Case 122024Big Data Engineer & ML Engineer

Real-Time Fraud Detection System

Real-time ML pipeline

Streaming fraud detection over transaction data. Kafka for ingest, Spark for scoring in flight, Cassandra for state, and a dashboard on Spring Boot and Flask. The design constraint was scoring a transaction before it settles rather than flagging it afterwards.

SparkKafkaCassandraSpring BootFlaskPython+1

Real-Time Fraud Detection System mockup 1

1 / 2

10,000+ transactions processed

Real-time fraud classification

100 customer profiles analyzed

SnapshotAt a glance

Big Data Engineer & ML Engineer

3 months

Outcomes

10,000+ transactions processed

Real-time fraud classification

100 customer profiles analyzed

Technologies Used

7 Technologies Integrated

Spark

Kafka

Cassandra

Spring Boot

Flask

Python

Java

95.7%

Impact

Key Features

Feature Implementation

6 Features

90%

Feature CoverageProject Scope

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

Engineered 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 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.

Inspiration

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

Solution

Optimized Spark ML pipeline with efficient preprocessing and real-time Kafka streaming integration.