Banking — Fraud Detection in Financial Transactions Using Machine Learning

Overview

The purpose of this DAG is to detect fraudulent transactions within the banking sector by leveraging machine learning techniques. It ingests transactional data from internal systems and transaction logs, ensuring a comprehensive analysis of financial activities. The ingestion pipeline begins with data extraction from various sources, including ERP transaction logs and internal databases. Following ingestion, the data undergoes a series of transformation steps, including data cleaning and normali

The purpose of this DAG is to detect fraudulent transactions within the banking sector by leveraging machine learning techniques. It ingests transactional data from internal systems and transaction logs, ensuring a comprehensive analysis of financial activities. The ingestion pipeline begins with data extraction from various sources, including ERP transaction logs and internal databases. Following ingestion, the data undergoes a series of transformation steps, including data cleaning and normalization, to prepare it for model training. Machine learning models are then trained on historical transaction data to identify patterns indicative of fraud. The models are rigorously validated to ensure accuracy and reliability, with quality control measures in place to maintain the integrity of sensitive data. Outputs of this DAG include real-time fraud alerts and detailed reports on detected anomalies. Key performance indicators (KPIs) such as false positive rates and response times to alerts are monitored to evaluate the effectiveness of the fraud detection system. The business value of this solution lies in its ability to significantly reduce financial losses due to fraud, enhance customer trust, and streamline compliance with regulatory requirements.

Part of the Document Automation solution for the Banking industry.

Use cases

• Minimizes financial losses from fraudulent activities
• Enhances customer trust and satisfaction
• Improves compliance with regulatory standards
• Streamlines operational efficiency in fraud management
• Provides actionable insights for risk assessment