Insurance — Fraud Detection in Claims Processing Pipeline

Overview

The purpose of this DAG is to leverage machine learning models to analyze insurance claims and identify potential fraud. It ingests data from claims management systems and historical databases, ensuring a comprehensive view of claims activity. The ingestion pipeline begins with data extraction from these sources, followed by data cleansing and preparation to ensure high-quality input for the models. The next step involves training machine learning algorithms on historical claims data, allowing t

The purpose of this DAG is to leverage machine learning models to analyze insurance claims and identify potential fraud. It ingests data from claims management systems and historical databases, ensuring a comprehensive view of claims activity. The ingestion pipeline begins with data extraction from these sources, followed by data cleansing and preparation to ensure high-quality input for the models. The next step involves training machine learning algorithms on historical claims data, allowing the system to learn patterns associated with fraudulent claims. After training, the models are evaluated for performance, focusing on metrics such as precision and recall to minimize false positives. Quality control measures are implemented, including bias detection and drift analysis, to maintain the integrity of the models over time. The outputs of this DAG include flagged claims for manual review, detailed reports on fraud detection rates, and insights into model performance. Key performance indicators (KPIs) monitored include the fraud detection rate and the number of false positives, which are crucial for assessing the effectiveness of the system. By automating the fraud detection process, this DAG delivers significant business value, reducing operational costs, increasing the speed of claims processing, and enhancing customer trust through improved accuracy.

Part of the Data & Model Catalog solution for the Insurance industry.

Use cases

• Reduces operational costs through automation of fraud detection
• Increases claims processing speed and efficiency
• Enhances accuracy in identifying fraudulent claims
• Improves customer trust and satisfaction
• Provides actionable insights for risk management