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Financial Fraud Detection System

Name: Financial Fraud Detection System
Author: Harsh Mehta

Real-time fraud detection using advanced ML algorithms

FinTech

Case study

Problem: Rule-only fraud stacks generated noise and missed adaptive attack patterns across high-volume transactions.
Approach: Built streaming feature pipelines with ML scoring, explainability hooks, and analyst workflows for review and feedback loops.
Outcome: High-precision flagging with ~99.5% model accuracy on benchmarks and faster investigation cycles for the risk team.

Details

Developed a real-time fraud detection system that analyzes transaction patterns and flags suspicious activities with 99.5% accuracy, protecting financial institutions from fraud.

Technologies Used

PythonScikit-learnApache SparkKafkaReact