At A Glance
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.Credit risk refers to the likelihood that a borrower will fail to repay a debt as agreed. Credit risk management is the art and science of utilizing risk mitigation tools to minimize losses while maximizing profits from lending activities.
Lenders can establish credit underwriting criteria for each of their products and utilize risk-based pricing to adjustthe terms of a loan or line of credit based on the risk associated with the product and borrower. Credit portfolio management extends beyond originations and individual decisions to encompass portfolios as a whole.
Why is credit risk management important?
Continuously 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, excluding borrowers who would have repaid as agreed. Expanding a credit box might increase approval rates, but it is only beneficial if the profit from good new loans exceeds credit losses.
Fraud is also on the rise and becoming increasingly complex, making fraud management a crucialpart 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 examine payment activity alone, it may be challenging to classify the loss as eithera fraud loss or a 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 minimizelosses. 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. Open banking data enables lenders to more accurately assessa 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 utilize 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.
- Use multiple data sources: Alternative credit dataand consumer-permissioned data offer increased and real-time visibility into borrowers’ creditworthiness to help lenders more accurately assess credit risk. These additional data sources can score those who are unscoreable by conventional models and help fuel ML credit risk models.
Experian helps lenders measure and manage credit risk
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 utilizes both traditional and alternative data to score up to 96 percent of U.S. consumers — compared to the 81 percent that conventional models can score.¹ 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.
¹Experian (2023). Lift Premium™ and Lift Plus™
