A Multiagent Based Approach to Money Laundering Detection and Prevention

Cláudio Alexandre, João Balsa

Abstract

The huge amount of bank operations that occur every day makes it extremely hard for financial institutions to spot malicious money laundering related operations. Although some predefined heuristics are used they aren’t restrictive enough, still leaving to much work for human analyzers. This motivates the need for intelligent systems that can help financial institutions fight money laundering in a diversity of ways, such as: intelligent filtering of bank operations, intelligent analysis of suspicious operations, learning of new detection and analysis rules. In this paper, we present a multiagent based approach to deal with the problem of money laundering by defining a multiagent system designed to help financial institutions in this task, helping them to deal with two main problems: volume and rule improvement. We define the agent architecture, and characterize the different types of agents, considering the distinct roles they play in the process.

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Paper Citation


in Bibtex Style

@conference{icaart15,
author={Cláudio Alexandre and João Balsa},
title={A Multiagent Based Approach to Money Laundering Detection and Prevention},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2015},
pages={230-235},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005281102300235},
isbn={978-989-758-073-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A Multiagent Based Approach to Money Laundering Detection and Prevention
SN - 978-989-758-073-4
AU - Alexandre C.
AU - Balsa J.
PY - 2015
SP - 230
EP - 235
DO - 10.5220/0005281102300235


in Harvard Style

Alexandre C. and Balsa J. (2015). A Multiagent Based Approach to Money Laundering Detection and Prevention . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-073-4, pages 230-235. DOI: 10.5220/0005281102300235