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Data Science 6 ay ML Engineer & Data Scientist

Fintech Fraud Detection Platform

Machine learning platform performing anomaly detection on real-time transaction streams. Streaming ML architecture analyzing 10,000+ transactions per second.

%78
Fraud Reduction
10K+
Transactions/Second
%60
False Positive Decrease
%97.3
Model Accuracy

Challenge

Analyzing 10,000+ transactions per second in real-time, adapting to constantly changing fraud patterns, and maintaining low false positive rates were the key challenges.

Solution

Built a low-latency streaming architecture with Apache Flink. Achieved adaptive anomaly detection with a hybrid model combining Isolation Forest and AutoEncoder. Continuously updated the model with online learning.

Highlights

1

Real-time streaming ML architecture (Flink + Kafka)

2

Hybrid anomaly detection model design

3

Comprehensive feature engineering with 100+ features

4

Explainable AI integration with SHAP

Technology Stack

Python
Apache Flink
Scikit-learn
TensorFlow
Kafka
ClickHouse
Grafana

About the Project

Developed for a fintech company, this platform analyzes user transactions in real-time to detect and prevent fraudulent transactions.

Technical Details

  • Apache Flink for real-time stream processing
  • Isolation Forest +
  • Feature engineering pipeline (100+ features)
  • Adaptive threshold mechanism
  • Explainable AI (SHAP) for decision interpretability

    • Impact

    Achieved 78% reduction in fraud losses and 60% decrease in false positive rate.