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.

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.

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.

Assignments for the semester Jun - Dec 2021 @ IIT Hyderabad

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

🛡️ 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! 🔐💯

An end-to-end predictive analytics pipeline and visual intelligence framework optimizing risk matrices and multi-tiered transaction verification queues for enterprise banking environments handling severe class imbalances.

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

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.

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.

A machine learning project for detecting fraudulent credit card transactions with real-time monitoring, risk scoring, and dashboard visualization.

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.

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.

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.

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.