One of the largest insurance providers in the world
The insurer had a manual triaging process to identify potentially fraudulent claims which comprised of rule-based engine to raise alerts and a large team to triage these alerts and select the candidate claims for further investigation. The objectives were as follows:
• Automate the triaging process with the AI model validating fraudulent claim alerts using multivariate data.
• Eliminate the black-box nature of the ML solution and explain the factors deciding the results for the claims.
A machine learning solution was created to identify the patterns in the historical data and create triaging predictions on new incoming claims including:
Reduced the number of alerts to be reviewed by triage team by 90%, resulting in lowered cost of operations.
The explainability module indicated the important features influencing the predictions.
Ability to assess if features that should not impact predictions were doing so.
Improved the consistency of prediction leading to 98% accuracy against the human triaging, with no deviations in false negatives.