Fraud Detection System

A sophisticated real-time transaction monitoring application using machine learning to detect fraudulent activities.

JavaJavaFXMachineLearningWekaH2DatabaseMavenDataAnalysisRealTime
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Fraud Detection System

Problem

Smaller financial institutions needed a fraud detection tool they could run without cloud infrastructure or specialist ML teams — existing solutions were expensive, online-only, and opaque in their decision logic.

Solution

Built a self-contained JavaFX desktop app that loads transaction datasets, trains a Weka ML classifier in-process, and surfaces real-time fraud alerts stored in an embedded H2 database — fully offline and auditable.

Achievement

  • Real-time transaction classification with Weka
  • Embedded H2 database for tamper-evident audit logs
  • Fully offline — no cloud dependencies
  • Transparent decision logic via Weka model inspection

The Fraud Detection System is a desktop application that empowers smaller institutions to flag suspicious transactions in real time using machine learning — without requiring cloud infrastructure, an internet connection, or specialist ML expertise.

How It Works

  1. User loads a CSV of transactions into the application
  2. Weka trains a classifier on labelled training data
  3. Each new transaction is scored in real time and flagged if above the fraud threshold
  4. All results are persisted in the H2 database for audit review

Key Features

Technology Stack

Built With