A prototype AML (Anti‑Money Laundering) monitoring API built with FastAPI and Pydantic. It ingests transaction messages, applies a cascading classification pipeline (rule‑based → lightweight ML → LLM), and emits risk scores and alerts for suspicious activity. Data validation, model integration, scalable deployment, and real‑time monitoring.

Fraud Detection REST API project built with FastAPI and LightGBM Binary Classifier.

Fraud detection classifier leveraging FastAPI and Docker infrastructure. Used for internal testing.

real-time fraud detection system built with Random Forest and XGBoost classifiers. The models are trained on a real-world fraud dataset and deployed via FastAPI for real-time predictions.

fraud-detection-ml-engine is an ML-based fraud detection PoC where a Java backend calls a Python FastAPI ML engine via REST. Behavioral, geo, and device risk agents predict fraud probability, and a decision agent classifies transactions as LOW, MID, or HIGH risk.

End-to-end Credit Card Fraud Detection: data preprocessing (SMOTE), model training (LogReg and Random Forest Classifier), evaluation (ROC/AUC), FastAPI prediction endpoints and Streamlit UI. Deployment-ready with CI-friendly structure and MIT license.

A Credit Card Fraud Detection System that uses machine learning to identify fraudulent transactions in real-time. Built with FastAPI, Streamlit, and Random Forest Classifier, this project demonstrates the practical application of ML in financial security. ## Key Points - 🎯 99.95% accuracy in fraud detection - ⚡ Real-time transaction a

SecureClaim AI is a full-stack insurance fraud detection system trained on Automobile, Home, and Health claim datasets. It uses XGBoost and FastAPI to generate a fraud risk score and automatically classify claims as ACCEPT or REJECT through a React-based interactive web interface.

SecureClaim AI is a full-stack insurance fraud detection system trained on Automobile, Home, and Health claim datasets. It uses XGBoost and FastAPI to generate a fraud risk score and automatically classify claims as ACCEPT or REJECT through a React-based interactive web interface.

Built an end-to-end Credit Card Fraud Detection system using PCA-anonymized transaction data and a Random Forest classifier. Developed a FastAPI backend for real-time inference and a Streamlit-based UI for interactive predictions, handling class imbalance and feature scaling.

Credit Card Fraud Detection System using an ensemble of Logistic Regression, Decision Tree, and SVM models to classify fraudulent transactions in a highly imbalanced dataset. The model is integrated with a FastAPI backend for real-time fraud prediction and deployed using Docker for scalable, production-ready machine learning applications.

This Fraud Detection Engine spots high-risk transactions on the fly with a Random Forest Classifier. It runs on FastAPI, so decisions come quick, and uses SHAP to explain exactly why it flags something as risky. The repo covers everything—creating synthetic data, building and deploying the model.

AI-based Voice Authenticity Detection API built with FastAPI and PyTorch. The system analyzes Base64-encoded MP3 audio to classify speech as Human or AI-generated using a trained Wav2Vec2-based model. Designed for fraud detection, safety, and real-time API evaluation.

Designed and developed a multi-agent Agentic AI grievance system for UPI using LangGraph and FastAPI. Built a stateful orchestrator to classify and route queries to domain-specific agents (Payment, Mandate, Fraud, FAQ). Implemented multi-step workflows, LLM-based reasoning, streaming chat APIs, and structured session-aware context management.