Fraud Detection Dashboard

Real-time anomaly detection system for identifying suspicious financial transactions.

Published January 10, 2024

1 min read

Technologies Used

Kafka

Spark Streaming

TensorFlow

React

PostgreSQL

Security dashboard highlighting flagged transactions and risk scores

Instant Threat Detection at Scale

In the high-speed world of digital finance, every millisecond counts. We built a Fraud Detection Dashboard that processes millions of transactions per second, using a hybrid model of rule-based logic and deep learning to identify suspicious activity instantly. This system empowered the bank's security team to stop fraud before it happened, saving millions in potential losses.

The Challenge

Legacy systems producing high rates of false positives (blocking legitimate users).

Inability to detect novel fraud patterns (zero-day attacks).

Latency in processing causing delays in transaction approval.

Fragmented view of user behavior across devices.

The Solution

Implemented an Apache Kafka pipeline for low-latency data ingestion.

Deployed a hybrid AI model combining expert rules with an autoencoder for anomaly detection.

Built a real-time React dashboard for analysts to review flagged cases.

Integrated device fingerprinting to track users across sessions.

Key Features

Real-Time Scoring

Assign a risk score to every transaction in under 50ms.

Behavioral analysis
Geolocation velocity
Device reputation

Visual Investigation

Graph-based visualization to trace funds and linked accounts.

Transaction graph
Identity resolution
Case management

Adaptive Learning

Models retrain automatically on confirmed fraud cases.

Feedback loop
Model versioning
A/B testing rules

Technology Stack

Kafka

Spark Streaming

TensorFlow

React

PostgreSQL

Project Timeline & Results

Week 1-4

Infrastructure & Ingestion

Set up Kafka cluster handling 10k events/sec.

Architected the streaming data pipeline. Connected to the core banking ledger and card processor feeds. Implemented data validation and normalization layers.

Week 5-8

Model Development

Achieved 92% detection rate with <1% false positives.

Trained the anomaly detection models using historical fraud datasets. Tuned hyperparameters to balance sensitivity and precision. Validated against a holdout set of recent transactions.

Week 9-12

Dashboard & Deployment

Deployed to production, monitoring $50M+ daily volume.

Built the analyst interface with real-time alerts. Integrated with the transaction blocking API. Conducted load testing to ensure sub-second latency under peak load.