Real time Claims fraud detection for a large insurance provider in US
Overview.
A large US insurance provider faces significant financial losses due to fraudulent claims. Traditional methods of detecting fraud are often reactive and rely on manual review after a claim is submitted. This reactive approach allows fraudulent claims to be paid out before detection, impacting the company's bottom line and potentially raising insurance premiums for honest customers.
This solution utilizes a robust model ensemble approach to identify potential fraudulent transactions.
The system combines various machine learning or statistical models trained to identify patterns and red flags indicative of fraudulent claims.
This ensemble approach leverages the strengths of different models to achieve more accurate and comprehensive fraud detection than any single model could provide.
The Impact.
The fraud detection frequency jumped to 6.5%.
A potential revenue increase of up to 3 times the value of the prevented fraudulent claim
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