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.
* 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
