BankingTier 1 Commercial Bank12 weeks5 engineers
Case Study
40%Reduction in Fraud Losses
ML-powered real-time transaction scoring system processing 50,000 transactions per second with sub-100ms latency.
Results
Before & After
| Metric | Before | After | Improvement |
|---|---|---|---|
| Fraud Detection Accuracy | 72% | 96% | ↑ 33% |
| Alert Processing Time | 4 hours | 12 minutes | ↓ 95% |
| False Positive Rate | 28% | 4% | ↓ 86% |
| Annual Fraud Cost | $12M | $7.2M | ↓ 40% |
The Challenge
What We Were Solving
A leading commercial bank was losing $12M annually to fraudulent transactions. Their existing rule-based system generated thousands of false positives daily, overwhelming their fraud operations team.
Our Solution
How We Built It
We designed and deployed a real-time ML scoring system using Azure ML, with a LightGBM model trained on 3 years of transaction history. The system integrates directly with the bank's core banking platform via FastAPI.
Tech Stack
Technologies Used
Azure MLPythonDatabricksFastAPI
“StarkLogik delivered a fraud detection capability we thought would take 18 months in under 3. The false positive reduction alone has saved us 40 analyst hours per week.”
Sarah Chen
CTO, Tier 1 Commercial Bank
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