Insurers depend on prebuilt business rules, workflow, and information collected from customers to detect fraud when, at the same time, customers are now expecting offers to be immediately issued for on-the-spot contract emission, leaving little space for deep fraud risk analysis.
Goals and Objectives
Use multiple internal and external data sources as well as image analysis tools to verify the reliability of the information/data provided to describe the risk and to run predictive models aimed at flagging the 2% to 3% of cases with high fraud risk and enable straight-through processing of the remaining 97% to 98% non-suspect cases.
IoT, Advanced analytics, Cognitive technologies, Cloud, Fraud visualization, fraud ring identification, Document forgery detection
Use Case Summary
Leverage digital technologies/AI to automatically check in real time for potential fraudulent underwriting cases. Accelerate the processing of no-fraud risks. Slow down and ultimately stop the processing of fraudulent cases.