🛡️ Build a production-ready ML system for fraud detection with auto-scaling, monitoring, and orchestration using Kubernetes on Yandex Cloud.

🛡️ Build a robust fraud detection system with ensemble machine learning models for real-time insights and explainable AI.

🔍 Detect credit card fraud efficiently using advanced machine learning techniques, achieving high accuracy rates on a large dataset of transactions.

📊 Detect financial anomalies using advanced machine learning to uncover unusual trading patterns and prevent fraud in real-time across various markets.

📊 Detect anomalies in financial transactions with Finomaly, a modular, open-source Python library supporting both rule-based and machine learning methods.

🌟 Build production-ready AI services with this FastAPI template, integrating MLOps best practices for efficient deployment and management.

📊 Predict fraudulent transactions using SQL and Python with labeled data for accurate supervised learning and robust model evaluation.

🕵️♂️ Detect fraud in bank transactions using SQL for feature engineering and Python's Isolation Forest for unsupervised anomaly detection.

💳 Revolutionize payment systems with Zen7 Payment Agent, a DePA implementation that enables automated, secure transactions and innovative commerce solutions.

🤖 Detect fraudulent transactions in real time with our AI system, reducing losses and providing clear explanations for compliance.

Real-time anomaly detection engine for financial transactions using Autoencoders.

Master's thesis project on GNNs for temporal money laundering motif detection

Machine Learning based Fraud Detection System using Python, SMOTE, and Random Forest.

REST API for financial transaction processing with fraud detection, balance management, daily spending limits, and analytics. Built with Python/FastAPI. Features include multi-currency support, duplicate detection, velocity checks, and comprehensive transaction lifecycle management.

🛡️ Sistema de Detecção de Fraude de Alta Performance (5k TPS, latência <200ms)

Open-source financial threat detection (FastAPI, Python).

A FastAPI service for real-time fraud prediction in financial transactions, focusing on Nigerian fintech. It provides REST endpoints for scoring and utilizes Dramatiq/Redis for background LoRA fine-tuning jobs.

Enterprise-grade fraud detection microservices platform with ML-powered real-time transaction scoring, built with FastAPI, Kafka, PostgreSQL, Redis, Neo4j. Features complete CI/CD, Kubernetes deployment, Helm charts, and Terraform IaC for Oracle Cloud.

Real-time fraud detection pipeline. Kafka ingests 10K+ events/min, Flink aggregates, Spark ML scores at 97.5% accuracy. ~$300/month gets you 3,700 predictions/min, <3s latency, auto-scaling to zero. Airflow + Terraform IaC. 79x throughput improvement via batch optimization.

A scalable microservices architecture for real-time AI fraud detection. Built with Python, FastAPI, Scikit-Learn, and asynchronous communication.