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Is there a formula for success when building portfolio-specific models?

Published: July 30, 2009 by Guest Contributor

By: Wendy Greenawalt

When consulting with lenders, we are frequently asked what credit attributes are most predictive and valuable when developing models and scorecards. Because we receive this request often, we recently decided to perform the arduous analysis required to determine if there are material differences in the attribute make up of a credit risk model based on the portfolio on which it is applied.

The process we used to identify the most predictive attributes was a combination of art and sciences — for which our data experts drew upon their extensive data bureau experience and knowledge obtained through engagements with clients from all types of industries. In addition, they applied an empirical process which provided statistical analysis and validation of the credit attributes included. Next, we built credit risk models for a variety of portfolios including bankcard, mortgage and auto and compared the credit attribute included in each.

What we found is that there are some attributes that are inherently predictive regardless for which portfolio the model was being developed. However, when we took the analysis one step further, we identified that there can be significant differences in the account-level data when comparing different portfolio models.

This discovery pointed to differences, not just in the behavior captured with the attributes, but in the mix of account designations included in the model. For example, in an auto risk model, we might see a mix of attributes from all trades, auto, installment and personal finance…as compared to a bankcard risk model which may be mainly comprised of bankcard, mortgage, student loan and all trades.  Additionally, the attribute granularity included in the models may be quite different, from specific derogatory and public record data to high level account balance or utilization characteristics.

What we concluded is that it is a valuable exercise to carefully analyze available data and consider all the possible credit attribute options in the model-building process – since substantial incremental lift in model performance can be gained from accounts and behavior that may not have been previously considered when assessing credit risk.

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Today’s changing economy is directly impacting consumers’ financial behaviors, with some individuals doing well and some showing signs of payment stress. And while these trends may pose challenges to financial institutions, such as how to expand their customer base without taking on additional risk, the right credit attributes can help them drive smarter and more profitable lending decisions. With Experian’s industry-leading credit attributes, organizations can develop precise and explainable acquisition models and strategies. As a result, they can: Expand into new segments: By gaining deeper insights into consumer trends and behaviors, organizations can better assess an individual’s creditworthiness and approve populations who might have been overlooked due to limited or no credit history. Improve the customer experience: Having a wider view of consumer credit behavior and patterns allows organizations to apply the best treatment at the right time based on each consumer’s specific needs. Save time and resources: With an ongoing managed set of base attributes, organizations don’t have to invest significant resources to develop the attributes themselves. Additionally, existing attributes are regularly updated and new attributes are added to keep pace with industry and regulatory changes. Case study: Enhance decision-making and segmentation strategies A large retail credit card issuer was looking to grow their portfolio by identifying and engaging more consumers who met their credit criteria. To do this, they needed to replace their existing custom acquisition model with one that provided a granular view of consumer behavior. By partnering with Experian, the company was able to implement an advanced custom acquisition model powered by our proprietary Trended 3DTM and Premier AttributesSM. Trended 3D analyzes consumers’ behavior patterns over time, while Premier Attributes aggregates and summarizes findings from credit report data, enabling the company to make faster and more strategic lending decisions. Validations of the new model showed up to 10 percent improvement in performance across all segments, helping the company design more effective segmentation strategies, lower their risk exposure and approve more accounts. To learn how Experian can help your organization make the best data-driven decisions, read the full case study or visit us. Download case study Visit us

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