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AI Fraud Detection MVP

AI Fraud Detection MVP
  • March 2026
  • 80 Project Files
  • AI / Machine Learning

Reduced false positives by 40% for a Pan-African lender using an ensemble ML model on 2M+ transaction records.

Our client, a major FinTech lender processing 50,000+ daily loan applications, struggled with high false-positive fraud flags. We built a hybrid XGBoost + Neural Network ensemble trained on 5 years of transaction logs. We delivered a low-latency REST API (sub-100ms) integrated directly into their loan origination system, deployed on AWS with auto-scaling.

Project Manager

Engr. Chigozie Ukebuo

Engr. Chigozie Ukebuo

Project Lead

Team Members

  • Amina Bello
  • Chidi Okonkwo
  • Kofi Mensah

Project Timeline

Data Audit & Feature Engineering

01 Jan 2026

Ingested 2M+ records and engineered 120 behavioural features.

Ensemble Model Training

15 Feb 2026

Trained XGBoost and DeepFF models; achieved 0.96 AUC on validation.

API & Integration

01 Mar 2026

Built FastAPI gateway with Redis caching for sub-100ms inference.

A/B Testing & Go-Live

20 Mar 2026

Ran A/B test on 20% traffic; rolled out to 100% after 1 week.

Challenges

Extreme class imbalance (0.5% fraud rate) while maintaining sub-100ms latency and full interpretability for compliance.

Results

40% reduction in false positives, 15% increase in borderline loan approvals, and 99.9% API uptime.

Quote
"electroSOFT turned our messy logs into a precision tool. We cut false positives by 40% in month one, saving our support team hundreds of hours." – CTO, Pan-African Lender

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