Tag: credit risk management

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

Alternative lending is continuing to revolutionize the financial services landscape. From full-file public records to cash flow transactions, alternative credit data empowers financial institutions to make more informed lending decisions.  This article focuses on cashflow insights and how they help financial institutions drive profitable and inclusive growth.  Challenges with traditional credit underwriting  Traditional underwriting often limits access to credit for marginalized communities, including young adults, immigrants, and those from low-income backgrounds. Because the process relies heavily on credit history and credit scores to determine an applicant’s ability to pay, those with less-than-ideal credit profiles could be overlooked. This then creates a cycle — those who are already disadvantaged face further barriers to accessing credit, limiting their abilities to invest in opportunities that can improve their financial situations, such as education or homeownership.  Additionally, traditional underwriting models can be rigid. Consumers with stable incomes or significant assets may be denied credit if their financial profiles don’t fit the narrow criteria established by traditional models. As the financial landscape evolves, it’s important for lenders to adopt more inclusive and adaptive approaches to credit underwriting.  What is cashflow underwriting?  Cashflow underwriting is a modern approach to evaluating a borrower’s creditworthiness. It uses fresh, consumer-permissioned bank account transaction (balance, income and expense) data, giving lenders greater visibility into loan applicants’ financial situation. This process is made possible through open banking, an established, secure framework that enables consumers to quickly and easily share their bank account information with third-party financial service providers.  READ: Learn more about the open banking landscape.  Let’s look at a few quick examples:   A prospective tenant is filling out a rental application. Instead of manually submitting paystubs to verify their income, open banking facilitates the digital sharing of full cashflow data in seconds, enabling property managers to quickly access the applicant’s full cash flow information.  A consumer was previously denied credit due to insufficient credit history. With cashflow underwriting, the consumer is offered a second chance to qualify for the loan by including cashflow data in the lender’s decisioning model. The additional information gathered on the consumer’s ability to pay can transform the initial decline decision into an approval.   Cashflow underwriting can also be used for credit line management. By assessing a borrower’s income and expense transactions, lenders can recommend optimal credit limits that cater to their spending potential while minimizing risk.  Benefits of cashflow underwriting  There are many benefits to integrating cashflow data into the credit underwriting process, including:  Enhanced risk assessment. Going off credit scores and repayment behaviors alone won’t provide lenders with a complete or current picture of applicants. Through open banking, lenders can gain access to cashflow data in real-time, allowing them to more accurately assess consumers, increase approvals, and reduce credit risk.  Inclusive lending. Over 100 million adult Americans are considered unscoreable, invisible, or subprime.1 However, 71% of consumers are willing to share their banking information if it could improve their chances of getting approved for credit.2 With deeper insights into consumers’ income and expenses, lenders can increase credit access in underserved communities.  Improved customer experiences. Gaining a more comprehensive view of a consumer’s financial situation enables lenders to determine what loan products they’re eligible for and craft personalized options.  READ: Learn more about the benefits of leveraging alternative data for credit underwriting.  Get started  Cashflow underwriting represents a significant step forward in the world of lending. It offers a more comprehensive approach to assessing creditworthiness, helping financial institutions drive growth and profitability.   Experian’s Cashflow Attributes are an open banking enabled solution that provides lenders with consumer-permissioned insights into borrowers’ financial behaviors. With 940+ attributes derived from transaction data across 133 categories, financial institutions can make smarter, more inclusive lending decisions. Learn more about Cashflow Attributes Learn more about open banking 1 2023 State of Alternative Credit Data Report, Experian, 2023.  2 Atomik Research survey of 2,005 U.S. adults online, matching national demographics, 2024.  This article includes content created by an AI language model and is intended to provide general information. 

Rising balances and delinquency rates are causing lenders to proactively minimize credit risk through pre-delinquency treatments. However, the success of these types of account management strategies depends on timely and predictive data. Credit attributes summarize credit data into specific characteristics or variables to provide a more granular view of a consumer’s behavior. Credit attributes give context about a consumer’s behavior at a specific point in time, such as their current revolving credit utilization ratio or their total available credit. Trended credit attributes analyze credit history data for consumer behavior patterns over time, including changes in utilization rates or how often a balance exceeded an account’s credit limit during the previous 12 months. In a recent analysis, we found that credit attributes related to utilization were highly predictive of future delinquencies in bankcard accounts, with many lenders better managing their credit risk when incorporating these attributes into their account management processes. READ: Find out how custom attributes and models can help you stay ahead of your competitors in the "Build a profitable portfolio with credit attributes" e-book. Using attributes to manage credit risk An enhanced understanding of credit attributes can be leveraged to manage risk throughout the customer lifecycle. They can be important when you want to: Improve credit strategies and efficiencies: Overlay attributes and incorporate them into credit policy rules, such as knockout criteria, to expand your lending population and increase automation without taking on more credit risk. Better understand customers' credit trends: Experian’s wide range of credit data, including trended credit attributes, can help you quickly understand how consumers are faring off-book for visibility into other lending relationships and if they’ll likely experience financial stress in the future. Credit attributes can also help precisely segment populations. For example, attributes can help you distinguish between two people who have similar credit risk scores — but very different trajectories — and will better determine who's the least risky customer. Predicting 60+ day delinquencies with credit attributes To evaluate the effectiveness of credit attributes during account review, we looked at 2.9 million open and active bankcard accounts to see which attributes best predicted the likelihood of an account reaching 60 days past due. For this analysis, we used snapshots of bankcard accounts that were reported in October 2022 and April 2023. Additionally, we analyzed the predictive power of over 4,000 attributes from Experian Premier AttributesSM and Trended 3DTM. Key findings Nine of the top 20 most predictive credit attributes were related to credit utilization rates. Delinquency-related attributes were predictive but weren’t part of the top 10. Three of the top 10 attributes were related to available credit. Turning insight into action While we analyzed credit attributes for account review, determining attribute effectiveness for other use cases will depend on your own portfolio and goals. However, you can use a similar approach to finding the predictive power of attributes. Once you identify the most predictive credit attributes for your population, you can also create an account review program to track these metrics, such as changes in utilization rates or available credit balances. Using Experian’s Risk and Retention Triggers℠ can immediately notify you of customers' daily credit activity to monitor those changes. Ongoing monitoring of attributes and triggers can help you identify customers who are facing financial stress and are headed toward delinquency. You can then proactively take steps to reduce your risk exposure, prioritize accounts, and modify pre-collections strategy based on triggering events. Experian offers credit attributes and the tools to use them Creating and managing credit attributes can be a complex and never-ending task. You need to regularly monitor attributes for performance drift and to address changing regulatory requirements. You may also want to develop new attributes based on expanding data sources and industry trends. Many organizations don’t have the resources to create, manage, and update credit attributes on their own. That’s where Experian’s 4,500+ attributes and tools can help to save time and money. Premier Attributes includes our core attributes and subsets for over 50 industries. Trended 3D attributes can help you better understand changes in consumer behavior and creditworthiness. Clear View AttributesTM offers insights from expanded FCRA data* that generally isn’t reported to consumer credit bureaus. You can easily review and manage your portfolios with Experian’s Ascend Quest™ platform. The always-on access allows you to request thousands of data elements, including credit attributes, risk scores, income models, segmentation data, and payment history, at any time. Use insights from the data and leverage Ascend Quest to quickly identify accounts that may be experiencing financial stress to limit your credit risk — and target others with retention and up-selling opportunities. Watch the Ascend Quest demo to see it in action, or contact us to learn more about Experian’s credit attributes and account review solutions. Watch demo Contact us

For lenders, first payment default (FPD) is more than just financial jargon; it's a crucial metric in assessing credit risk. This blog post will walk you through the essentials of FPD,  from defining the term to exploring how you can prevent and mitigate its potential impact. Understanding first payment default FPD occurs when a consumer fails to make their initial payment on a loan or credit agreement, which is often perceived as an early signal of a potential cascade of risky behavior. Recognizing FPD is the starting point for lenders to address potential issues with new borrowers before they escalate. One important aspect to grasp is the timeline of FPD. It’s not just about missing the first payment; it's about "early" missing. The timing of defaults is often critical in assessing the overall risk profile of a borrower or group of borrowers. The earlier a borrower starts to miss payments, the riskier they tend to be. Examining the causes of FPD The roots of FPD are diverse and can be classified into two broad categories: External factors: These include sudden financial crises, changes in employment status, or unforeseen expenses. Such factors are often beyond the borrower's immediate control. Internal factors: This category covers more deliberate or chronic financial habits, such as overspending, lack of savings, or overleveraging on credit. It's often indicative of longer-term financial instability. Understanding the causes of early payment default is the first step in effective risk management and customer engagement strategies. Implications of FPD for lenders FPD doesn't just signal immediate financial loss for lenders in terms of the missed installment. It sets off a cascade of consequences that affect the bottom line and the reputation of the institution. Financial loss. Lenders incur direct financial losses when a payment is missed, but the implications go beyond the missed payment amount. There are immediate costs associated with servicing, collections, and customer support. In the longer term, repeated defaults can lead to write-offs, impacting the institution's profitability and regulatory standing. Regulatory scrutiny. Repeated instances of FPD can also draw the attention of regulators, leading to scrutiny and potentially increased compliance costs. Mitigating first payment default Mitigating FPD requires a multifaceted approach that blends data, advanced analytics, customer engagement, and agile risk management. Lenders need to adopt strategies that can detect early signs of potential FPD and intervene preemptively. Data-driven decision-making. Leveraging advanced analytics and credit risk modeling is crucial. By incorporating transactional and behavioral data, lenders can make more accurate assessments of a borrower's risk profile. Utilizing predictive models can help forecast which borrowers are likely to default on their first payment, allowing for early intervention. Proactive customer engagement. Initiatives that revolve around education, personalized financial planning advice, and flexible payment arrangements can help borrowers who might be at risk of FPD. Proactive outreach can engage customers before a default occurs, turning a potential negative event into a positive experience. Agile risk management. Risk management strategies should be dynamic and responsive to changing market and customer conditions. Regularly reviewing and updating underwriting criteria, credit policies, and risk assessment tools ensures that lenders are prepared to tackle FPD challenges as they arise. Using FPD as a customer management tool Lastly, and perhaps most importantly, lenders can use FPD as a tool to foster better customer management. Every FPD is a data point that can provide insights into customer behavior and financial trends. By studying the causes and outcomes of FPD, lenders can refine their risk mitigation tools and improve their customer service offerings. Building trust through handling defaults. How lenders handle defaults, specifically the first ones, can significantly impact customer trust. Transparent communication, fair and considerate policies, and supportive customer service can make a difference in retaining customers and improving the lender's brand image. Leveraging data for personalization. The increasing availability of data means lenders can offer more personalized services. By segmenting customers based on payment behavior and response to early interventions, lenders can tailor offerings that meet the specific financial needs and challenges of individual borrowers. How Experian® can help First payment default is a critical aspect of credit risk management that requires attention and proactive strategies. By understanding the causes, implications, and mitigation strategies associated with FPD, financial institutions can not only avoid potential losses but also build stronger, more enduring relationships with their customers. Learn more about Experian’s credit risk modeling solutions. Learn more This article includes content created by an AI language model and is intended to provide general information.

Today's lenders use expanded data sources and advanced analytics to predict credit risk more accurately and optimize their lending and operations. The result may be a win-win for lenders and customers. What is credit risk? Credit risk is the possibility that a borrower will not repay a debt as agreed. Credit risk management encompasses the policies, tools and systems that lenders use to understand this risk. These can be important throughout the customer lifecycle, from marketing and sending preapproved offers to underwriting and portfolio management. Poor risk management can lead to unnecessary losses and missed opportunities, especially because risk departments need to manage risk with their organization's budgetary, technical and regulatory constraints in mind. How is it assessed?  Credit risk is often assessed with credit risk analytics — statistical modeling that predicts the risk involved with credit lending. Lenders may create and use credit risk models to help drive decisions. Additionally (or alternatively), they rely on generic or custom credit risk scores: Generic scores: Analytics companies create predictive models that rank order consumers based on the likelihood that a person will fall 90 or more days past due on any credit obligation in the next 24 months. Lenders can purchase these risk scores to help them evaluate risk. Custom scores: Custom credit risk modeling solutions help organizations tailor risk scores for particular products, markets, and customers. Custom scores can incorporate generic risk scores, traditional credit data, alternative credit data* (or expanded FCRA-regulated data), and a lender's proprietary data to increase their effectiveness. About 41 percent of consumer lending organizations use a model-first approach, and 55 percent use a score-first approach to credit decisioning.1 However, these aren't entirely exclusive groupings. For example, a credit score may be an input in a lender's credit risk model — almost every lender (99 percent) that uses credit risk models for decisioning also uses credit scores.2 Similarly, lenders that primarily rely on credit scores may also have business policies that affect their decisions. What are the current challenges? Risk departments and teams are facing several overarching challenges today: Staying flexible: Volatile market conditions and changing consumer preferences can lead to unexpected shifts in risk. Organizations need to actively monitor customer accounts and larger economic trends to understand when, if, and how they should adjust their risk policies. Digesting an overwhelming amount of data: More data can be beneficial, but only if it offers real insights and the organization has the resources to understand and use it efficiently. Artificial intelligence (AI) and machine learning (ML) are often important for turning raw data into actionable insights. Retaining IT talent: Many organizations are trying to figure out how to use vast amounts of data and AI/ML effectively. However, 82 percent of lenders have trouble hiring and retaining data scientists and analysts.3 Separating fraud and credit losses: Understanding a portfolio's credit losses can be important for improving credit risk models and performance. But some organizations struggle to properly distinguish between the two, particularly when synthetic identity fraud is involved. Best practices for credit risk management Leading financial institutions have moved on from legacy systems and outdated risk models or scores. And they're looking at the current challenges as an opportunity to pull away from the competition. Here's how they're doing it: Using additional data to gain a holistic picture: Lenders have an opportunity to access more data sources, including credit data from alternative financial services and consumer-permissioned data. When combined with traditional credit data, credit scores, and internal data, the outcome can be a more complete picture of a consumer's credit risk. Implementing AI/ML-driven models: Lenders can leverage AI/ML to analyze large amounts of data to improve organizational efficiency and credit risk assessments. 16 percent of consumer lending organizations expect to solely use ML algorithms for credit decisioning, while two-thirds expect to use both traditional and ML models going forward.4 Increasing model velocity: On average, it takes about 15 months to go from model development to deployment. But some organizations can do it in less than six.5 Increasing model velocity can help organizations quickly respond to changing consumer and economic conditions. Even if rapid model creation and deployment isn't an option, monitoring model health and recalibrating for drift is important. Nearly half (49 percent) of lenders check for model drift monthly or quarterly — one out of ten get automated alerts when their models start to drift.6 WATCH: Accelerating Model Velocity in Financial Institutions Improving automation and customer experience Lenders are using AI to automate their application, underwriting, and approval processes. Often, automation and ML-driven risk models go hand-in-hand. Lenders can use the models to measure the credit risk of consumers who don't qualify for traditional credit scores and automation to expedite the review process, leading to an improved customer experience. Learn more by exploring Experian's credit risk solutions. Learn more * 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 (FCRA). Hence, the term “Expanded FCRA Data" may also apply in this instance and both can be used interchangeably. 1-6. Experian (2023). Accelerating Model Velocity in Financial Institutions