Retail Banking — Fraud Detection

Problem Statement  

In the banking sector, detecting fraud in real-time transactions is crucial but often challenging due to the complexity and volume of data involved. Traditional platforms may struggle to deliver timely insights, leading to potential financial losses and security risks.

Solution Overview

Databricks offers a solution through its Lakehouse Platform, combining data lakes and data warehouses to build a real-time fraud detection system for banking transactions. The platform enables the ingestion of data from external systems like EPR/Salesforce, transformation using Delta Live Tables (DLT) for reliable data processing pipelines, secure data governance, and the utilization of Databricks SQL for dashboard creation and fraud analysis. Additionally, machine learning models developed with Databricks AutoML flag risky transactions, and Databricks Model Serving deploys a REST API for real-time inferences with model A/B testing. Orchestration of these steps is managed with Databricks Workflows.

Tech Stack leveraged

  • Databricks Lakehouse Platform
  • Delta Live Tables (DLT)
  • Databricks SQL
  • Databricks AutoML
  • Databricks Model Serving
  • Databricks Workflows

Benefits Delivered

  • Real-time fraud detection for banking transactions
  • Efficient data ingestion and transformation
  • Secure data governance and compliance
  • Machine learning model deployment for risk flagging
  • Real-time inferences through REST API
  • Streamlined orchestration with Databricks Workflows

This case study showcases how Databricks’ Lakehouse Platform can be leveraged in the banking sector to enhance fraud detection capabilities, streamline data processing, and improve security measures in real-time transaction monitoring.