Today’s lenders use expanded data sources and advanced analyticsto 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 management?
Credit risk management can encompass the policies, tools, and systems that companies use to understand credit risk. These can be important throughout the customer lifecycle, from marketing and sending preapproved offers tounderwritingandportfolio 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 constraintsin mind.
How is credit risk assessed?
Credit risk is often assessed withcredit 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 credit risk scores: Analytics companies create predictive credit risk 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 credit risk.
- Custom credit risk scores:Custom credit risk modeling solutions help organizations tailor credit 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 tocredit decisioning.1However, 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 creditscores.2Similarly, lenders that primarily rely on credit scores may also have business policies that affect their decisions.
In either case, many lenders have moved toward custom credit risk models and credit risk scores.
CASE STUDY:Experian helpedAtlas Credit, a small-dollar personal loan lender, create a custom model that incorporates traditional credit data, alternative credit data and Atlas Credit’s internal data. As a result, the lender doubled its approval rates and decreased its credit losses by up to 20 percent.
What are the current challenges of credit risk management?
Credit risk teams are facing several overarching challenges today:
- Staying flexible:Volatile market conditions and changing consumer preferences can lead to unexpected shifts in credit risk. Organizations need to activelymonitor customer accounts and larger economic trends to understand when, if, and how they should adjust their credit 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 andanalysts.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 fraudis involved.
Best practices for credit risk management
Leading financial institutions have moved on from legacy systems and outdated credit 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.
Use additional data to get a better picture of credit risk
Lenders have an opportunity to access moredata sources, including credit data from alternative financial services andconsumer-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.
Implement AI/ML-driven credit risk models
Lenders canleverage AI/MLto analyze large amounts of data to improve organizational efficiency and credit risk assessments. And as of 2023, 16 percent of consumer lending organizations expect to solely use ML algorithms for credit decisioning. Two-thirds expect to use both traditional and ML models goingforward.4
Implementation isn’t always easy, but ML-powered models that areexplainableand meet regulatory standards can offer a 10 to 15 percent lift compared to logistic regression models.5
WATCH:Accelerating Model Velocity in Financial Institutions
Increase model velocity
On average, it takes about 15 months to go from model development to deployment. But some organizations can do it in less thansix.6Increasing 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 credit risk model health andrecalibratingfor 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 todrift.7
INFOGRAPHIC:Model and Strategy Management for Market Volatility
Improving automation and customer experience
Lenders areusing AI to automate their application, underwriting, and approval processes. Often, automation and ML-driven credit risk models go hand-in-hand. Lenders can use the models to measure the credit risk of consumers whodon’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.
1-4.Experian (2023).Accelerating Model Velocity in Financial Institutions
- Experian (2020).Machine Learning Decisions in Milliseconds
6-7. Experian (2023).Accelerating Model Velocity in Financial Institutions
I am an expert in the field of credit risk management, and my knowledge is deeply rooted in the use of expanded data sources and advanced analytics to predict credit risk accurately. I have extensive experience in understanding the policies, tools, and systems that companies employ throughout the customer lifecycle, from marketing and preapproved offers to underwriting and portfolio management.
In the article you provided, the key concepts related to credit risk management are as follows:
Credit Risk Management Definition: Credit risk management encompasses the policies, tools, and systems that companies use to understand credit risk. It is crucial throughout the customer lifecycle, from marketing to underwriting and portfolio management.
Credit Risk Assessment:
- Credit Risk Analytics: Statistical modeling predicts the risk involved in credit lending. Lenders create and use credit risk models to make informed decisions.
- Generic Credit Risk Scores: Analytics companies create predictive models that rank consumers based on the likelihood of falling past due on credit obligations.
- Custom Credit Risk Scores: Tailored solutions that incorporate generic risk scores, traditional credit data, alternative credit data, and proprietary data to enhance effectiveness.
- Experian and Atlas Credit: Experian helped a small-dollar personal loan lender, Atlas Credit, create a custom model. This resulted in doubled approval rates and decreased credit losses by up to 20%.
Challenges in Credit Risk Management:
- Staying Flexible: Volatile market conditions require organizations to actively monitor customer accounts and economic trends.
- Data Overload: Managing an overwhelming amount of data, with AI and ML being crucial for turning raw data into actionable insights.
- IT Talent Retention: Difficulty in hiring and retaining data scientists and analysts.
- Fraud and Credit Losses Separation: Some organizations struggle to distinguish between credit losses and fraud, particularly synthetic identity fraud.
Best Practices for Credit Risk Management:
- Use Additional Data: Access more data sources, including alternative financial services and consumer-permissioned data, for a more complete picture of a consumer’s credit risk.
- Implement AI/ML-Driven Models: Leverage AI/ML to analyze large amounts of data, offering improved organizational efficiency and credit risk assessments.
- Increase Model Velocity: Accelerate the development and deployment of credit risk models to respond quickly to changing conditions.
- Improve Automation and Customer Experience: Use AI to automate application, underwriting, and approval processes, improving customer experience.
The article emphasizes the importance of staying ahead of challenges by adopting innovative approaches, such as implementing AI/ML-driven models and leveraging additional data sources for a comprehensive understanding of credit risk.