An application to detect fraudulent ecommerce transactions by analyzing the transaction log files.

Predictive modeling projects developed during the Risk & Fraud Analytics course (Master in Business Analytics & Big Data) at IE HST.

Fraud Detection Machine Learning algorithms on the Credit Card Fraud Dataset. Balancing classes with synthetic samples (SMOLE)

Description

🐍 Python · Machine Learning · Logistic Regression · Pandas · Scikit-Learn · Seaborn · Fraud Detection · Credit Risk Management 🐍

This project is a credit card fraud detection system using machine learning and speech recognition to identify fraudulent transactions. It employs a Support Vector Machine (SVM) model to classify transaction types based on clues provided via speech inputs.

Embedding transaction graphs to classify fraudulent Ethereum wallet addresses.

This is a comprehensive fraud detection system built using machine learning to identify fraudulent financial transactions in real-time. The project implements a Random Forest classifier trained on the PaySim dataset, handling highly imbalanced data using SMOTE (Synthetic Minority Over-sampling Technique) and engineered features with 99% accuracy.

Real-time fraud detection system for fintech using Azure Event Hubs, Functions, and Cosmos DB with Terraform IaC

A data analysis project that studies real and fake job posts to help people avoid scams and find genuine jobs.

Supervised ML pipeline for detecting suspicious transactions using a synthetic AML dataset. Includes EDA, feature engineering, SMOTE, and model evaluation.

A machine learning project for detecting credit card fraud using Logistic Regression, Decision Tree, Random Forest, and XGBoost. Includes SMOTE for class imbalance and ROC-AUC analysis for evaluation.

This project implements both supervised and unsupervised machine learning approaches to detect anomalous patterns in credit card transactions that could indicate loyalty program abuse. The system is designed to help financial institutions prevent fraud while maintaining customer trust.

Research on machine learning, deep learning, and ensemble methods in imbalanced fraud and anomaly detection scenarios.

Projects related to risk analytics and forecasting such as churn prediction, credit risk, company default, market risk, sales forecast, fraud, and many more.

Comparing the performance of different models in detecting credit card fraud before and after hyperparameter tuning

Fraudulent Transaction detection

End-to-end analytical data pipeline for credit card fraud detection using AWS, Snowflake, dbt, Airflow, and Streamlit.

Machine learning project to detect credit card fraud using XGBoost, Random Forest, Decision Tree and Logistic Regression