fraud-analytics

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.

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

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

Reproducible AML/FT and financial crime analytics with rules, backtesting, explainable ML, customer-aware evaluation, and human-governed AI architecture.

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

Enterprise AI-powered fraud detection platform with real-time monitoring, ensemble machine learning, FastAPI backend, analyst workflows, fraud case management, and intelligent fraud analytics.

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.

End-to-end communications surveillance platform using NLP, network analytics, risk scoring, testing and interactive investigation dashboards built on the Enron Email Dataset.

End-to-end banking fraud operations platform with synthetic data, ML scoring, rules, FastAPI, React, Power BI, Airflow, and Kafka foundations.

Explainable fraud analytics and financial risk decisioning with temporal entity intelligence, OOT validation, customer-impact analysis, and governed AI.

AWS banking transaction risk analytics lakehouse using Kinesis, S3, Glue/PySpark, Lambda, Athena, Redshift, Lake Formation, data quality and CI/CD.

Bank account-opening fraud alert triage: risk-ranked queue, reason codes, a guardrailed local LLM assistant, and Tableau tables.

Transaction fraud analytics on 284K+ card transactions: SQL segment profiling, rule-based risk scoring, and a Power BI dashboard.

Fraud-risk scoring backend with FastAPI, XGBoost, real OpenML holdout analysis, review-budget metrics, audit workflow, and drift monitoring.

Enterprise Banking Fraud Analytics using Snowflake, SQL, Python, and Power BI.

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

Cloud-native risk scoring (FastAPI + Streamlit), Azure CI/CD, Responsible AI

End-to-end fraud & suspicious transaction analytics using SQL, Python, Tableau & Streamlit to detect fraud patterns, analyze risk and support investigations.

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