Predictive Modeling for True-Name Fraud
Financial institutions can lift consumer confidence, deter fraud attempts, and cut financial losses by predicting identity theft with modeling.
The risk and exposure associated with true-name fraud has sparked demand for advanced solutions that can identify this type of activity at the source: the time of application. By implementing real-time, cost effective solutions with a low false-positive rate, lenders will increase compliance with legislation. This paper includes background on identity theft and summarizes the performance of both the original and the refined models. It is designed to help financial institutions learn more about the challenges of risk modeling for fraud.
