Geospatial data in AML risk management: A review of applications and best practices
1 Tecsys Inc, Alberta, Canada.
2 Lafarge Africa Plc, Ikoyi, Lagos.
3 Wayfair, Lutterworth, England, UK.
4 Zenith Bank Plc, Lagos, Nigeria.
5 Independent Researcher, Port Harcourt, Nigeria.
6 Sanctus Maris Concepts Ltd.
7 Independent Researcher, Lagos.
Review
Open Access Research Journal of Science and Technology, 2024, 12(01), 148–169.
Article DOI: 10.53022/oarjst.2024.12.1.0126
Publication history:
Received on 07 September 2024; revised on 18 October 2024; accepted on 21 October 2024
Abstract:
This review explores the role of geospatial data in enhancing Anti-Money Laundering (AML) risk management, focusing on its applications, best practices, and strategic implications for organizations. Geospatial data, combined with advanced analytical tools, provides critical insights into transaction patterns, customer locations, and geographic risk factors, enabling financial institutions to detect suspicious activities more effectively. The paper examines how geospatial data integration supports compliance with regulatory frameworks, bolsters transaction monitoring systems, and facilitates enhanced due diligence processes.
Through a systematic review of existing literature and case studies, this paper identifies key applications such as identifying high-risk jurisdictions, mapping transaction flows, and detecting cross-border money laundering schemes. Best practices are highlighted, including leveraging geospatial analytics in conjunction with machine learning algorithms, optimizing data governance, and ensuring interoperability across systems.
The findings suggest that organizations adopting these practices are better equipped to combat money laundering by improving risk assessment, reducing false positives, and enhancing investigative efficiency. Additionally, the review discusses the challenges of data privacy, regulatory compliance, and the technical complexities associated with geospatial data integration. The paper concludes by outlining future prospects, recommending further advancements in real-time geospatial analytics, cross-sector collaborations, and the adoption of emerging technologies to strengthen AML frameworks. This review serves as a comprehensive guide for financial institutions seeking to incorporate geospatial data into their AML strategies, driving both compliance and operational efficiency.
Keywords:
Geospatial Data; Anti-Money Laundering (AML); Risk Management; Predictive Analytics; Real-Time Geolocation; Financial Institutions; Compliance Workflows; Cross-Border Data Sharing, AI-Driven Geospatial Analytics; Fraud Detection; Transaction Monitoring; Data Privacy; Financial Crime Prevention; Data Accuracy; Regulatory Compliance
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0