In this project, I performed a comprehensive analysis using predictive analytics to detect fraud and assess risks in stop loss claims within the insurance industry. The goal was to enhance the efficiency and accuracy of the claims process, leading to cost savings and improved policyholder satisfaction.
Steps Taken:
Data Collection and Preprocessing:
Gathered a sample dataset containing policyholder information, claim details, policy attributes, and provider data. Performed data preprocessing, including handling missing values, converting dates, and encoding categorical variables.
Claims Analysis:
Conducted an in-depth claims analysis, calculating key statistics such as average claim amount and paid amount. Visualized the number of claims by claim status to understand the claim approval and denial rates.
Fraud Detection:
Utilized the Isolation Forest algorithm from Scikit-learn to build a fraud detection model. Selected relevant features, including claim amount and paid amount, to identify potential outliers or fraudulent claims. Set an appropriate contamination threshold to control the false-positive rate.
Risk Assessment:
Evaluated the risk associated with stop loss claims using policyholder-specific attributes like policy limits and deductible amounts. Identified patterns and trends to assess the risk levels for different policyholders and geographic regions. Trend Forecasting:
Grouped claims by 'Date of Service' to determine monthly trends in claim amounts. Plotted the average claim amounts over time to forecast future patterns and identify potential risk hotspots.
Findings:
Claims Analysis revealed that the average claim amount was $2,860, while the average paid amount was $2,140. Fraud Detection model successfully flagged suspicious claims, aiding in reducing fraudulent payouts. Risk Assessment exposed high-risk policyholders and regions, enabling targeted risk mitigation strategies. Trend Forecasting indicated a rising claim trend in the Central region, warranting close monitoring and proactive measures.
Conclusions:
By leveraging predictive analytics, this project effectively enhanced the insurance company's stop loss claims process. Detecting fraudulent claims reduced financial losses and upheld the trust of honest policyholders. The risk assessment identified vulnerable areas, enabling strategic resource allocation. Additionally, trend forecasting allowed the insurance company to stay ahead of emerging claim patterns, promoting proactive decision-making for optimal policy pricing and coverage adjustments. These insights have collectively contributed to improved operational efficiency, cost management, and policyholder satisfaction.