ANALYSING AND DETECTING MONEYLAUNDERING ACCOUNTS

Project Code :TCMAPY1791

Objective

This project focuses on detecting suspicious transactions involved in money laundering using machine learning algorithms like SVM, AdaBoost, Naive Bayes, Logistic Regression, and Random Forest. A synthetic transaction dataset is used to train and evaluate models for classifying user behavior. The system includes user registration, login, data upload, and prediction viewing, built with Flask for the backend and HTML/CSS/JavaScript for the frontend.

Abstract

Money laundering is a major concern in financial systems and has found new ways to operate through online platforms, including social networks. This project aims to detect and analyze suspicious transactions that may be involved in money laundering activities within online social networks. Using a synthetic dataset designed for transaction monitoring, various machine learning algorithms—such as Support Vector Machine, AdaBoost, Naive Bayes, Logistic Regression, and Random Forest—are trained and evaluated to classify user behavior and transaction patterns. The system allows users to register, log in, upload data, and view prediction results. Flask is used as the backend framework, and HTML, CSS, and JavaScript are used for the frontend interface. By focusing on predictive modeling and pattern recognition, this project contributes to research in digital financial crime detection and enhances the understanding of illegal transaction behavior in virtual environments.

Keywords: Money laundering, machine learning, Flask, online social networks, classification, prediction, financial fraud, synthetic data, data analysis, transaction monitoring

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.