AI Fraud Detection System
97% fraud detection accuracy with minimal false positives
Fraud detection accuracy reached 97%
False positives reduced by 41%
Fraud investigation time reduced by 64%
Overview
Fraud detection is one of the most demanding machine learning problems in production: the cost of a false negative is a fraudulent transaction; the cost of a false positive is a legitimate customer blocked. Getting the precision-recall balance right requires domain expertise, high-quality feature engineering, and real-time inference infrastructure. We delivered a fraud detection system that meets production accuracy requirements without creating friction for genuine users.
The Challenge
Financial transactions required real-time fraud detection that could identify suspicious activity accurately without generating enough false positives to frustrate legitimate customers.
The Solution
Developed machine learning models for real-time fraud detection using behavioural patterns, transaction features, and anomaly detection — integrated into the existing payment flow.
How We Approached It
Feature Engineering
Designed features capturing velocity, device fingerprints, location patterns, and transaction history that distinguish fraud from legitimate behaviour.
Class Imbalance Handling
Applied SMOTE and cost-sensitive learning to address the extreme class imbalance inherent in fraud datasets.
Real-Time Pipeline
Deployed the model behind a sub-100ms scoring API so every transaction receives a risk score before the authorisation decision.
Feedback Loop
Built a case management tool for fraud analysts to label false positives and negatives, feeding corrections back into model retraining.
Key Features Built
Results & Impact
Fraud detection accuracy reached 97%
False positives reduced by 41%
Fraud investigation time reduced by 64%
Technologies
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