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

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.

End-to-end Databricks/AWS lakehouse project with Bronze/Silver/Gold layers, GDPR-aware data quality, dashboard views and AI-ready fraud analytics.

Fraud risk operations analytics platform using BigQuery SQL, Python validation/modeling, threshold optimization, and a Dash dashboard on the synthetic PaySim dataset.

🛡️ 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! 🔐💯

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.

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

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

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

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.

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.

Explainable AI-powered telecom fraud detection system using Random Forest, Isolation Forest, Rule-Based Intelligence, SHAP Explainability, FastAPI, and Streamlit Dashboard for real-time fraud risk assessment.

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.

This project analyzes 284,000+ banking transactions to detect suspicious activity using time-series anomaly detection and an Agentic AI investigation workflow.

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