Insurance — Fraud Detection through Claims Document Analysis

Overview

The purpose of this DAG is to extract and analyze claims documents to detect anomalies that may indicate fraudulent activities within the insurance sector. The data sources include claims documentation, historical fraud cases, and machine learning models that enrich the dataset for improved detection accuracy. The ingestion pipeline begins with the collection of claims documents, followed by data preprocessing to standardize formats and extract relevant features. Next, the processing logic appli

The purpose of this DAG is to extract and analyze claims documents to detect anomalies that may indicate fraudulent activities within the insurance sector. The data sources include claims documentation, historical fraud cases, and machine learning models that enrich the dataset for improved detection accuracy. The ingestion pipeline begins with the collection of claims documents, followed by data preprocessing to standardize formats and extract relevant features. Next, the processing logic applies machine learning algorithms to identify patterns and anomalies in the data, flagging potential fraud cases for further investigation. Quality controls are implemented throughout the pipeline to ensure data integrity and accuracy, including validation checks and anomaly detection thresholds. The outputs of this DAG include detailed reports of flagged claims, a dashboard for real-time monitoring, and a governance log that tracks decision-making processes. Key performance indicators (KPIs) for monitoring include the number of claims analyzed, fraud detection rate, and time taken to investigate flagged cases. The business value of this DAG lies in its ability to reduce fraudulent payouts, enhance operational efficiency, and improve overall risk management within the insurance industry.

Part of the Recommendations solution for the Insurance industry.

Use cases

• Reduces financial losses from fraudulent claims
• Enhances operational efficiency through automation
• Improves risk management and decision-making
• Provides real-time insights for proactive measures
• Strengthens compliance with regulatory requirements