FinTech

AI-Powered Fraud Detection System

Major Payment Network

Duration

12 months

Team Size

15+ engineers

Completed

August 2025

Project Overview

Understanding the scope and objectives

Developed and deployed a real-time fraud detection system using advanced machine learning algorithms that analyzes transaction patterns, user behavior, and contextual signals to identify and prevent fraudulent transactions with unprecedented accuracy.

87%

Fraud Reduction

<10ms

Detection Time

Millions

Fraud Prevented

99.8%

Accuracy Rate

Services Provided

Machine LearningReal-time ProcessingCloud ArchitectureData EngineeringAPI Development

Technologies Used

PythonTensorFlowAWS SageMakerApache FlinkRedisPostgreSQLDocker

Impact Summary

This project delivered measurable business value, exceeding client expectations and establishing a foundation for continued growth and innovation.

The Challenge

Understanding the Problem

Every successful project starts with a deep understanding of the challenges at hand.

The payment network was experiencing increasing fraud losses, with traditional rule-based systems unable to keep pace with sophisticated attack patterns. The existing system had high false-positive rates, causing friction for legitimate customers while still missing complex fraud schemes.

Challenge 1

Process and analyze millions of transactions per second with sub-10ms latency requirements

Challenge 2

Reduce false positive rates while dramatically improving fraud detection accuracy

Challenge 3

Adapt to evolving fraud patterns without manual rule updates

Challenge 4

Integrate seamlessly with existing payment infrastructure across global data centers

Our Solution

How We Solved It

Our team developed a comprehensive solution tailored to the client's unique needs.

Our Approach

We built a hybrid ML system combining supervised learning for known fraud patterns with unsupervised anomaly detection for emerging threats. The architecture prioritized real-time performance while enabling continuous model improvement through online learning.

Ensemble ML Models

Developed multiple specialized models working together to detect different fraud types with high precision.

Real-Time Feature Store

Built a high-performance feature computation engine delivering fresh signals for every transaction.

Adaptive Learning System

Implemented online learning capabilities that allow models to adapt to new fraud patterns in real-time.

Explainable AI Dashboard

Created tools for fraud analysts to understand model decisions and investigate flagged transactions.

Global Deployment

Deployed across 5 continents with edge computing for minimal latency impact on transactions.

Automated Retraining

Established MLOps pipelines for continuous model monitoring, evaluation, and retraining.

Implementation Process

Phase 1

Data Analysis

Phase 2

Model Development

Phase 3

System Integration

Phase 4

Global Rollout

The Results

Measurable Impact

Our solutions delivered quantifiable business value that exceeded expectations.

87%

Fraud Reduction

Decrease in successful fraudulent transactions

<10ms

Detection Speed

Real-time detection without transaction delays

-65%

False Positives

Reduction in incorrectly flagged transactions

Millions

Annual Savings

In prevented fraud losses

Key Business Outcomes

Protected over 2 billion annual transactions across global payment network

Reduced customer friction with dramatically lower false positive rates

Created competitive advantage through superior fraud prevention

Enabled expansion into higher-risk markets with confidence

Built foundation for future ML applications across the organization

Achieved regulatory recognition for advanced fraud prevention capabilities

"The ML-powered fraud detection system has been transformative for our business. We have seen dramatic reductions in fraud losses while actually improving the customer experience. TechnischWerk delivered cutting-edge AI that works in the real world."

Ananya Iyer

VP of Risk Management

Major Payment Network