The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.

Enterprise AI-powered fraud detection platform with real-time monitoring, ensemble machine learning, FastAPI backend, analyst workflows, fraud case management, and intelligent fraud analytics.

Side-by-side build of the same fraud-analytics workload on Databricks and Snowflake. Same dbt models, both engines, with cross-platform parity check.

🛡️ Welcome to our Credit Card Fraud Detection project! 💳 Harnessing the formidable prowess machine learning, we're steadfast in our mission to fortify your financial stronghold against deceitful adversaries. Join our crusade for financial resilience,Ensuring every transaction is securely monitored! 🔐💯

Fraud Detection Analytics Project using Python, SQL, Power BI and Tableau. End-to-end case simulating a fintech fraud analyst workflow.

Assignments for the semester Jun - Dec 2021 @ IIT Hyderabad

Fraud Transaction Detector is a machine learning system that identifies and flags potentially fraudulent transactions, provides risk scoring, analytics summaries via Agentic AI, and actionable insights to help businesses monitor and prevent fraud effectively.

A risk-based fraud alert triage system that scores transactions, prioritizes alerts by severity, and applies proportionate remediation actions to minimize financial loss while preserving customer experience.

Real-time transaction fraud risk scoring for Acquirer clients — Straive Strategic Consulting

Fraud Analytics Dashboard - Data Science & Analytics project in AI / ML / GenAI for CSEProjects360 final year project catalog.

Fraud analytics and risk scoring portfolio project that models transactional behavior, applies rule-based fraud detection, and generates account-level risk scores and an operational dashboard for monitoring high-risk activity and rule effectiveness.

Procurement risk analytics — Neo4j graph patterns (shared addresses, winner rotation) + anomaly detection and data-quality checks

Four-part ML framework for spend forecasting, segmentation, fraud scoring, and credit limit recommendation across ~1.5M transactions and 18,070 accounts

End-to-end Credit Card Fraud Detection project using Python, Scikit-learn, and Streamlit — includes data ingestion, feature engineering, model training, scoring, monitoring, and an interactive dashboard for fraud analysis.

This repository offers a comprehensive overview of various analytical techniques for fraud detection and provides implementation guidance for an effective fraud prevention solution to help you detect fraud early.

Card Fraud Detection Analysis of European Cardholders from September 2013.

Credit card transaction fraud detection model that utilized credit card transaction data from 2010, incorporating data from both credit card companies and merchants.

Cloud-native risk scoring (FastAPI + Streamlit), Azure CI/CD, Responsible AI

SQL project simulating a fraud detection rules engine with risk scoring and case prioritization.