FRIS — Fraud Risk Intelligence System

A production-grade fraud detection system designed to operate exactly where real-world banks and fintechs need it.

Why This Project? (Business Context)

Financial institutions face three hard problems in fraud:

1. Labels are delayed and incomplete — you can’t rely purely on supervised learning.
2. Inference must be deterministic — regulators don’t accept "notebook logic".
3. Every decision must be explainable — internal audit, compliance, and customers demand clarity.

FRIS is built as the system that would sit between transaction processing and risk decisioning in a real bank/fintech:

• ingest a transaction
• compute engineered signals + model outputs
• combine supervised + unsupervised signals
• return a risk score and explanation
• forward the decision to fraud ops, rule engines, or customer flows

It acts as the fraud intelligence layer, not just a classifier.** A full-stack, production-oriented fraud detection system. Built end-to-end with deterministic preprocessing, stacked ML inference, SHAP explainability, FastAPI serving, Docker packaging, and a Streamlit UI.

This repository contains the complete system exactly as it runs in production.

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🚀 Live Demo

Below is the live version of the exact system described: ** Backend (FastAPI): https://fraud-risk-intelligence-system-api.onrender.com/

Frontend (Streamlit): https://fraud-risk-intelligence-system.streamlit.app/

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🧱 System Architecture

High-Level Architecture Diagram

API Sequence Diagram

Streamlit UI Screenshot

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Below is the text-form pipeline diagram:**

Raw Transaction (JSON)
        ↓
Input Validation (Pydantic)
        ↓
Frozen Feature Pipeline
        ↓
Base Models
  - XGBoost
  - Autoencoder (PyTorch)
  - Isolation Forest
  - MLP
        ↓
Meta-Feature Builder
        ↓
Stacked Ensemble (Logistic Regression)
        ↓
Risk Score + Label
        ↓
SHAP Explanation (Inference-Aligned)
        ↓
FastAPI → Streamlit UI

One pipeline. One truth. Training = Inference = Explainability.

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🔍 Tech Highlights

These are the core engineering skills demonstrated by FRIS:

• training/inference parity (deterministic, frozen preprocessing)
• stacked ensembles with OOF predictions
• SHAP explainability at inference (no notebook drift)
• FastAPI backend with lifecycle-safe loading
• Dockerized deployment for reproducibility
• CI-ready repository structure with tests
• strict schema validation and boundary contracts
• Streamlit frontend correctly separated from ML logic

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🔍 Key Features**

1. Deterministic Feature Engineering

All preprocessing is frozen:

• numerical transforms
• temporal features
• frequency encodings
• aggregation stats
• missingness flags

Stored as:

• feature_columns.json
• preprocessors.joblib

Inference never recomputes anything.

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2. Hybrid Modeling

FRIS combines multiple weak signals:

• XGBoost (supervised)
• Autoencoder (reconstruction-based anomaly signal)
• Isolation Forest (unsupervised)
• MLP (nonlinear auxiliary signal)

These feed into a Logistic Regression stacker trained only on OOF predictions.

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3. Real Explainability (SHAP)

FRIS implements:

• global importance
• local per-transaction attributions
• top-K feature drivers
• inference-aligned explanations

No notebook recomputation. No drift.

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4. Production-Grade API

FastAPI backend with:

• GET /health
• POST /predict
• POST /explain

Includes:

• lifecycle-safe model loading
• frozen contracts
• schema validation
• deterministic behavior across environments
• end-to-end tests

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5. Deployment & Packaging

• Dockerized backend
• artifacts baked in
• pinned requirements
• deployed on Render (API)
• deployed on Streamlit Cloud (UI)

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6. Streamlit UI

Minimal, narrative-style interface:

• enter a transaction
• get fraud probability + label
• see SHAP explanation
• transparency panel

UI contains zero ML logic — everything flows through the API.

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📦 Repository Structure

FRIS/
├── src/
│   ├── api/               # FastAPI backend
│   ├── features/          # Frozen feature pipeline
│   ├── models/            # Model loaders + artifacts
│   ├── pipeline/          # Inference spine
│   ├── explain/           # SHAP explain layer
│   └── utils/             # Helpers
│
├── app/                   # Streamlit frontend
│   └── streamlit/
│
├── data/
│   ├── processed/
│   └── artifacts/         # models, encoders, scalers, explainer
│
├── tests/                 # API + inference tests
│
├── docs
├── notebooks
├── Dockerfile
├── requirements.txt
└── README.md

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📡 Usage

Run backend locally:

pip install -r requirements.txt
uvicorn src.api.main:app --reload

Call API:

POST /predict
{
  "Time": 10000,
  "V1": -1.35,
  ...
  "Amount": 92.10
}

Response:
{
  "score": 0.87,
  "label": "fraud"
}

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📊 Dataset

• 284,807 transactions
• 0.17% fraud rate
• PCA-derived features V1–V28
• Columns: Time, Amount, V1–V28, Class

Raw data not included.

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✔️ What FRIS Demonstrates

• training/inference parity
• frozen preprocessing
• leakage detection
• stacked model design
• honest SHAP explainability
• API-first ML engineering
• Docker deployment
• UI separation of concerns
• real-world ML constraints

This is not a model.
This is a complete ML system.

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📌 Project Status

FRIS v1.0 — Complete, deployed, stable.

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