Kaycore Technologies
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Financial Services6 months

AI-Powered Fraud Detection System

Client

International Payment Processor

Overview

A major international payment processor was losing millions annually to fraudulent transactions. We developed an advanced machine learning-based fraud detection system that identifies suspicious patterns in real-time.

The Challenge

The payment processor's existing fraud detection rules-based system was: • Catching only 60% of fraudulent transactions • Generating 35% false positives, impacting customer experience • Processing fraud checks offline, with 2-hour delays • Unable to adapt to new fraud patterns • Costing the company $4.2M annually in fraud losses

Our Solution

We developed a comprehensive AI/ML solution: • Built ensemble machine learning models combining XGBoost, Neural Networks, and Isolation Forests • Implemented real-time processing using Apache Kafka and Spark Streaming • Created feature engineering pipeline to extract patterns from transaction data • Deployed models as microservices with sub-100ms latency • Implemented continuous learning to adapt to new fraud patterns • Built analytics dashboard for fraud analyst insights

Tech Stack

PythonTensorFlowXGBoostApache KafkaSparkKubernetesPostgreSQLGrafana

Key Results

fraud Reduction

87% reduction in successful fraudulent transactions

false Positives

65% reduction in false positives

cost Savings

$2.3M in annual fraud losses prevented

processing Latency

<100ms average detection latency

accuracy

98.7% precision in fraud detection

Detected and blocked 87% more fraud while reducing false positives

Saved the company $2.3M in the first year

Real-time processing replaced batch processing

ML models continuously improve with new fraud patterns

Improved customer satisfaction with fewer declined legitimate transactions

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