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.

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 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

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

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

An AI-powered fraud detection system that uses machine learning to detect suspicious financial transactions in real time. Features include interactive dashboards, secure authentication, and comprehensive reporting for fintech risk analysis.

A deep learning-based web application for deepfake video detection, powered by the fine-tuned XceptionNet (Extreme Inception) model. The system allows users to upload videos for deepfake detection, processes them through the trained model, and provides results via a clean Django-based web interface.

This GitHub repository provides a comprehensive set of tools and algorithms for detecting fraud anomalies in various data sources. Fraudulent activities can have severe consequences, impacting businesses and individuals alike. With this repository, we aim to empower researchers with effective techniques to identify and prevent fraudulent behavior.

Machine learning model to detect fraudulent mass subscriptions by analyzing user behavior patterns like tab switching, typing speed, and navigation activity.

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!

A sample website integrating the ComplyCube SDK.

I created a website using Flask framework for detecting financial fraud using Machine Learning Model