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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.

Experian’s eighth annual identity and fraud report found that consumers continue to express concerns with online security, and while businesses are concerned with fraud, only half fully understand its impact – a problem we previously explored in last year’s global fraud report. In our latest report, we explore today’s evolving fraud landscape and influence on identity, the consumer experience, and business strategies. We surveyed more than 2,000 U.S. consumers and 200 U.S. businesses about their concerns, priorities, and investments for our 2023 Identity and Fraud Report. This year’s report dives into: Consumer concerns around identity theft, credit card fraud, online privacy, and scams such as phishing.Business allocation to fraud management solutions across industries.Consumer expectations for both security and their experience.The benefits of a layered solution that leverages identity resolution, identity management, multifactor authentication solutions, and more. To identify and treat each fraud type appropriately, you need a layered approach that keeps up with ever-changing fraud and applies the right friction at the right time using identity verification solutions, real-time fraud risk alerts, and enterprise orchestration. This method can reduce fraud risks and help provide a more streamlined, unified experience for your consumers. To learn more about our findings and how to implement an effective solution, download Experian’s 2023 Identity and Fraud Report. Download the report

In a changing economy, banks of all sizes are more budget conscious, leading many to pull back on their marketing spend for new customer acquisition. But by making strategic marketing moves now, banks can uncover new opportunities and drive profitable, long-term growth. So, how can you find, engage, and win over high-value customers? Know who’s in the market for credit To build an effective bank customer acquisition strategy, you’ll want to be proactive with your campaign planning. Let’s say you’ve already defined your customer profile and have insights into their interests, lifestyles, and demographics. With predictive metrics and advanced tools like trended data and propensity-to-open models, you can further refine your segmentation strategies by identifying individuals who are likely to be in the market for your product. This way, you can reach consumers at the right time and personalize offers to achieve higher open rates. Embrace the digital era With today’s consumers increasing their banking activities online, leveraging digital channels in your bank customer acquisition strategy is imperative. In addition to connecting with consumers through direct mail, consider reaching out to them through email, social media, or your mobile banking applications. This will not only help increase the visibility of the offer, but also allow consumers to receive and respond faster. Another way to enhance your banking strategies for growth while meeting consumer expectations for digital is by making it easier and more convenient for consumers to onboard. With an automated and data-driven credit decisioning solution, you can streamline steps that are traditionally manual and time-consuming, such as data collection and identity verification. By providing seamless customer acquisition in banking, you can accelerate your decision-making and increase the likelihood of conversion. Make the most of your marketing spend While customer acquisition in banking should remain a high priority, we understand that driving growth on a tight marketing budget can be challenging. That’s why we created a tip sheet outlining ways for banks and other lenders to enhance their customer acquisition processes while effectively managing costs. Some of the tips include: Going beyond conventional scoring methods. By leveraging an advanced customer acquisition solution, you can gain a holistic view of your prospective customers to enhance predictive performance and identify hidden growth opportunities. Focusing on high-potential customers. Pinpointing consumers who are actively seeking credit enables you to focus your offers and resources on those who are likely to respond, resulting in a greater return on marketing investment. Amplifying your credit offers. Re-presenting preapproved credit offers through the digital channels that consumers most frequent enables you to expand your campaign reach, increase response rates, and reduce direct mailing costs. View the tip sheet to learn how you can make the most of your marketing budget to acquire new customers and drive long-term growth. Access tip sheet


