An insurance company will process several hundred thousand legitimate claims, and only a handful of fraudulent ones. Yet, these fraudulent claims impact both the insurer as well as the insurance ecosystem as a whole – from those evaluating insurance products to current customers to specialists processing claims. Insurers are justified in their desire to root out fraud, waste, and abuse as these claims cost insurers an estimated $40 billion per year.
This content highlights how insurers can leverage machine learning for claims fraud detection.
• Traditional fraud detection methods.
• Predictive modelling usage.
• How effective monitoring identifies increasingly accurate fraudulent patterns.
• Handling imbalanced data.
• A high level view of the modelling process.