 million in 2019 alone.¹¹ SolarWinds (2020): Hackers compromised the software supply chain, affecting numerous government agencies and major corporations globally. Cause: The SolarWinds breach was a sophisticated supply chain attack where malicious actors compromised the software update process, injecting malware into software updates distributed by SolarWinds, allowing them access to numerous government and corporate networks.¹² Cost: At least $18 million¹³ JBS USA (2021): The ransomware attack on the world’s largest meat processor disrupted operations and impacted the company’s IT systems. Cause: A ransomware attack, where cybercriminals exploited vulnerabilities in the company’s IT systems to encrypt data and demand a ransom for its release, causing significant disruptions to operations.¹⁴ Cost: $11 million ransom paid to hackers from JBS to restore their IT systems. Post-breach response Assessment and Damage Control Immediate Action Steps In the event of a data breach, the immediacy of response becomes one factor in determining the outcome. Swift and decisive actions during the initial moments can be instrumental in preventing the situation from escalating. The primary focus at this stage is isolating the affected systems, swiftly disconnecting compromised servers and devices from the network. This can help stop unauthorized access and establishes the foundation for a more concentrated and effective response. Alerting the incident response team, IT personnel, and relevant stakeholders promptly is also worth considering to help gain control over the situation. Forensic Analysis Understanding the who, what, and how of an incident is also an important step following a breach. In this context, involving forensic experts in a meticulous analysis is prudent. These professionals specialize in unraveling the intricacies of the breach, identifying entry points, and tracing the movements of attackers within your systems. The significance of forensic analysis extends beyond mere identification; it serves as the groundwork for prevention. Through a comprehensive study of the employed attack vectors and techniques, organizations can enhance their cybersecurity infrastructure. This process of gathering critical information about the breach contributes to the ability to preempt similar incidents, fostering a more resilient stance against evolving cyber threats. Communication Strategy Internal Communication Effective internal communication plays a pivotal role in building a resilient response framework. In the early stages of a crisis, employees emerge as the initial line of defense. Clearly conveying the severity of the situation provides them with a comprehensive understanding of the impact and the organization’s devised response plan. This also empowers the workforce, fostering a sense of unity within the organization and help the organization navigate challenges ahead cohesively, reinforcing its resilience in the face of adversity. External Communication External communication holds equal importance, reaching beyond the organization to customers, partners, and stakeholders. It’s essential to recognize the significance of constructing messages with transparency, honesty, and a proactive stance. Silence or ambiguity can intensify the repercussions, so prioritizing openness becomes foundational for rebuilding trust. Being timely and forthright in sharing information about the breach and the steps taken to rectify the situation is generally a good strategy when engaging with partners and stakeholders. This approach not only informs but can also mold the perception of the organization’s dedication to security and integrity following the aftermath of a breach with a strategic and forward-thinking mindset. Legal and Regulatory Compliance Notification Requirements Within the regulatory framework, a prompt response is an important post-breach step for organizations. It may first involve comprehensively detailing the legal obligations surrounding breach notifications to both regulatory authorities and affected individuals. It’s essential to recognize the variability in requirements across different regions and industries, underscoring the importance of remaining well-informed about these specific nuances. Timeliness of notifications is also factor for organizations to consider. Numerous jurisdictions impose substantial fines for delays in reporting, making it essential for organizations to adhere to strict timelines. Transparency holds equal weight, necessitating clear communication about the extent of the breach, the nature of compromised information, and the specific measures being implemented to address the situation. This approach can help in being compliant with legal standards and plays a vital role in fostering trust among those directly impacted by the breach. Legal Counsel Engagement Organizations generally seek the support of legal counsel to help navigate the intricate legal aftermath of a data breach. Legal experts can help an organization through potential lawsuits and regulatory fines. Engaging legal experts early allows their insights to guide the overall strategy, shaping everything from the communication plan to the recovery efforts. With early legal counsel support, the organization can be proactive in addressing legal challenges, potentially mitigating the severity of consequences that may arise. Recovery and Remediation IT System Restoration The intricacies of IT system restoration mirror the reconstruction of a fortress following an intrusion. Restoring affected IT systems to normal functionality involves comprehensive measures such as thorough system checks, vulnerability assessments, and the eradication of any residual traces left by a breach. Additionally, organizations generally look to enhance security measures during the recovery phase. Simply reverting to the pre-breach state is not enough; instead, the recovery process serves as an opportunity to accept vulnerabilities in old systems and bolster defenses. This entails updating and patching systems, reassessing access controls, and contemplating the incorporation of advanced threat detection tools. Such measures collectively work to minimize the risk of a recurrence and contribute to an overall fortified cybersecurity posture. Prevention Strategies Best practices for securing sensitive data Securing sensitive data is important in the age of relentless cyber threats. Employing encryption protocols, conducting regular security audits, and limiting access privileges are foundational best practices. These proactive measures help create a robust defense, forming an intricate web that shields critical information from potential breaches. Employee training programs to mitigate human error Human error remains a significant contributor to data breaches. Implementing comprehensive employee training programs can be helpful in cultivating a security-conscious workforce and mitigating human error-caused vulnerabilities. From recognizing phishing attempts to practicing proper password hygiene, a well-informed staff acts as the first line of defense and can significantly reduce the likelihood of unintentional security lapses. Implementing robust cybersecurity measures The cornerstone of any data breach prevention strategy is the implementation of robust cybersecurity measures. This includes advanced intrusion detection systems, firewalls, and regular software updates. Proactively addressing vulnerabilities and staying abreast of the latest cybersecurity advancements help fortify an organization’s digital perimeter, creating an environment that is inherently resistant to malicious infiltrations. Staying abreast of emerging trends Staying ahead of data breach threats requires a keen awareness of emerging trends. From sophisticated phishing techniques to novel forms of malware, businesses should continuously adapt their cybersecurity strategies against evolving tactics employed by cybercriminals. The dynamic nature of the cybersecurity landscape demands constant innovation. Adopting cutting-edge technologies like artificial intelligence for threat detection and investing in predictive analytics allows businesses to stay one step ahead, proactively identifying and neutralizing potential threats before they escalate. Collaboration and information-sharing within industries In the face of evolving cyber threats, collaboration is a powerful defense. Establishing networks for information-sharing within industries enables businesses to benefit from collective intelligence. By sharing best practices and threat intelligence, organizations can collectively strengthen their defenses against the ever-changing data breach landscape. Takeaway Data breaches are a persistent threat for all businesses capturing and storing personal identifiable information. Such businesses are inherently the stewards of this data and must protect that data to avoid bad actors gaining access for malicious intent. Knowing what a data breach is just the first step of protecting that data, and it is key to take action. From securing sensitive data to fostering a cybersecurity-aware workforce, businesses must not merely react to the escalating threat of data breaches but proactively strive to create an impenetrable shield around their valuable information. Visit our website for more information about our offerings and how Experian can help you prepare and respond to data breaches. ¹Hippa Journal, 55% of Healthcare Organizations Suffered a Third-Party Data Breach in the Past Year [2022]²Verizon, 2023 Verizon Data Breach Investigations Report³Gurucul, 2023 Insider Threat Report⁴IBM, Cost of a Data Breach Report 2023⁵Office of the Attorney General, California Consumer Privacy Act (CCPA)⁶CSO, INside the Russian hack of Yahoo: How they did it⁷BPB Online, Yahoo Data Breach: What Actually Happened?⁸CSO, Marriott data breach FAQ: How did it happen and what was the impact?⁹Cybersecurity Dive, Marriott finds financial reprieve in reduced GDPR penalty¹⁰Investopedia, Capital One Data Breach Impacts 106 Million Customers¹¹CNET, Capital One $190 Million Data Breach Settlement: Today Is the Last Day to Claim Money¹²Tech Target, SolarWinds hack explained: Everything you need to know¹³Reuters, SolarWinds says dealing with hack fallout cost at least $18 million¹⁴BBC, Meat giant JBS pays $11m in ransom to resolve cyber-attack

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

In an era where record-breaking home prices and skyrocketing interest rates define the mortgage landscape, borrowers find themselves sidelined by prohibitive costs. With the purchase market at a standstill, mortgage lenders are grappling with how to sustain and grow their businesses. Navigating these turbulent waters requires innovative solutions that address the current market dynamics and pave the way for a more resilient and adaptive future.    Today, I’m sitting down with Ivan Ahmed, Director of Product Management for Experian’s Property Data solutions, to learn more about Experian’s Residential Property Attributes™, a new and exciting dataset that can significantly enhance mortgage marketing and mortgage lead generation strategies and drive business growth for lenders, particularly during these challenging times.    Question 1: Ivan, can you provide a brief overview of Residential Property Attributes and its relevance in today’s mortgage lending landscape?   Answer 1: Absolutely. Residential Property Attributes is our latest product innovation designed to revolutionize how mortgage lenders approach marketing and growth decisions. It’s a robust dataset containing nearly 300 attributes that seamlessly integrates borrower property and tradeline information, providing a more holistic view of a borrower’s financial situation. This powerful dataset empowers lenders to make well-informed, impactful marketing decisions by refining campaign segmentation and targeting. Our attributes group into five categories:  Question 2: As a data-focused company, we frequently discuss the importance of leveraging data and analytics to enhance marketing performance with clients. Considering other data providers that offer property data analytics or credit behavior data, what makes our capabilities distinct?  Answer 2: The defining feature of Residential Property Attributes is its integration with borrower tradeline data. Many lenders today focus primarily on credit behavior, but we consider property data analytics, a critical aspect, equally important. By merging these two components, we present lenders with a thorough and accurate understanding of their target borrowers. This combination is revolutionary for marketing leaders looking to boost campaign performance and return on investment (ROI).  Consider this scenario: On paper, two borrowers may seem homogenous, with similar credit scores, payment histories, and debt-to-income ratios. However, when you incorporate property-level insights, a striking disparity in their overall financial situations emerges. This level of insight prevents possible misdirection in marketing efforts.  Question 3: Could you share more about the practical benefits of Residential Property Attributes, especially regarding enhancing marketing performance?  Answer 3: Residential Property Attributes is instrumental in amplifying performance. It enables precise audience segmentation, allowing lenders to tailor marketing campaigns to address specific borrower needs. Here are a few examples:  Lenders can identify borrowers with over $100k in tappable equity and high-interest personal loans and credit card debt. These borrowers are ideal for a cash-out refinance campaign aimed at debt consolidation. They can use a similar approach for Home Equity Line of Credit (HELOC) or Reverse Mortgage campaigns.  Another instance is the utilization of property listings data. This identifies borrowers who are actively selling their properties and may need a new mortgage loan. This insight, coupled with credit-based 'in the market' propensity scores, enables lenders to pinpoint highly motivated borrowers. Such personalization improves engagement and enhances the borrower experience. The result is a marketing campaign that resonates with the audience, thus yielding higher response rates and conversions. The integrated view provided by Residential Property Attributes is the secret ingredient enabling lenders to maximize ROI by optimizing their marketing journey at every step.  Taking action  As we traverse today's complex mortgage landscape, it's clear that conventional methods fall short. As we face unprecedented challenges, adopting a holistic view of borrowers via Residential Property Attributes is not an option but a necessity. It's more than a tool; it's a compass guiding lenders towards more informed, resilient, and successful futures in the ever-changing world of mortgage lending.  Learn more about Residential Property Attributes

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

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

According to Experian’s Automotive Consumer Trends Report: Q3 2023, CUVs accounted for 48.3% of new retail registrations and SUVs comprised 13.0%.