Legacy scoring models are ineffective at identifying fraudulent applications. Many institutions use rules based scoring methods and third party data to make decisions. Misclassified bad debt is charged off as a credit loss rather than fraud. Bad debt charge-offs due to first-party fraud may be as high as 20% consumer, non-mortgage losses.
Goals and Objectives
Financial institutions will invest in fraud prevention technology that will provide a complete view of the applicant. FIs will seek out platforms or best-of-breed solutions that collect and analyze numerous data elements about the applicant. The outcome will be a score that indicates probable level of trust.
Alternative data services, AI/machine learning, Analytics, Big Data, Cloud, Managed services
Use Case Summary
FIs will use advanced identity analytic offerings to identify likely first-party fraud to prevent early-pay defaults and bust-out schemes.