Fraud Detection Model with Production Data Pipelines and Monitoring
A fintech platform needed a fraud detection system that could run in real time with measurable accuracy and clear monitoring. We built the data pipeline, trained models, deployed low latency inference, and implemented drift detection so the system stayed reliable as behavior changed.
Confidential engagement. NDA available upon request.
78%
Fraud Loss Reduction
0.3%
False Positive Rate
120ms
Median Inference
10
Weeks to Launch
About the Client
Industry
FinTech
Company Size
70 to 140 employees
Background
A fintech platform processing high volume transactions. They needed to reduce fraud while preserving customer experience and avoiding excessive false positives.
ML and Product Constraints
Latency limits
Decisions had to be made quickly during checkout and account actions.
Data quality and feature drift
Data sources were inconsistent, and features shifted as product behavior changed.
Explainability needs
Risk decisions needed interpretable signals to support review and appeals.
Operational monitoring
The model needed monitoring for drift, performance, and incident response readiness.
The Mission
Build a fraud detection system that reduces losses with low latency, measurable performance, and monitoring that keeps the system reliable over time.
How We Approached It
01. Data and feature design
Week 1 to 3- Data source audit and quality fixes
- Feature set definition and labeling approach
- Evaluation metrics definition
- Baseline model training and review
02. Production pipeline and deployment
Week 4 to 8- Feature pipeline build and validation checks
- Model training workflow and versioning
- Low latency inference deployment
- A B testing plan and rollout gating
03. Monitoring and governance
Week 9 to 10- Performance monitoring and drift detection
- Alerting and runbooks for incidents
- Model review cadence and retraining triggers
- Post launch tuning
Vulnerabilities Discovered
0
CRITICAL
2
HIGH
2
MEDIUM
0
LOW
Label leakage risk
Some features risked indirectly leaking future outcomes, inflating offline metrics without real world performance.
Some features risked indirectly leaking future outcomes, inflating offline metrics without real world performance.
Feature drift due to product changes
Several features changed meaning over time, requiring drift monitoring and retraining rules.
Several features changed meaning over time, requiring drift monitoring and retraining rules.
Data quality gaps in key fields
Missing or inconsistent values reduced model stability and required stronger validation.
Missing or inconsistent values reduced model stability and required stronger validation.
Review workflow unclear
Operations needed clear steps to review and override decisions with auditability.
Operations needed clear steps to review and override decisions with auditability.
How We Fixed It
Feature validation and governance
Removed leakage risks and added validation checks and versioning for features and training data.
Low latency inference
Deployed a fast inference service with caching and safe fallbacks.
Monitoring and drift detection
Implemented monitoring and alerts for performance changes and drift to trigger retraining.
Measurable Outcomes
The system reduced fraud losses while keeping customer friction low through careful tuning and ongoing monitoring.
78%
Fraud Loss Reduction
0.3%
False Positive Rate
120ms
Median Inference
100%
Model Changes Tracked
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