Tag: lending decisions

Smaller creditors often struggle to access reliable credit‑reporting solutions, as many available options frequently require technical integration, such as full Application Programming Interface (API) implementation or enterprise‑level approvals, creating barriers that small lenders cannot easily overcome. Minimum volume requirements further intensify the challenge, forcing smaller creditors to pay disproportionately high costs for the limited number of reports they need. As a result, the financial burden and operational complexity restrict their ability to compete, hindering growth and preventing them from adopting the same efficient, data‑driven processes available to larger institutions. Experian fully recognizes the need to empower smaller creditors and is proud to introduce a new capability, Experian Express, designed with these creditors in mind. Experian Express is a digital onboarding portal that fast-tracks the credentialing process for smaller creditors to gain access to Experian’s Credit Profile Report for the purpose of extending credit. Via a fully digital online process, users can choose from two plans tailored to the needs of community banks and credit unions. A new opportunity to build high-value relationships With Experian Express, credit unions and community banks can offer benefits to both consumer and business customers seeking access to credit. Consumers and businesses want more digital convenience, and a primary institution that can meet their needs in one place, as more than half of consumers who switch their primary bank hold over four checking relationships¹ and 55% of U.S. consumers say mobile apps are their most‑used method for managing bank accounts,² while customers across all age groups are curating financial services from multiple providers due to digital gaps at their primary institutions.³ Community banks and credit unions that offer integrated digital credit products, faster onboarding, and personalized advice can convert today’s rising credit demand into long-term, primary relationships rather than one-off loan transactions. What is the value of the opportunity for capturing more consumer and business relationships? Both consumers and businesses are showing strong signals of growth in their demand for credit. Consumer demand for credit in 2026 represents $52.6B annualized,4 when converting the Federal Reserve’s latest growth pace into a reachable opportunity for community banks and credit unions in today’s market. Elevated new business formation presents an opportunity to build more relationships, as a growing majority of small businesses have been in operation for less than 2 years and have little credit history. New small businesses often use the business owner’s personal credit to access capital for growth. Younger businesses are accounting for a growing portion of newly opened commercial accounts. In 2025, businesses under 2 years old accounted for 36% of new commercial accounts.5 It is important to recognize that customer composition is changing as new business formation rises, with solopreneurs and gig workers making up a growing majority of new small businesses. Smaller lenders like credit unions and community banks, which serve as the backbone of Main Street, should prioritize this demographic as the divide between consumer and business customers in their portfolios continues to blur. Taking advantage of the new wave of customers while mitigating fraud More customers demand digital experiences; however, Experian’s 2026 Global Future of Fraud Forecast shows that artificial intelligence (AI) is simultaneously enabling an unprecedented escalation in fraud. Fraud losses are rising sharply: nearly 60% of companies reported increased fraud from 2024 to 2025, and consumers lost more than $12.5 billion to fraud in 2024 alone.⁶ Experian warns that fraudsters are rapidly weaponizing agentic AI to launch autonomous, harder‑to‑detect digital attacks, creating “machine‑to‑machine mayhem” as transactions occur without clear ownership or liability.⁷ Generative‑AI–enabled deepfakes are also accelerating, allowing fraudsters to impersonate job candidates, bypass identity checks, and infiltrate sensitive systems at scale.⁷ In addition, as most small businesses are newly formed with little credit history, up to 46% of small business loan applications show signs of first-party fraud, commonly known as first payment default, such as misrepresented revenue or business details. 7 The misrepresentation of financial information by new business customers creates a unique issue for creditors as they face a wave of first-payment defaults. As digital adoption grows, businesses and consumers face an environment where fraud is not only faster and more scalable but increasingly woven into everyday digital interactions. How can firms take advantage of the new wave of business customers while protecting their portfolios? In a world where fragmented data and siloed systems hinder accurate decision-making, a unified approach to scoring for both creditworthiness and fraud signals offers a solution. Whether dealing with a consumer or a small business looking for access to credit, relationships with customers represent a new form of digital currency that provides long-term value. Need to find a way to grow your business and consumer accounts? Start by using the right data to better understand their needs and easily upsell your existing customers. Seeing the whole picture of your customers is the key to outperforming competitors. To stay competitive, community banks and credit unions must act with laser precision to block fraudsters and unlock credit for underserved, yet high-potential, consumer and small business customers. Now it is easier than ever to gain an edge with Experian’s vast datasets, which provide depth and accuracy to deliver unmatched insights for confident decision-making through Credit Profile Reports. Community banks and credit unions can use Experian Express’ tailored annual plans, which include fraud prevention tools, to gain access to Experian’s Credit Profile Reports and better understand the creditworthiness of a consumer applying for credit or a small business owner’s personal credit to enhance their ability to get access to credit. Lenders can use Experian Express as a bridge to access Experian’s credit solutions online to perform credit checks. Ready to start a conversation? Learn more about Experian Express

How can lenders ensure they’re making the most accurate and fair lending decisions? The answer lies in consistent model validations. What are model validations? Model validations are vital for effective lending and risk-based pricing programs. In addition to helping you determine which credit scoring model works best on your portfolio, the performance (odds) charts from validation results are often used to set score cutoffs and risk-based pricing tiers. Validations also provide the information you need to implement a new score into your decisioning process. Factors affecting model validations Understanding how well a score predicts behavior, such as payment delinquency or bankruptcy, enables you to make more confident lending decisions. Model performance and validation results can be impacted by several factors, including: Dynamic economic environment – Shifts in unemployment rates, interest rate hikes and other economic indicators can impact consumer behavior. Regulatory changes affecting consumers – For example, borrowers who benefited from a temporary student loan payment pause may face challenges as they resume payments. Scorecard degradation – A model that performed well several years ago may not perform as well under current conditions. When to perform model validations The Office of the Comptroller of the Currency’s Supervisory Guidance on Model Risk Management states model validations should be performed at least annually to help reduce risk. The validation process should be comprehensive and produce proper documentation. While some organizations perform their own validations, those with fewer resources and access to historical data may not be able to validate and meet the guidance recommendations. Regular validations support compliance and can also give you confidence that your lending strategies are built on solid, current data that drive better outcomes. Good model validation practices are critical if lenders are to continue to make data-driven decisions that promote fairness for consumers and financial soundness for the institution. Make better lending decisions If you’re a credit risk manager responsible for the models driving your lending policies, there are several things you can do to ensure that your organization continues to make fair and sound lending decisions: Assess your model inventory. Ensure you have comprehensive documentation showing when each model was developed and when it was last validated. Validate the scores you are using on your data, along with those you are considering, to compare how well each model performs and determine if you are using the most effective model for your needs. Produce validation documentation, including performance (odds) charts and key performance metrics, which can be shared with regulators. Utilize the performance charts produced from the validation to analyze bad rates/approval rates and adjust cutoff scores as needed. Explore alternative credit scoring models to potentially enhance your scoring process. As market conditions and regulations continue to evolve, model validations will remain an essential tool for staying competitive and making sound lending decisions. Ready to ensure your lending decisions are based on the latest data? Learn more about Experian’s flexible validation services and how we can support your ongoing success. Contact us today to schedule a consultation. Learn more

Today’s changing economy is directly impacting consumers’ financial behaviors, with some individuals doing well and some showing signs of payment stress. And while these trends may pose challenges to financial institutions, such as how to expand their customer base without taking on additional risk, the right credit attributes can help them drive smarter and more profitable lending decisions. With Experian’s industry-leading credit attributes, organizations can develop precise and explainable acquisition models and strategies. As a result, they can: Expand into new segments: By gaining deeper insights into consumer trends and behaviors, organizations can better assess an individual’s creditworthiness and approve populations who might have been overlooked due to limited or no credit history. Improve the customer experience: Having a wider view of consumer credit behavior and patterns allows organizations to apply the best treatment at the right time based on each consumer’s specific needs. Save time and resources: With an ongoing managed set of base attributes, organizations don’t have to invest significant resources to develop the attributes themselves. Additionally, existing attributes are regularly updated and new attributes are added to keep pace with industry and regulatory changes. Case study: Enhance decision-making and segmentation strategies A large retail credit card issuer was looking to grow their portfolio by identifying and engaging more consumers who met their credit criteria. To do this, they needed to replace their existing custom acquisition model with one that provided a granular view of consumer behavior. By partnering with Experian, the company was able to implement an advanced custom acquisition model powered by our proprietary Trended 3DTM and Premier AttributesSM. Trended 3D analyzes consumers’ behavior patterns over time, while Premier Attributes aggregates and summarizes findings from credit report data, enabling the company to make faster and more strategic lending decisions. Validations of the new model showed up to 10 percent improvement in performance across all segments, helping the company design more effective segmentation strategies, lower their risk exposure and approve more accounts. To learn how Experian can help your organization make the best data-driven decisions, read the full case study or visit us. Download case study Visit us

In today’s uncertain economic environment, the question of how to reduce portfolio volatility while still meeting consumers’ needs is on every lender’s mind.  With more than 100 million consumers already restricted by traditional scoring methods used today, lenders need to look beyond traditional credit information to make more informed decisions. By leveraging alternative credit data, you can continue to support your borrowers and expand your lending universe. In our most recent podcast, Experian’s Shawn Rife, Director of Risk Scoring and Alpa Lally, Vice President of Data Business, discuss how to enhance your portfolio analysis after an economic downturn, respond to the changing lending marketplace and drive greater access to credit for financially distressed consumers. Topics discussed, include: Making strategic, data-driven decisions across the credit lifecycle Better managing and responding to portfolio risk Predicting consumer behavior in times of extreme uncertainty Listen in on the discussion to learn more. Experian · Effective Lending in the Age of COVID-19

This is the third in a series of blog posts highlighting optimization, artificial intelligence, predictive analytics, and decisioning for lending operations in times of extreme uncertainty. The first post dealt with optimization under uncertainty and the second with predicting consumer payment behavior. In this post I will discuss how well credit scores will work for consumer lenders during and after the COVID-19 crisis and offer some recommendations for what lenders can be doing to measure and manage that model risk in a time like this. Perhaps no analytics innovation has created opportunity for more individuals than the credit score has. The first commercially available credit score was developed by MDS (now part of Experian) in 1987. Soon afterwards FICO® popularized the use of scores that evaluate the risk that a consumer would default on a loan. Prior to that, lending decisions were made by loan officers largely on the basis on their personal familiarity with credit applicants. Using data and analytics to assess risk not only created economic opportunity for millions of borrowers, but it also greatly improved the financial soundness of lending institutions worldwide. Predictive models such as credit scores have become the most critical tools for consumer lending businesses. They determine, among other things, who gets a loan and at what price and how an account such as a credit line is managed through its life cycle. Predictive models are in many cases critical for calculating loan and loss reserves, for stress testing, and for complying with accounting standards. Nearly all lenders rely on generic scores such as the FICO® score and VantageScore® credit score. Most larger companies also have a portfolio of custom scorecards that better predict particular aspects of payment behavior for the customers of interest. So how well are these scorecards likely to perform during and after the current pandemic? The models need to predict consumer credit risk even as: Nearly all consumers change their behaviors in response to the health crisis, Millions of people—in America and internationally—find their income suddenly reduced, and Consumers receive large numbers of accommodations from creditors, who have in turn temporarily changed some of their credit reporting practices in response to guidelines in the federal CARES Act. In an earlier post, I pointed out that there is good reason to believe that credit scores will tend to continue to rank order consumers from most likely to least likely to repay their debts even as we move from the longest economic expansion in history to a period of unforeseen and unexpected challenges. But the interpretation of the score (for example, the log odds or the bad rate) may need to be adjusted. Furthermore, that assumes that the model was working well on a lender’s population before this crisis started. If it has been a long time since a scorecard was validated, that assumption needs to be questioned. Because experts are considering several different scenarios regarding both the immediate and long-term economic impacts of COVID-19, it’s important to have a plan for ongoing monitoring as long as necessary. Some lenders have strong Model Risk Management (MRM) teams complying with requirements from the Federal Reserve, Federal Deposit Insurance Corporation (FDIC), the Office of the Comptroller of the Currency (OCC). Those resources are now stretched thin. Other institutions, with fewer resources for MRM, are now discovering gaps in their model inventories as they implement operational changes. In either case, now’s the time to reassess how well scorecards are working. Good model validation practices are especially critical now if lenders are to continue to make the sound data-driven decisions that promote fairness for consumers and financial soundness for the institution. If you’re a credit risk manager responsible for the generic or custom models driving your lending, servicing, or capital allocation policies, there are several things you can do--starting now--to be sure that your organization can continue to make fair and sound lending decisions throughout this volatile period: Assess your model inventory. Do you have good documentation showing when each of the models in your organization was built? When was it last validated? Assign a level of criticality to each model in use. Starting with your most critical models, perform a baseline validation to determine how the model was performing prior to the global health crisis. It may be prudent to conduct not only your routine validation (verifying that the model was continuing to perform at the beginning of the period) but also a baseline validation with a shortened performance window (such as 6-12 months). That baseline validation will be useful if the downturn becomes a protracted one—in which case your scorecard models should be validated more frequently than usual. A shorter outcome window will allow a timelier assessment of the relationship between the score and the bad rate—which will help you update your lending and servicing policies to prevent losses. Determine if any of your scorecards had deteriorated even before the global pandemic. Consider recalibrating or rebuilding those scorecards. (Use metrics such as the Population Stability Index, the K-S statistic and the Gini Coefficient to help with that decision.) Many lenders chose not to prioritize rebuilding their behavioral scorecards for account management or collections during the longest period of economic growth in memory. Those models may soon be among the most critical models in your organization as you work to maintain the trust of your accountholders while also maintaining your institution’s financial soundness. Once the CARES accommodation period has expired, it will be important to revalidate your models more frequently than in the past—for as long as it takes until consumer behavior normalizes and the economy finds its footing. When you find it appropriate to rebuild a scorecard model, consider whether now is the time to implement ethical and explainable AI. Some of our clients are finding that Machine Learned models are more predictive than traditional scorecards. Early Experian research using data from the last recession indicates this will continue to be true for the foreseeable future. Furthermore, Experian has invested in Research and Development to help these clients deliver FCRA-compliant Adverse Action reasons to their consumers and to make the models explainable and transparent for model risk governance and compliance purposes. The sudden economic volatility that has resulted from this global health crisis has been a shock to all organizations. It is important for lenders to take the pulse of their predictive models now and throughout the downturn. They are especially critical tools for making sound data-driven business decisions until the economy is less volatile. Experian is committed to helping your organization during times of uncertainty. For more resources, visit our Look Ahead 2020 Hub. Learn more