Ml Powered Insurance Fraud Detection With Explainability

Abstract

One Major Issue Facing The Banking Industry Is Insurance Fraud., Leading To Substantial Monetary Losses And Increased Premiums For Honest Policyholders. Traditional Rule-based Fraud Detection Systems Struggle To Adapt To Evolving Fraudulent Tactics, Necessitating The Adoption Of Machine Learning (ML) Techniques For More Effective Fraud Detection. This Research Explores An ML-powered Fraud Detection Framework That Leverages Supervised And Unsupervised Learning Models To Identify Fraudulent Claims With High Accuracy. To Enhance Trust And Transparency, Explainability Strategies Like LIME (Local Interpretable Model-agnostic Explanations) And SHAP (Shapley Additive Explanations) Are Combined., Allowing Investigators To Interpret Model Predictions. The Study Examines Various ML Algorithms, Evaluates Their Performance Using Real- World Insurance Datasets, And Demonstrates How Explainability Improves Decision-making By Offering Information On Fraud Patterns. Our Findings Suggest That An Explainable ML Approach Not Only Enhances Fraud Detection Rates But Also Reduces False Positives, Ensuring Fair Claim Processing. This Research Contributes To The Field Of Financial Fraud Detection By Presenting An Interpretable And Adaptive ML-driven Solution That Balances Accuracy, Efficiency, And Transparency.

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