Tag: credit underwriting

In this article...What is reject inference? How can reject inference enhance underwriting? Techniques in reject inference Enhancing reject inference design for better classification How Experian can assist with reject inference  In the lending world, making precise underwriting decisions is key to minimizing risks and optimizing returns. One valuable yet often overlooked technique that can significantly enhance your credit underwriting process is reject inferencing. This blog post offers insights into what reject inference is, how it can improve underwriting, and various reject inference methods.  What is reject inference?  Reject inference is a statistical method used to predict the potential performance of applicants who were rejected for a loan or credit — or approved but did not book. In essence, it helps lenders and financial institutions gauge how rejected or non-booked applicants might have performed had they been accepted or booked. By incorporating reject inference, you gain a more comprehensive view of the applicant pool, which leads to more informed underwriting decisions.  Utilizing reject inference helps reduce biases in your models, as decisions are based on a complete set of data, including those who were initially rejected. This technique is crucial for refining credit risk models, leading to more accurate predictions and improved financial outcomes.  How can reject inference enhance underwriting?  Incorporating reject inference into your underwriting process offers several advantages:  Identifying high-potential customers: By understanding the potential behavior of rejected applicants, you can uncover high-potential customers who might have been overlooked before.  Improved risk assessment: Considering the full spectrum of applicants provides a clearer picture of the overall risk landscape, allowing for more informed lending decisions. This can help reduce default rates and enhance portfolio performance.  Optimizing credit decisioning models: Including inferred data from rejected and non-booked applicants makes your credit scoring models more representative of the entire applicant population. This results in more robust and reliable predictions.  Techniques in reject inference  Several techniques are employed in reject inference, each with unique strengths and applications. Understanding these techniques is crucial for effectively implementing reject inference in your underwriting process. Let's discuss three commonly used techniques:  Parceling: This technique involves segmenting rejected applicants based on their characteristics and behaviors, creating a more detailed view of the applicant pool for more precise predictions.  Augmentation: This method adds inferred data to the dataset of approved applicants, producing a more comprehensive model that includes both approved and inferred rejected applicants, leading to better predictions.  Reweighting: This technique adjusts the weights of approved applicants to reflect the characteristics of rejected applicants, minimizing bias towards the approved applicants and improving prediction accuracy.  Pre-diction method  The pre-diction method is a common approach in reject inference that uses data collected at the time of application to predict the performance of rejected applicants. The advantage of this method is its reliance on real-time data, making it highly relevant and current.  For example, pre-diction data can include credit bureau attributes from the time of application. This method helps develop a model that predicts the outcomes of rejected applicants based on performance data from approved applicants. However, it may not capture long-term trends and could be less effective for applicants with unique characteristics.  Post-diction method  The post-diction method uses data collected after the performance window to predict the performance of rejected applicants. Leveraging historical data, this method is ideal for capturing long-term trends and behaviors.  Post-diction data may include credit bureau attributes from the end of the performance window. This method helps develop a model based on historical performance data, which is beneficial for applicants with unique characteristics and can lead to higher performance metrics. However, it may be less timely and require more complex data processing compared to pre-diction.  Enhancing reject inference design for better classification  To optimize your reject inference design, focus on creating a model that accurately classifies the performance of rejected and non-booked applicants. Utilize a combination of pre-diction and post-diction data to capture both real-time and historical trends.  Start by developing a parceling model using pre-diction data, such as credit bureau attributes from the time of application, to predict rejected applicants' outcomes. Regularly update your model with the latest data to maintain its relevance.  Next, incorporate post-diction data, including attributes from the end of the performance window, to capture long-term trends. Combining both data types will result in a more comprehensive model.  Consider leveraging advanced analytics techniques like machine learning and artificial intelligence to refine your model further, identifying hidden patterns and relationships for more accurate predictions. How Experian can assist with reject inference  Reject inference is a powerful tool for enhancing your underwriting process. By predicting the potential performance of rejected and non-booked applicants, you can make more inclusive and accurate decisions, leading to improved risk assessment and optimized credit scoring models.  Experian offers various services and solutions to help financial institutions and lenders effectively implement reject inference into their decisioning strategy. Our solutions include comprehensive and high-quality datasets, which empower you to build models that are more representative of the entire applicant population. Additionally, our advanced analytics tools simplify data analysis and model development, enabling you to implement reject inference efficiently without extensive technical expertise.  Ready to elevate your underwriting process? Contact us today to learn more about our suite of advanced analytics solutions or hear what our experts have to say in this webinar.  Watch Webinar Learn More This article includes content created by an AI language model and is intended to provide general information. 

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. 

This article was updated on February 13, 2024. Traditional credit data has long been a reliable source for measuring consumers' creditworthiness. While that's not changing, new types of alternative credit data are giving lenders a more complete picture of consumers' financial health. With supplemental data, lenders can better serve a wider variety of consumers and increase financial access and opportunities in their communities. What is alternative credit data? Alternative credit data, also known as expanded FCRA-regulated data, is data that can help you evaluate creditworthiness but isn't included in traditional credit reports.1 To comply with the Fair Credit Reporting Act (FCRA), alternative credit data must be displayable, disputable and correctable. Lenders are increasingly turning to new types and sources of data as the use of alternative credit data becomes the norm in underwriting. Today, lenders commonly use one or more of the following: Alternative financial services data: Alternative financial services (AFS) credit data can include information on consumers' use of small-dollar installment loans, single-payment loans, point-of-sale financing, auto title loans and rent-to-own agreements. Consumer permission data: With a consumer's permission, you can get transactional and account-level data from financial accounts to better assess income, assets and cash flow. The access can also give insight into payment history on non-traditional accounts, such as utilities, cell phone and streaming services. Rental payment history: Property managers, electronic rent payment services and rent collection companies can share information on consumers' rent payment history and lease terms. Full-file public records: Local- and state-level public records can tell you about a consumer's professional and occupational licenses, education, property deeds and address history. Buy Now Pay Later (BNPL) data: BNPL tradeline and account data can show you payment and return histories, along with upcoming scheduled payments. It may become even more important as consumers increasingly use this new type of point-of-sale financing. By gathering more information, you can get a deeper understanding of consumers' creditworthiness and expand your lending universe. From market segmentation to fraud prevention and collections, you can also use alternative credit data throughout the customer lifecycle. READ: 2023 State of Alternative Credit Data Report Challenges in underwriting today While unemployment rates are down, high inflation, rising interest rates and uncertainty about the economy are impacting consumer sentiment and the lending environment.2 Additionally, lenders may need to shift their underwriting approaches as pandemic-related assistance programs and loan accommodations end. Lenders may want to tighten their credit criteria. But, at the same time, consumers are becoming accustomed to streamlined application processes and responses. A slow manual review could lead to losing customers. Alternative credit data can help you more accurately assess consumers' creditworthiness, which may make it easier to identify high-risk applicants and find the hidden gems within medium-risk segments. Layering traditional and alternative credit data with the latest approaches to model building, such as using artificial intelligence, can also help you implement precise and predictive underwriting strategies. Benefits of using alternative data for credit underwriting Using alternative data for credit underwriting — along with custom credit attributes and automation — is the modern approach to a risk-based credit approval strategy. The result can offer: A greater view of consumer creditworthiness: Personal cash flow data and a consumer's history of making (or missing) payments that don't appear on traditional credit reports can give you a better understanding of their financial position. Improve speed and accuracy of credit decisions: The expanded view helps you create a more efficient underwriting process. Automated underwriting tools can incorporate alternative credit data and attributes with meaningful results. One lender, Atlas Credit, worked with Experian to create a custom model that incorporated alternative credit data and nearly doubled its approvals while reducing risk by 15 to 20 percent.3 Increase financial inclusion: There are 28 million American adults who don't have a mainstream credit file and 21 million who aren't scoreable by conventional scoring models.4 With alternative credit data, you may be able to more accurately assess the creditworthiness of adults who would otherwise be deemed thin file or unscorable. Broadening your pool of applications while appropriately managing risk is a measurable success. What Experian builds and offers Experian is continually expanding access to expanded FCRA-regulated data. Our Experian RentBureau and Clarity Services (the leading source of alternative financial credit data) have long given lenders a more complete picture of consumers' financial situation. Experian also helps lenders effectively use these new types of data. You can also incorporate the data into your proprietary marketing, lending and collections strategies. Experian is also using alternative credit data for credit scoring. The Lift Premium™ model can score 96 percent of U.S. adults — compared to the 81 percent that conventional models can score using traditional data.5 The bottom line Lenders have been testing and using alternative credit data for years, but its use in underwriting may become even more important as they need to respond to changing consumer expectations and economic uncertainty. Experian is supporting this innovation by expanding access to alternative data sources and helping lenders understand how to best use and implement alternative credit data in their lending strategies. Learn more 1When 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. 2Experian (2024). State of the Economy Report 3Experian (2020). OneAZ Credit Union [Case Study] 4Oliver Wyman (2022). Financial Inclusion and Access to Credit [White Paper] 5Ibid.

Well-designed underwriting strategies are critical to creating more value out of your member relationships and driving growth for your business. But what makes an advanced underwriting strategy? It’s all about the data, analytics, and the people behind it. How a credit union achieved record loan growth Educational Federal Credit Union (EdFed) is a member-owned cooperative dedicated to serving the financial needs of school employees, students, and parents within the education community. After migrating to a new loan origination system, the credit union wanted to design a more profitable underwriting strategy to increase efficiency and grow their business. EdFed partnered with Experian to design an advanced underwriting strategy using our vast data sources, advanced analytics, and recommendations for greater automation. After 30 months of implementing the new loan origination system and underwriting strategies, the credit union increased their loans by 32% and automated approvals by 21%. “The partnership provided by Experian, backed by analytics, makes them the dream resource for our growth as a credit union. It isn’t just the data… it’s the people.” – Michael Aubrey, SVP Lending at Educational Federal Credit Union Learn more about how Experian can help you enhance your underwriting strategy. Learn more

Credit risk management best practices have been established and followed for years, but new technology and data sources offer lenders an opportunity to refine their credit risk management strategies.   What is credit risk management? Credit risk is the possibility that a borrower will not repay a debt as agreed. And credit risk management is the art and science of using risk mitigation tools to minimize losses while maximizing profits from lending activity.   Lenders can create credit underwriting criteria for each of their products and use risk-based pricing to alter the terms of a loan or line of credit based on the risk associated with the product and borrower. Credit portfolio management goes beyond originations and individual decisions to consider portfolios at large.   CASE STUDY: Atlas Credit worked with Experian to create a machine learning-powered model, optimize risk score cutoffs and automate their underwriting. The small-dollar lender nearly doubled its loan approval rates while reducing its losses by up to 20 percent. Why is credit risk management important? Continually managing credit risk matters because there's always a balancing act.   Tightening a credit box — using more restrictive underwriting criteria — might reduce credit losses. However, it can also decrease approval rates that would exclude borrowers who would have repaid as agreed. Expanding a credit box might increase approval rates but is only beneficial if the profit from good new loans exceeds credit losses.   Fraud is also on the rise and becoming more complex, making fraud management an important part of understanding risk. For instance, with synthetic identity fraud, fraudsters might “age an account" or make on-time payments before, “busting out” or maxing out a credit card and then abandoning the account.  If you look at payment activity alone, it might be hard to classify the loss as a fraud loss or credit loss.  Additionally, external economic forces and consumer behavior are constantly in flux. Financial institutions need effective consumer risk management and to adjust their strategies to limit losses. And they must dynamically adjust their underwriting criteria to account for these changes. You could be pushed off balance if you don't react in time. What does managing credit risk entail? Lenders have used the five C’s of credit to measure credit risk and make lending decisions for decades:  Character: The likelihood a borrower will repay the loan as agreed, often measured by analyzing their credit report and a credit risk score.   Capacity: The borrower's ability to pay, which lenders might measure by reviewing their outstanding debt, income, and debt-to-income ratio.   Capital: The borrower's commitment to the purchase, such as their down payment when buying a vehicle or home.   Collateral: The value of the collateral, such as a vehicle or home for an auto loan or mortgage.   Conditions: The external conditions that can impact a borrower's ability to afford payments, such as broader economic trends.  Credit risk management considers these within the context of a lender’s goals and its specific lending products. For example, capital and collateral aren't relevant for unsecured personal loans, which makes character and capacity the primary drivers of a decision.   Credit risk management best practices at origination Advances in analytics, computing power and real-time access to additional data sources are helping lenders better measure some of the C’s.   For example, credit risk scores can more precisely assess character for a lender's target market than generic risk scores. And open banking data allows lenders to more accurately understand a borrower's capacity by directly analyzing their cash flows.   With these advances in mind, leading lenders:  View underwriting as a dynamic process: Lenders have always had to respond to changing forces, and the pandemic highlighted the need to be nimble. Consider how you can use analytical insights to quickly adjust your strategies.   Test the latest credit risk modeling techniques: Artificial intelligence (AI) and machine learning (ML) techniques can improve credit risk model performance and drive automated credit risk decisioning. We've seen ML models consistently outperform traditional credit risk models by 10 to 15 percent.¹ Use multiple data sources: Alternative credit data* and consumer-permissioned data offer increased and real-time visibility into borrowers' creditworthiness. These additional data sources can also help fuel ML credit risk models.   Expand their lending universe: Alternative data can also help lenders more accurately assess the credit risk of the 49 million Americans who don't have a credit file or aren't scoreable by conventional models.² At the same time, they consciously remove biases from their decisions to increase financial inclusion.  READ: The Getting AI-driven decisioning right in financial services white paper explores trends, advantages, challenges and best practices for using AI in decisioning.   Experian helps lenders measure and manage credit risk Experian can trace its history of helping companies manage their credit risk back to 1803.³ Of course, a lot has changed since then, and today Experian is a leading provider of traditional credit data, alternative credit data and credit risk analytics.   For those who want to quickly benefit from the latest technological advancements, our Lift Premium™ credit risk model uses traditional and alternative data to score up to 96 percent of U.S. consumers — compared to the 81 percent that conventional models can score.4 Experian’s Ascend Platform and Ascend Intelligence Services™ can help lenders develop, deploy and monitor custom credit risk models to optimize their decisions.    With end-to-end platforms, our account and portfolio management services can help you limit risk, detect fraud, automate underwriting and identify opportunities to grow your business.   Learn more about Experian's approach to credit risk management ¹Experian (2020). Machine Learning Decisions in Milliseconds ²Oliver Wyman (2022). Financial Inclusion and Access to Credit ³Experian (2013). A Brief History of Experian 4Experian (2023). Lift Premium™ and Lift Plus™ *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.