fraud-analysis

SaaS Email Deliverability & Abuse Intelligence — star-schema analytics project: a mid-year deliverability incident and a Free-tier abuse cluster, with Python data pipeline, DuckDB SQL, and a Power BI dashboard.

Interactive fraud analytics dashboard built using Power BI, Power Query, and DAX to analyze 2,133 payment transactions.

Power BI dashboard analyzing 61,000+ Supply chain orders across DataCo (2015-2017). Covers sales performance, profitability,delivery operations, fraud detection and strategic recommendations.

Smart Fraud Guard Wallet is an intelligent fintech mobile application designed to protect users from financial fraud using advanced artificial intelligence and cybersecurity techniques.

Cybersecurity & Threat Hunting Portfolio · DFIR Forensics · Reverse Engineering · MITRE ATT&CK Case Studies

🔍💼 Fraud Analysis EDA uncovers patterns and anomalies in fraudulent transactions through comprehensive exploratory data analysis. With visualizations like correlation heatmaps and time-based fraud trends, this project provides actionable insights to mitigate risks and enhance security.

Analytical portfolio demonstrating transaction monitoring, judgment-based alert review, and Excel-driven risk analysis across fraud, AML, and KYC workflows, with a focus on regulator-safe decisioning and operational consistency.

Self-teaching cybersecurity from an Economics + DeFi background. Documenting every lesson. Target: CompTIA Security+.

End-to-end fraud analysis project using Python, SQL and Excel.

Interactive Power BI dashboard for analyzing insurance claims, customer policies, claim trends, and fraudulent activities using DAX, Power Query, and data visualization.

ML-powered credit card fraud detection system with FastAPI, synthetic data, imbalanced classification, and a Next.js fraud monitoring dashboard

Fraud KPI dashboard project using Python and SQL to monitor fraud rate, transaction volume and financial impact.

End-to-end Data Analyst project analyzing insurance claims with SQL and Power BI: data cleaning, KPI analysis, fraud insights, data quality and dashboard reporting.

Fraud transaction analysis project using Python, SQLite, Pandas and Matplotlib to identify suspicious transaction patterns

PostgreSQL analysis of credit card transactions, fraud patterns, and customer spending behavior.

Chargeback risk analysis project using SQL and Python to identify high-risk customers, estimate financial losses and support fraud prevention.

Interactive fintech analytics dashboard built with Python, pandas, Plotly, and Streamlit for transaction, customer, fraud, and geographic analysis.

🔍 Analyze transaction data to identify fraud risks, streamline alert reviews, and ensure compliance in AML and KYC contexts using Excel-driven techniques.