This project implements an end-to-end pipeline for detecting SMS spam using LLM-based embeddings (Mistral), interpretable machine learning, and risk-aware reporting.

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.

Built an unsupervised Machine Learning pipeline to detect anomalies in Bitcoin transactions by selecting 19 key features from 700.

A machine learning-based web application to detect financial fraud in real time. Users can input transaction details and get instant fraud predictions.

A sample website integrating the ComplyCube SDK.

A collection of projects where I worked on building anomaly detection pipelines. This rep covers code for EDA, outlier detection, and stock analysis.

The visual graph of fraud detection website

Production MLOps pipeline for fraud detection with automated testing, monitoring, and zero-downtime deployments

Detecting fraud on online customer transactions

Data preprocessing and classification for the detection of fraudulent transactions

🛡️ SecureCard-AI: A high-performance credit card fraud detection system implemented in a Jupyter Notebook, achieving 99.97% accuracy.

Machine learning models for credit card fraud detection with baseline vs SMOTE comparison, evaluated using Recall, Precision, and F1-score.

🎯 A comprehensive Bank Identification Number (BIN) intelligence platform for e-commerce fraud detection and prevention.

A book project accompanying the CopyDetect package. The book provides comprehensive coverage of response similarity analysis using R.

Ambriel Anti Fraud & Aml Complience Documentation

Using R Language to predict whether a user will download an app after clicking a mobile app advertisement. Click on the link below to see more details!

The objective of this project is to develop a robust classification model capable of identifying and flagging potentially fraudulent job postings on LinkedIn.

🧠 ML training pipeline within our MLOps architecture. Manages the training, evaluation, and versioning of the CatBoost model.

Test repo for the Smartboard project

Machine Learning project to detect fraudulent transactions on ethereum blockchain, reducing false positives