Proposes an AI Agent that Continuously Learns and Adapts Anti-Money Laundering Rules for Blockchain-Based Transactions, Detecting Novel Laundering Strategies

Abstract

In decentralised blockchain ecosystems in particular, the growing complexity and number of financial transactions makes it very difficult to discover instances of money laundering. Due to the quick evolution of laundering tactics, traditional heuristics and rule-based systems are often inadequate. In order to combat AML in blockchain settings, this article suggests an AI system that relies on several agents. Intelligent agents are built into the system to discover suspicious trends in transactional data by independently monitoring, filtering, and analysing it. The agents' ability to learn new things over time is crucial since it allows them to change AML regulations and find new types of laundering that don't fit into current typologies. Managing the enormous amount of blockchain transactions and adapting AML detection algorithms to new threats are the two main concerns that the suggested architecture attempts to address. We go over the agent architecture, the functions they do, and how they work together to uncover new forms of money laundering. This smart and adaptable approach shows promise for improving anti-money-laundering capabilities in contemporary decentralised financial systems.