Real-Time Fraud Scoring Pipeline

Project: Real-Time Fraud Scoring Pipeline
Role: Senior Machine Learning Engineer
Technologies: Python, LightGBM, FastAPI, Feature Store, Docker
Domain: FinTech, Fraud Detection, Streaming Analytics, MLOps

Executive Summary

In financial technology, fraud detection is a constant race against time. Systems must score millions of transactions with extreme accuracy; a delayed response frustrates users, while a false decline damages merchant relationships. The Real-Time Fraud Scoring Pipeline is a high-throughput, low-latency streaming system that successfully scores 1.2M+ daily transactions at < 180ms p95 latency. By implementing a two-tier ensemble classifier and automated feature-drift monitoring, the pipeline reduced false-positive rates by 75% and slashed production incident recovery times from ~50 minutes to under 8 minutes.

The Challenge

The legacy fraud detection architecture suffered from three critical flaws:

• Latency Bottlenecks: Inference times averaged 2.1 seconds, causing transaction timeouts.
• High False Positive Rates: A staggering 14.0% of legitimate transactions were flagged as fraud, resulting in devastating "false declines" for honest customers.
• Model Degradation: Fraud patterns evolve rapidly. The legacy model frequently drifted, and updating it required weeks of manual retraining, leading to significant financial losses during drift periods.
• Brittle Deployments: Deploying a new model took over an hour to rollback if it failed in production.

The Solution: A Layered Streaming Architecture

To achieve sub-200ms latency while increasing accuracy, I architected a multi-stage streaming pipeline built around a self-serve online feature store and a dual-model adjudication engine.

Core Technical Pillars:

1. Two-Tier Layered Classifier:
   • Primary Model: A high-speed LightGBM classifier that evaluates basic transaction features in under 10ms.
   • Secondary Ensemble Model: Triggered only for ambiguous transactions (scores between 0.45 and 0.80). This Random Forest/ExtraTrees ensemble uses deeper feature interactions to eliminate false alarms without bogging down the entire pipeline.
2. Self-Serve Online Feature Store: Engineered a low-latency key-value feature store providing sub-5ms lookups for sliding-window features (e.g., txn_count_1h, device_risk_score), drastically reducing data scientist onboarding time from 3 weeks to 5 days.
3. Automated MLOps Automation:
   • PSI Drift Monitor: Tracks feature distribution shifts in real-time. If the Population Stability Index exceeds 0.25, the system automatically triggers a weekly retraining pipeline.
   • Canary Deployments: New models are tested on 10% of live traffic. If error rates exceed 2.0%, the system automatically executes a rollback in under 5 minutes.

Key Features & Business Impact

1. Massive Reduction in False Declines

By layering the secondary ensemble model and validating against a 50,000-case labeled dataset, the False Positive Rate plummeted from 14.0% to 3.5%. Concurrently, the false-decline rate on legitimate transactions dropped by 9%, significantly boosting merchant revenue.

2. Unprecedented Speed and Cost Efficiency

The transition to asynchronous streaming slashed inference latency by ~92% (from 2.1s down to < 180ms p95 latency) while simultaneously cutting per-transaction compute costs by 64%.

Empirical Evidence & Outcomes

• Incident Mitigation: The automated canary rollback framework cut production incident Mean Time To Recovery (MTTR) from ~50m to ~8m (~84% reduction).
• Drift Management: Automated PSI monitoring and retraining reduced model-drift incidents by 80%.

"The Real-Time Fraud Scoring Pipeline illustrates the pinnacle of FinTech MLOps. By decoupling the high-speed initial assessment from the deep-feature ensemble evaluation, it proves that you do not have to sacrifice analytical depth to achieve sub-200ms streaming SLAs."