Rethink Secure Score: risk-based scoring across Defender XDR, Entra, AD, Azure, ExposureGraph, and Shodan — weighted by consequence, Tier 0–3 criticality, and risk factors. Fix what attackers reach for first. Asset profiling, ready-to-use reports, executive summary. Zero footprint, MVP-built, free.

A data science project for detecting fraud in mobile money transactions using the PaySim dataset.

Loan default risk analysis using Power BI. Identifies high-risk borrower segments by credit score, DTI, and loan characteristics.

Healthcare Fraud Risk Analytics is an end-to-end data analytics project that leverages Python, PostgreSQL, Machine Learning, and Power BI to detect fraudulent healthcare claims, identify high-risk providers, analyze fraud patterns, quantify financial losses, and build predictive models. The project achieved a fraud detection ROC-AUC score of 0.9987

SQL-powered analysis of 25,000+ Indian credit card transactions to uncover regional spending patterns, growth opportunities, and risk indicators.

Performed end-to-end loan portfolio analysis by designing and relational modeling loan, customer, payment, deposit, and branch datasets in SQL, structuring data to support advanced analytics and reporting.

Anomaly detection on Atlanta Crime Dataset using Kibana ML with severity scoring · Internship @ Appolo Computers 2024

Phishing Email Detection & Security Awareness Report – Future Interns Task 02

End-to-end machine learning pipeline for detecting credit card fraud using Azure Databricks, PySpark, MLlib, and MLflow automation.