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.

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

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.

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

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.

🛡️ 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.

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

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

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

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.

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

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.

Leakage-safe, capacity-aware transaction fraud scoring: calibrated risk model, rule catalogue, anomaly layer, and cost-based alert prioritisation with drift monitoring.