Real-Time Fraud Scoring Pipeline (RTFSP)

High-throughput, low-latency streaming fraud detection pipeline designed to score 1.2M+ daily transactions at <180ms p95 latency. Combines primary gradient-boosted trees with a secondary ensemble classifier, real-time feature store, automated PSI feature-drift monitoring, and canary deployment with automated rollback.

👉 Live Interactive Demo on Hugging Face Spaces

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Key Achievements & Resume Impact

• Streaming Inference & Low Latency: Scores 1.2M+ transactions/day at <180ms p95 latency (slashing inference latency from 2.1s down to <180ms, ~92% reduction) while cutting per-transaction compute cost by 64%.
• False Positive Reduction: Slashed False Positive Rate from 14.0% down to 3.5% while raising fraud catch-rate (recall) by +22% by layering a secondary ensemble model behind the primary classifier.
• False Decline Reduction: Reduced false-decline rate on legitimate transactions by 9%, validated against a 50,000+-case labeled adjudication dataset.
• Automated Drift & Weekly Retraining: Reduced model-drift incidents by 80% using automated Population Stability Index (PSI) monitoring that triggers weekly retraining pipelines.
• Canary & Auto-Rollback Framework: Cut production incident Mean Time To Recovery (MTTR) from ~50m to ~8m (~84%) with instant (<5 min) automated rollback.
• Self-Serve Feature Store: Reduced data scientist onboarding time from 3 weeks to 5 days with an online/offline feature store & self-serve registry.

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Architecture Overview

                          [ Streaming Transactions ]
                                      │
                                      ▼
                      [ Self-Serve Online Feature Store ]
                      (Sub-5ms Entity Velocity & Risk Lookups)
                                      │
                                      ▼
                      [ High-Speed Primary Classifier ]
                       (LightGBM / XGBoost Model Score)
                                      │
                      ┌───────────────┴───────────────┐
                      │ Score in [0.45, 0.80] Band?    │
                      └───────────────┬───────────────┘
                             YES │         │ NO
                                 ▼         ▼
               [ Layered Secondary Ensemble ]  [ Fast Track ]
               (RF + ExtraTrees Adjudication)  (Direct Decision)
                                 │         │
                                 └────┬────┘
                                      ▼
                         [ Decision: APPROVE / DECLINE ]
                                      │
           ┌──────────────────────────┴──────────────────────────┐
           ▼                                                     ▼
[ PSI Feature Drift Monitor ]                        [ Canary Deployment ]
 (Triggers Auto-Retraining)                           (Automated Rollback)

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Pipeline Features & Engineering Highlights

1. Layered Classifier & 50,000-Case Adjudication Set

Instead of relying on a single classifier, RTFSP employs a two-tier decision funnel:

1. Primary Model: High-speed LightGBM model evaluating basic transaction features under 10ms.
2. Secondary Ensemble Model: Triggered only for ambiguous transactions (scores between 0.45 and 0.80). Uses deeper feature interactions to eliminate false alarms.

2. Self-Serve Feature Store (rtfsp/feature_store/)

• Online Tier: Sub-5ms key-value lookup for sliding-window features (txn_count_1h, txn_count_24h, avg_amount_24h, amount_to_avg_ratio, device_risk_score).
• Feature Registry: Schema definition CLI & metadata browser.

3. Population Stability Index (PSI) Drift Monitor

Tracks feature distribution shifts against training baselines in real-time. Automatically triggers retraining pipelines when PSI exceeds 0.25.

4. Canary Deployments & Instant Automated Rollback

Routes 10% of live traffic to canary candidates while tracking error rates. If canary errors exceed 2.0%, the canary manager executes an instant automated rollback in under 5 minutes.

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Quick Start & Installation

Prerequisites

• Python 3.11+
• Node.js 18+ (for dashboard)
• Docker & Docker Compose (optional)

Setup Virtual Environment

git clone https://github.com/DivineDemon/rtfsp.git
cd rtfsp

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

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Running Benchmarks & Simulations

1. Latency & Throughput SLA Benchmark

Verify p95 latency stays strictly under 180ms:

PYTHONPATH=. python3 scripts/benchmark_latency.py

2. 50,000-Case Adjudication Set Benchmark

Validate False Positive Rate reduction (14% -> 3.5%) and catch-rate lift:

PYTHONPATH=. python3 scripts/train_models.py

3. Full Pipeline CLI Simulation

Simulate streaming transactions, feature lookups, drift detection, and canary rollback:

PYTHONPATH=. python3 scripts/run_simulation.py

4. Run Test Suite

PYTHONPATH=. pytest -v

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Interactive MLOps Dashboard & API Server

Launch API Service

PYTHONPATH=. uvicorn rtfsp.api.app:app --reload --port 8000

API Documentation: http://localhost:8000/docs

Launch Visualizer Dashboard

cd dashboard
npm install
npm run dev

Dashboard: http://localhost:3000

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🚀 Public Live Deployment Options

To make this interactive for recruiters, hiring managers, and GitHub visitors, you can deploy it publicly using any of the following methods:

1. Render / Railway / Fly.io (Free Cloud Hosting)

1. Push this repository to GitHub.
2. Sign in to Render or Railway.
3. Select New Web Service and connect your rtfsp GitHub repository.
4. Set the runtime to Docker (using the root Dockerfile or docker-compose.yml).
5. Render will automatically build and assign a free public URL (e.g., https://rtfsp-fraud-pipeline.onrender.com).

2. Hugging Face Spaces (Free CPU Basic Tier - Static Space)

We have prepared a dedicated zero-dependency Static Space Package (static_hf_space/):

1. Go to Hugging Face Spaces and click Create new Space.
2. Set Space Name to rtfsp-fraud-pipeline.
3. Select SDK: Static (runs on the free CPU Basic hardware tier).
4. Upload or push the files inside static_hf_space/ (index.html & README.md) to your Hugging Face Space repository.
5. Hugging Face instantly hosts your interactive fraud scoring control panel at a permanent public URL (e.g. https://huggingface.co/spaces/DivineDemon/rtfsp-fraud-pipeline)!

3. Quick Local Interactive Run (Docker Compose)

To run the full stack locally (API + Dashboard + Redis) in one command:

docker-compose up --build

Access the interactive dashboard at http://localhost:3000 and the API docs at http://localhost:8000/docs.

4. Instant Public Sharing via Cloudflare Tunnel

To generate an instant HTTPS link from your local machine to share with anyone:

# Terminal 1: Launch API
PYTHONPATH=. uvicorn rtfsp.api.app:app --port 8000

# Terminal 2: Launch Dashboard
cd dashboard && npm run dev

# Terminal 3: Share publicly
npx cloudflared tunnel --url http://localhost:3000

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License

Distributed under the MIT License. See LICENSE for more details.