Fraud Detection model for African Fintechs
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title: Hybrid Temporal Graph Transformer
emoji: 📈
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
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# Advanced Fraud Detection for African FinTechs
A real-time, AI-powered fraud detection and trust-scoring engine built on top of an integrated payment backend. Hybrid-Temporal-Graph-Transformer uses a Hybrid Temporal Graph Transformer (TGT) architecture — an ensemble of five specialized deep learning models — to detect, score, and intercept fraudulent transactions before settlement.
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## 📌 Table of Contents
- Problem Statement
- Solution Overview
- Architecture
- Core Models
- System Integration
- Decision Engine
- Adaptive Learning
- Tech Stack
- Payment Backend Integration
- Getting Started
- Project Structure
- Low-Data Strategies
- Compliance & Explainability
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## 🚨 Problem Statement
Africa's FinTech ecosystem is under siege from fraud that existing systems were never built to catch. Nigerian FinTechs face a uniquely dangerous combination of:
- **SIM Swap Fraud** — criminals hijack mobile numbers to bypass authentication and authorize transfers
- **Synthetic/Identity KYC Fraud** — AI-generated or forged documents used to open fraudulent accounts
- **Social Engineering (Vishing/Smishing)** — victims manipulated into authorizing transfers to mule accounts
- **Payment Injection** — unauthorized transaction requests injected directly into the API layer
- **Coordinated Fraud Rings** — networks of linked accounts operating as money laundering infrastructure
Classical rule-based systems and standard ML models (Random Forest, Logistic Regression) fail here because fraud in Africa is **relational, behavioral, and adaptive** — it mutates faster than static models can respond. Every verification failure has a real cost: real people lose money, real businesses lose trust, and financial inclusion gets pushed further out of reach.
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## 💡 Solution Overview
This is a **Hybrid Temporal Graph Transformer (TGT)** — a real-time AI fra …