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

The Credit Card Fraud Detection System is a web-based machine learning application designed to analyze online financial transactions and detect potentially fraudulent activities. Built with Streamlit, TensorFlow, and Python, the system leverages an Autoencoder deep learning model trained on large-scale transaction data to identify abnormal transac

Multi-agent AI system for automated fraud detection and regulatory compliance built using Azure AI Foundry, Databricks, and Microsoft Agent Framework.

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.

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! 🔐💯

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.

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.

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

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

Assignments for the semester Jun - Dec 2021 @ IIT Hyderabad

Analyze fraud patterns, risk scores, and transaction data with SQL, Python, and BI dashboards for fintech fraud detection insights

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

Bank Risk Intelligence Dashboard built with Microsoft Fabric, Power BI Service and DAX

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 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.