Anomaly Detection in Financial Transactions using Machine Learning and Blockchain Technology

Abstract

Propose: Digitally signed transactions are the cornerstone of data connected to repressive chains that are kept in distributed ledgers. In particular, cryptocurrency has grown to become a place of refuge for criminal financing operations. These illegal patterns may be found by machine learning. With the advent of machine learning (ML), the capabilities to identify and mitigate fraudulent activities have significantly improved. This paper extract outlines the application of ML techniques in detecting anomalous transactions, highlighting the methodologies, challenges, and future directions. Anomaly detection in financial transactions is a critical task for maintaining the integrity and security of financial systems.