Building a Risk Profile for Detecting Terrorism Financing

David Makiya, João Balsa

2025

Abstract

This paper presents a novel and theoretical approach to detecting terrorism financing through the development of risk-based transaction profiles using machine learning models. By integrating client and transaction data, the proposed framework employs unsupervised clustering techniques to identify suspicious financial activities. A multi-agent system, coupled with National Risk Indicators (NRI) and Long Short-Term Memory (LSTM) neural networks, can enhance predictive capabilities for easier detection. The proposed model addresses the evolving strategies of terrorist groups, offering financial institutions a dynamic and scalable tool for mitigating terrorism financing risks while improving accuracy in anti-money laundering (AML) and counter-terrorism financing (CTF) efforts.

Paper Citation

in Harvard Style

Makiya D. and Balsa J. (2025). Building a Risk Profile for Detecting Terrorism Financing. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 631-638. DOI: 10.5220/0013166700003890

in Bibtex Style

@conference{icaart25,
author={David Makiya and João Balsa},
title={Building a Risk Profile for Detecting Terrorism Financing},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={631-638},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013166700003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Building a Risk Profile for Detecting Terrorism Financing
SN - 978-989-758-737-5
AU - Makiya D.
AU - Balsa J.
PY - 2025
SP - 631
EP - 638
DO - 10.5220/0013166700003890
PB - SciTePress