Equifax®  today announced the launch of Credit Abuse Risk, a new predictive model that uses FCRA-regulated data and is designed to help protect lenders against first-party fraud and drive more confident lending decisions.

As the financial impact of first-party fraud continues to rise, Credit Abuse Risk was developed to uncover atypical patterns indicative of two types of fraudulent activities: loan stacking, when an individual quickly applies for multiple loans with no intent to repay those loans, and credit washing, when a person tries to remove accurate, but negative information from a credit report. These patterns can be identified during prequalification offers, account origination, or portfolio review. This allows lenders to modify loan terms based on FCRA-compliant insights.

"By focusing on application behavior in real-time, Credit Abuse Risk quickly helps to reduce the potential for fraud and related costs," said Felipe Castillo, Chief Product Officer for U.S. Information Solutions at Equifax. "This supports a more confident lending environment, and helps keep credit available for consumers."

Credit Abuse Risk features include:

• Enhanced insights: The model focuses on behavioral indicators that provide a clear view of atypical credit activity.
• Targeted decisioning: Specifically designed to address the lifecycle of fraud, from the building of inflated credit profiles to a sudden influx of disputes on unpaid accounts that falls outside the normal range, without limiting the important consumer protections to correct inaccurate or incomplete credit data.
• Comprehensive portfolio protection: Provides lenders with important insights across all credit tiers.
• Actionable intelligence: Allows lenders to make real-time, regulated decisions on credit terms for a consumer, providing an FCRA-compliant score with adverse action reason codes.

A Comprehensive, Layered Defense
Credit Abuse Risk is a vital component of the Equifax layered fraud defense strategy to help inform better lending decisions. It works alongside Synthetic Identity Risk tools to provide a complete view of identity legitimacy and hidden repayment risk.