Real-Time Fraud Detection System
Real-time ML pipeline
Real-Time Fraud Detection was forged from urgency: threats shouldn't wait for analysis. Built on the backbone of Spark, Kafka, and Cassandra, I created a pipeline where every transaction is watched, every pattern is learned, and every fraud attempt is caught - all in the blink of an eye.
1 / 210,000+ transactions processed
Real-time fraud classification
100 customer profiles analyzed
SnapshotAt a glance
Big Data Engineer & ML Engineer
3 months
10,000+ transactions processed
Real-time fraud classification
100 customer profiles analyzed
Technologies Used
7 Technologies Integrated
Impact
Key Features
Feature Implementation
Project Vision
Detect fraudulent transactions in real-time with high accuracy.
Core Process
The process of Developing it.
Simulated 100 customers and 10K+ transactions, stored in Cassandra via Spark SQL, trained Random Forest model with Spark ML, implemented Kafka streaming for real-time prediction, and built dashboard with Spring Boot.
Build notesWhat I built
03Engineered a real-time fraud detection pipeline using Spark ML with Random Forest classifier, processing 10,000+ transactions from 100 customer profiles stored in Cassandra, achieving real-time fraud classification by optimizing Spark SQL queries and implementing efficient preprocessing workflows.
Built a Kafka streaming ingestion system that enables real-time transaction monitoring, integrating with Spark ML pipeline for instant fraud prediction, creating a system where every transaction is watched, every pattern is learned, and every fraud attempt is caught in the blink of an eye.
Developed a comprehensive dashboard using Spring Boot and Flask REST APIs, providing real-time fraud detection insights and transaction analytics, enabling financial institutions to monitor and respond to fraudulent activities immediately with high accuracy and minimal latency.
Need for real-time fraud detection in financial transactions.
Features
- Spark ML pipeline
- Kafka streaming ingestion
- Real-time fraud classification
- Cassandra data storage
- Spring Boot dashboard
- Flask REST APIs
Challenges
- Real-time processing latency
- Model accuracy optimization
- Streaming data integration
