Tag: credit risk strategies

Changes in your portfolio are a constant. To accelerate growth while proactively identifying risk, you’ll need a well-informed portfolio risk management strategy. What is portfolio risk management? Portfolio risk management is the process of identifying, assessing, and mitigating risks within a portfolio. It involves implementing strategies that allow lenders to make more informed decisions, such as whether to offer additional credit products to customers or identify credit problems before they impact their bottom line. Leveraging the right portfolio risk management solution Traditional approaches to portfolio risk management may lack a comprehensive view of customers. To effectively mitigate risk and maximize revenue within your portfolio, you’ll need a portfolio risk management tool that uses expanded customer data, advanced analytics, and modeling. Expanded data. Differentiated data sources include marketing data, traditional credit and trended data, alternative financial services data, and more. With robust consumer data fueling your portfolio risk management solution, you can gain valuable insights into your customers and make smarter decisions. Advanced analytics. Advanced analytics can analyze large volumes of data to unlock greater insights, resulting in increased predictiveness and operational efficiency. Model development. Portfolio risk modeling methodologies forecast future customer behavior, enabling you to better predict risk and gain greater precision in your decisions. Benefits of portfolio risk management Managing portfolio risk is crucial for any organization. With an advanced portfolio risk management solution, you can: Minimize losses. By monitoring accounts for negative performance, you can identify risks before they occur, resulting in minimized losses. Identify growth opportunities. With comprehensive consumer data, you can connect with customers who have untapped potential to drive cross-sell and upsell opportunities. Enhance collection efforts. For debt portfolios, having the right portfolio risk management tool can help you quickly and accurately evaluate collections recovery. Maximize your portfolio potential Experian offers portfolio risk analytics and portfolio risk management tools that can help you mitigate risk and maximize revenue with your portfolio. Get started today. Learn more

It's no secret that the banking industry is essential to a thriving economy. However, the nature of the industry makes it prone to various risks that can have significant consequences. Therefore, effective and efficient risk management is vital for mitigating these risks and enhancing the stability of the banking sector. This is where risk management in banking comes in. Let’s look at the importance of risk management in banking and its role in mitigating risks in the industry. What is risk management in banking? Risk management in banking is an approach used by financial institutions to manage risks associated with banking operations. Establishing a structured risk management process is essential to identifying, evaluating and controlling risks that could affect your operations. The process involves developing and implementing a comprehensive risk management framework consisting of several components, including risk assessment, mitigation, monitoring and reporting. Importance of banking risk management Banks face risks from every angle – changing customer behaviors, fraud, uncertain markets, and regulatory compliance, making banking risk management critical for the stability of financial institutions. There are various risks associated with the industry, including:  Credit risk: The probability of a financial loss resulting from a borrower's failure to repay a loan, which results in an interruption of cash flows and increased costs for collection. How to mitigate: Leverage advanced analytics, data attributes, and predictive models to improve predictability, manage portfolio risk, make better decisionsand acquire the best customers. Market risk:The likelihood of an investment decreasing in value because of market factors (I.e., changes in interest rates, geopolitical events or recessions). How to mitigate: While it is impossible to eliminate market risk, you can diversify your assets, more accurately determine your risk threshold and stay informed on economic and market conditions.  Liquidity risk:The risk that an organization cannot meet its short-term liabilities and financial payment obligations. How to mitigate: More regularly forecast your cash flow and conduct stress tests to determine potential risk scenarios that would cause a loss of liquidity and how much liquidity would be lost in each instance.  Operational risk:Potential sources of losses that result from inadequate or failed internal processes (I.e., poorly trained employees, a technological breakdown, or theft of information). How to mitigate: Hire the right staff and adequately train them, stay up to date with cybersecurity threats and automate processes to reduce human error. Reputational risk: The potential that negative publicity regarding business practices, whether true or not, will cause a decline in the customer base, costly litigation or revenue reductions. How to mitigate: Define your bank’s core ethical values and relay them to stakeholders and employees. You should also develop a reputational management strategy and contingency plan in case a reputation-affecting incident occurs. Risk management in banking best practices Successful banks embrace risks while developing powerful mechanisms to prevent or manage them and stay ahead. By taking a proactive approach and leveraging risk management tools, you can minimize losses, enhance stability and grow responsibly.  The steps for implementing a banking risk management plan, include:  Risk identification and assessment: Financial institutions need to identify potential risks associated with their operations and assess the severity and impact of these risks. Risk mitigation: Once risks have been identified and assessed, financial institutions can implement strategies to mitigate the effects of these risks. There are several strategies for risk mitigation, including risk avoidance, reduction, acceptance and transfer. Risk monitoring and reporting: One of the fundamental principles of a banking risk management strategy is ongoing monitoring and reporting. Financial institutions should continually monitor their operations to identify evolving risks and develop mitigation strategies. Generating reports about the progress of the risk management program gives a dynamic view of the bank’s risk profile and the plan’s effectiveness. Several challenges may arise when implementing a risk management strategy. These include new regulatory rules or amendments, cybersecurity and fraud threats, increased competition in the sector, and inefficient resources and processes. An effective risk management plan serves as a roadmap for improving performance and allows you to better allocate your time and resources toward what matters most.  Benefits of implementing a risk management strategy Banks must prioritize risk management to stay on top of the various critical risks they face every day. There are several benefits of taking a proactive approach to banking risk management, including:Improved efficiency: Enhance efficiency and deploy more reliable operations by identifying areas of weakness or inefficiencies in operational processes.Confident compliance: Ensure you comply with new and amended regulatory requirements and avoid costly fines. Enhanced customer confidence: Foster customer confidence to increase customer retention and mitigate reputational risk. Partnering to reduce risk and maximize growth Effective risk management is crucial for mitigating risks in the banking industry. By implementing a risk management framework, financial institutions can minimize losses, enhance efficiency, ensure compliance and foster confidence in the industry. At Experian, we have a team of experts dedicated to supporting our banking partners. Our team’s expertise paired with our innovative solutions can help you implement a powerful risk management process, as well as: Leverage data to reach company-wide business goals. Lower the cost of funds by attracting and retaining deposits. Protect your business against fraud and risk. Create less friction through automated decisioning. Grow your business portfolio and increase profitability. Learn more about our risk management solutions for banks and fraud risk solutions.

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.

Machine learning (ML) is a powerful tool that can consume vast amounts of data to uncover patterns, learn from past behaviors, and predict future outcomes. By leveraging ML-powered credit risk models, lenders can better determine the likelihood that a consumer will default on a loan or credit obligation, allowing them to score applicants more accurately. When applied to credit decisioning, lenders can achieve a 25 percent reduction in exposure to risky customers and a 35 percent decrease in non-performing loans.1 While ML-driven models enable lenders to target the right audience and control credit losses, many organizations face challenges in developing and deploying these models. Some still rely on traditional lending models with limitations preventing them from making fast and accurate decisions, including slow reaction times, fewer data sources, and less predictive performance. With a trusted and experienced partner, financial institutions can create and deploy highly predictive ML models that optimize their credit decisioning. Case study: Increase customer acquisition with improved predictive performance Looking to meet growth goals without increasing risk, a consumer goods retailer sought out a modern and flexible solution that could help expand its finance product options. This meant replacing existing ML models with a custom model that offers greater transparency and predictive power. The retailer partnered with Experian to develop a transparent and explainable ML model. Based on the model’s improved predictive performance, transparency, and ability to derive adverse action reasons for declines, the retailer increased sales and application approval rates while reducing credit risk. Read the case study Learn about our custom modeling capabilities 1 Experian (2020). The Art of Decisioning in Uncertain Times

Many financial institutions have made inclusion a strategic priority to expand their reach and help more U.S. consumers access affordable financial services. To drive deeper understanding, Experian commissioned Forrester to do new research to identify key focal points for firms and how they are moving the needle. The study found that more than two-thirds of institutions had a strategy created and implemented while one-quarter reported they are already up and running with their inclusion plans.1 Tapping into the underserved The research examines the importance of engaging new audiences such as those that are new to credit, lower-income, thin file, unbanked and underbanked as well as small businesses. To tap into these areas, the study outlines the need to develop new products and services, adopt willingness to change policies and processes, and use more data to drive better decisions and reach.2 Expanded data for improved risk decisioning The research underlines the use of alternative data and emerging technologies to expand reach to new audiences and assist many who have been underserved. In fact, sixty-two percent of financial institutions surveyed reported they currently use or are planning to use expanded data to improve risk profiling and credit decisions, with focus on: Banking data Cash flow data Employment verification data Asset, investments, and wealth management data Alternative financial services data Telcom and utility data3 Join us to learn more at our free webinar “Reaching New Heights Together with Financial Inclusion” where detailed research and related tools will be shared featuring Forrester’s principal analyst on Tuesday, May 24 from 10 – 11 a.m. PT. Register here for more information. Find more financial inclusion resources at www.experian.com/inclusionforward. Register for webinar Visit us 1 Based on Forrester research 2 Ibid. 3 Ibid.

The coronavirus (COVID-19) outbreak is causing widespread concern and economic hardship for consumers and businesses across the globe – including financial institutions, who have had to refine their lending and downturn response strategies while keeping up with compliance regulations and market changes. As part of our recently launched Q&A perspective series, Shannon Lois, Experian’s Head of DA Analytics and Consulting and Bryan Collins, Senior Product Manager, tackled some of the tough questions for lenders. Here’s what they had to say: Q: What trends and triggers should lenders be prepared to react to? BC: Lenders are still trying to figure out how to assess risk between the broader, longer-term impacts of the pandemic and the near-term Coronavirus Aid, Relief, and Economic Security (CARES) Act that extends relief funds and deferment to consumers and small businesses. Traditional lending processes are not possible, lenders will have to adjust underwriting strategies and workflows as they deploy hardship programs while complying with the Act. From a utilization perspective, lenders need to look for near-term trends on payments, balances and skipped payments. From an extension standpoint, they should review limits extended or reduced by other lenders. Critical trends to look for would be missed or late auto payments, non-traditional credit shopping and rental payment delinquencies. Q: What should lenders be doing to plan for an uptick in delinquencies? SL: First, lenders should make sure they have a complete picture of how credit risk and losses are evolving, as well as any changes to their consumers’ affordability status. This will allow a pointed refinement of their customer management strategies (I.e. payment holidays, changing customer to cheaper product, offering additional services, re-pricing, term amendment and forbearance management.) Second, given the increased stress on collection processes and regulations guidelines, they should ensure proper and prepared staffing to handle increased call volumes and that agency outsourcing and automation is enabled. Additionally, lenders should migrate to self-service and interactive communication channels whenever possible while adopting new segmentation schemas/scores/attributes based on fresh data triggers to queue lower risk accounts entering collections. Q: How can lenders best help their customers? SL: Lenders should understand customers’ profiles with vulnerability and affordability metrics allowing changes in both treatment and payment. Payment Holidays are common in credit card management, consider offering payment freezes on different types of credit like mortgage and secured loans, as well as short term workout programs with lower interest rates and fee suppression. Additionally, lenders should offer self-service and FAQ portals with information about programs that can help customers in times of need. BC: Lenders can help by complying with aspects of the CARES Act guidance; they must understand how to deploy payment relief and hardship programs effectively and efficiently. Data integrity and accuracy of loan reporting will be critical. Financial institutions should adjust their collection and risk strategies and processes. Additionally, lenders must determine a way to address the unbanked population with relief checks. We understand how challenging it is to navigate the changing economic tides and will continue to offer support to both businesses and consumers alike. Our advanced data and analytics can help you refine your lending processes and better understand regulatory changes. Learn more About Our Experts: Shannon Lois, Head of DA Analytics and Consulting, Experian Data Analytics, North America Shannon and her team of analysts, scientists, credit, fraud and marketing risk management experts provide results-driven consulting services and state-of-the-art advanced analytics, science and data products to clients in a wide range of businesses, including banking, auto, credit, utility, marketing and finance. Shannon has been a presenter at many credit scoring and risk management conferences and is currently leading the Experian Decision Analytics advisory board. Bryan Collins, Senior Product Manager, Experian Consumer Information Services, North America Bryan is a member of Experian's CIS product management team, focusing on the Acquisitions suite and our evolving Ascend Identity Services Platform. With more than 20 years of experience in the financial services and credit industries, Bryan has established strong partnerships and a thorough understanding of client needs. He was instrumental in the launch of CIS's segmentation suite and led product management for lender and credit-related initiatives in Auto. Prior to joining Experian, Bryan held marketing and consumer experience roles in consumer finance, business lending and card services.

The most recent Experian State of the Automotive Finance Market report shows more consumers are leasing vehicles. Leases accounted for 28.4 percent of all new vehicles financed in Q4 2013 - the highest level on record since 2006.