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Case Study / Finance

Financial Fraud Detection

Real-Time Fraud Prevention with AI and Graph Analytics

Real-time fraud prevention using ensemble ML, graph neural networks, and explainable AI frameworks with <100ms latency.

Client

Tier 1 Bank

Industry

Finance

Services

Fraud Detection

Timeline

7 months

Team

12 engineers

Stack

PyTorch, Kafka, Kubernetes, Elasticsearch, AWS, Python, XGBoost
Financial Fraud Detection
01 / The challenge

The Challenge

A major tier-1 bank was facing escalating fraud losses and an overwhelming number of false positives from their legacy rule-based fraud detection system. Their fraud team was drowning in alerts, missing real fraud while investigating false alarms.

Key pain points

  • $50M annual fraud losses
  • 85% false positive rate on fraud alerts
  • Average 24-hour fraud detection delay
  • No real-time transaction monitoring
  • Inability to detect sophisticated fraud rings
02 / Our solution

Our Solution

We built a next-generation fraud detection platform combining ensemble machine learning, graph neural networks for fraud ring detection, and explainable AI for regulatory compliance.

Graph Neural Networks

Detecting fraud rings and money laundering networks through transaction graph analysis

Real-Time Scoring

Sub-100ms fraud scoring for every transaction with streaming architecture

Explainable AI

SHAP-based explanations for every fraud decision, enabling regulatory compliance

Ensemble Models

Combination of XGBoost, neural networks, and rule engines for robust detection

03 / Technical architecture

Technical Architecture

High-throughput, low-latency architecture designed for real-time financial transaction processing.

PyTorch for model development
Apache Kafka for event streaming
Kubernetes for orchestration
Elasticsearch for investigation
AWS infrastructure
Custom graph database for network analysis

Technical highlights

Graph neural networks, explainable AI dashboards, real-time stream processing

04 / Results & impact

Results & Impact

$10M

Fraud Prevented

First year savings from prevented fraud

50%

False Positive Reduction

Fewer false alarms for investigators

99.9%

Detection Rate

Fraud detection accuracy

<100ms

Latency

Real-time transaction scoring

This fraud detection system has completely transformed our ability to protect our customers. We're catching fraud in real-time while dramatically reducing false positives.

Chief Risk Officer

Tier 1 Bank

05 / Key takeaways

What we learned

01

Graph neural networks are essential for detecting coordinated fraud rings

02

Explainable AI is mandatory for financial services compliance

03

Real-time processing requires careful architecture design

Next step

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