Insurance — Fraud Detection from Claims Data

Overview

The primary purpose of this DAG is to identify fraudulent activities within insurance claims data. It ingests various data sources, including historical claims data, investigation reports, and customer profiles. The ingestion pipeline extracts and preprocesses this data to ensure quality and consistency. The processing steps involve applying machine learning algorithms to classify claims based on risk factors, followed by anomaly detection to identify unusual patterns that may suggest fraudulent

The primary purpose of this DAG is to identify fraudulent activities within insurance claims data. It ingests various data sources, including historical claims data, investigation reports, and customer profiles. The ingestion pipeline extracts and preprocesses this data to ensure quality and consistency. The processing steps involve applying machine learning algorithms to classify claims based on risk factors, followed by anomaly detection to identify unusual patterns that may suggest fraudulent behavior. Alerts are generated for claims flagged as suspicious, enabling timely investigation. The outputs of this DAG include a dashboard with visual representations of detected anomalies, detailed reports on flagged claims, and a secure repository of processed data. Monitoring key performance indicators (KPIs) such as the number of alerts generated, false positive rates, and investigation outcomes ensures the effectiveness of the fraud detection process. The business value lies in reducing financial losses due to fraud, improving operational efficiency, and enhancing the overall integrity of the insurance claims process.

Part of the Fraud & Anomaly Analytics solution for the Insurance industry.

Use cases

• Reduces financial losses from fraudulent claims
• Enhances operational efficiency in claims processing
• Improves detection accuracy with machine learning
• Strengthens compliance with regulatory requirements
• Increases customer trust through fraud prevention