FINTECH FRAUD DETECTION
predictive
Fintech AI Fraud Detection & Anomaly Prevention
A real-time financial transaction monitoring system leveraging Graph Neural Networks and XGBoost to identify fraudulent credit card transactions and AML suspicious patterns under 50ms latency.
# Anomaly Detection# Graph Neural Network# XGBoost# Spark Streaming
Contract formatContract development
Development system5 people
Development period5 months
Key Features
Core System Capabilities
Optimized modular design for both End-Users and Enterprise Management Admin
01
Risk Analyst side
- Real-time suspicious transaction alert feed
- Fraud risk score & factor breakdown
- Graph visualization of fraud rings
- Manual rule override & whitelist UI
- Case management workflow
5 features
02
AI Fraud Engine side
- Real-time streaming evaluation (<50ms)
- Graph Neural Network (GNN) entity modeling
- XGBoost gradient boosting classifier
- Self-learning fraud pattern adaptation
4 features
03
Compliance side
- Anti-Money Laundering (AML) report generator
- Regulatory compliance audit logs
- False positive rate optimization
- Core banking API integration
4 features
Development Scope
Project Life Cycle & Phases
Phase 01
Requirement definition
Phase 02
UI/UX design
Phase 03
Financial Risk AI tuning
Phase 04
Development
Phase 05
Unit / Integration testing
Phase 06
Operation and Maintenance
Tech Stack Architecture
Technologies Used in Project
Languages & Core Frameworks
Python, Scala, PyTorch Geometric
AI Tools & GenAI Models
Graph Neural Network (GNN), XGBoost, DeepSeek R1, Antigravity Fraud Shield
Databases & Vector Storage
Neo4j Graph DB, Redis, PostgreSQL
Infrastructure & Cloud Services
Apache Spark Streaming, Docker, AWS Kinesis
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