Home / Current Issue / Paper 1713552
An Integrated Cybersecurity and Anti-Money Laundering Governance Framework for Financial Crime Prevention
Subject area: Science,Engineering and Technology · Area of research: Cybersecurity
DOI: https://doi.org/10.64388/IREV4I11-1713552
Abstract
Financial crime remains a persistent challenge for the global financial system, with cybersecurity breaches and money laundering schemes posing significant operational, regulatory, and reputational risks. While financial institutions have traditionally addressed these issues through discrete compliance, risk management, and IT security frameworks, increasing interconnectivity, digitisation, and sophistication of cybercriminal tactics have highlighted the need for integrated governance strategies. This paper proposes an integrated cybersecurity and anti-money laundering (AML) governance framework designed to prevent financial crimes through a cohesive, multi-layered approach. Drawing on contemporary literature and regulatory guidance, the framework synthesises organisational governance, technological safeguards, operational processes, compliance mechanisms, and stakeholder engagement into a unified model. The framework addresses both preventative and detective measures, incorporating risk assessment, threat intelligence, transaction monitoring, and employee training while ensuring alignment with existing legal and regulatory obligations. This study contributes to the literature by presenting a structured, conceptual model that bridges traditional AML controls with cybersecurity governance, emphasising proactive risk mitigation, real-time monitoring, and cross-functional integration. The findings have implications for financial institutions seeking to enhance their resilience to financial crimes and for regulators aiming to develop more effective oversight mechanisms.
Keywords
Cybersecurity, Anti-Money Laundering, Financial Crime Prevention, Governance Framework, Risk Management, Compliance.
References
[1] Abass, O. S., Balogun, O., & Didi, P. U. (2019). A Predictive Analytics Framework for Optimizing Preventive Healthcare Sales and Engagement Outcomes. IRE Journals, 2(11), 497–503.
[2] Abass, O. S., Balogun, O., & Didi, P. U. (2020). A Sentiment-Driven Churn Management Framework Using CRM Text Mining and Performance Dashboards. IRE Journals, 4(5), 251–259.
[3] Abayomi, A. A., Odofin, O. T., Ogbuefi, E., Adekunle, B. I., & Agboola, O. A. (2020). Evaluating Legacy System Refactoring for Cloud-Native Infrastructure Transformation in African Markets.
[4] Abbasi, B., Babaei, T., Hosseinifard, Z., Smith-Miles, K., & Dehghani, M. (2020). Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management. Computers and Operations Research, 119. https://doi.org/10.1016/j.cor.2020.104941
[5] Abdulsalam, R., Farounbi, B. O., & Ibrahim, A. K. (2020). Financial Governance and Fraud Detection in Public Sector Payroll Systems: A Model for Global Application.
[6] Adeyoyin, O., Awanye, E. N., Morah, O. O., & Ekpedo, L. (2020). A Conceptual Framework Linking Financial Strategy and Operational Excellence in Manufacturing Firms.
[7] Ahmad, A., Desouza, K. C., Maynard, S. B., Naseer, H., & Baskerville, R. L. (2020). How integration of cyber security management and incident response enables organizational learning. Journal of the Association for Information Science and Technology, 71(8), 939–953. https://doi.org/10.1002/ASI.24311
[8] Aifuwa, S. E., Oshoba, T. O., Ogbuefi, E., Ike, P. N., & Nnabueze, S. B. (2020). Predictive Analytics Models Enhancing Supply Chain Demand Forecasting Accuracy and Reducing Inventory Management Inefficiencies. International Journal of Multidisciplinary Research and Growth Evaluation, 1.
[9] Akintayo, O. D., Ifeanyi, C. N., & Onunka, O. (2020). A Conceptual Lakehouse-DevOps Integration Model for Scalable Financial Analytics in MultiCloud Environments. International Journal of Multidisciplinary Research and Growth Evaluation, 1.
[10] Akpe, O. E., Ogeawuchi, J. C., Abayomi, A. A., Agboola, O. A., & Ogbuefi, E. (2020). A Conceptual Framework for Strategic Business Planning in Digitally Transformed Organizations. Iconic Research and Engineering Journals, 4(4), 207–222. https://www.irejournals.com/paper-details/1708525
[11] Alami, H., Gagnon, M. P., Ag Ahmed, M. A., & Fortin, J. P. (2019). Digital health: Cybersecurity is a value creation lever, not only a source of expenditure. Health Policy and Technology, 8(4), 319–321. https://doi.org/10.1016/J.HLPT.2019.09.002
[12] Alvarenga, A., & Tanev, G. (2017). A Cybersecurity Risk Assessment Framework that Integrates Value-Sensitive Design. Technology Innovation Management Review, 7(4), 32–43. https://doi.org/10.22215/TIMREVIEW/1069
[13] Amatare, S. A., & Ojo, A. K. (2020). Predicting customer churn in telecommunication industry using convolutional neural network model. IOSR Journal of Computer Engineering, 22(3), 54–59.
[14] Amini-Philips, A., Ibrahim, A. K., & Eyinade, W. (2020). Designing Data-Driven Revenue Assurance Systems for Enhanced Organizational Accountability. International Journal of Multidisciplinary Research and Growth Evaluation, 1.
[15] Asata, M. N., Nyangoma, D., & Okolo, C. H. (2020). Reframing Passenger Experience Strategy: A Predictive Model for Net Promoter Score Optimization. Iconic Research and Engineering Journals, 4(5), 208–227.
[16] Ashiedu, B. I., Ogbuefi, E., Nwabekee, S., Ogeawuchi, J. C., & Abayomi, A. A. (2020). Developing Financial Due Diligence Frameworks for Mergers and Acquisitions in Emerging Telecom Markets. Iconic Research and Engineering Journals, 4(1), 183–196. https://www.irejournals.com/paper-details/1708562
[17] Ayanbode, N., Cadet, E., Etim, E. D., Essien, I. A., & Ajayi, J. O. (2019). Deep learning approaches for malware detection in large-scale networks. IRE Journals, 3(1), 483–502.
[18] Babatunde, L. A., Etim, E. D., Essien, I. A., Cadet, E., Ajayi, J. O., Erigha, E. D., & Obuse, E. (2020). Adversarial machine learning in cybersecurity: Vulnerabilities and defense strategies. Journal of Frontiers in Multidisciplinary Research, 1(2), 31–45. https://doi.org/10.54660/JFMR.2020.1.2.31-45
[19] Balogun, O., Abass, O. S., & Didi, P. U. (2020a). A Behavioral Conversion Model for Driving Tobacco Harm Reduction Through Consumer Switching Campaigns. IRE Journals, 4(2), 348–355.
[20] Balogun, O., Abass, O. S., & Didi, P. U. (2020b). A Market-Sensitive Flavor Innovation Strategy for E-Cigarette Product Development in Youth-Oriented Economies. IRE Journals, 3(12), 395–402.
[21] Barbon, A., Di Maggio, M., Franzoni, F., & Landier, A. (2019). Brokers and Order Flow Leakage: Evidence from Fire Sales. Journal of Finance, 74(6), 2707–2749. https://doi.org/10.1111/JOFI.12840
[22] Bhattacharyya, S., Chattopadhyay, H., Biswas, R., Ewim, D. R. E., & Huan, Z. (2020). Influence of inlet turbulence intensity on transport phenomenon of modified diamond cylinder: a numerical study. Arabian Journal for Science and Engineering, 45(2), 1051–1058.
[23] Bukhari, T. T., Oladimeji, O., & Etim, E. D. (2019a). A Predictive HR Analytics Model Integrating Computing and Data Science to Optimize Workforce Productivity Globally. IRE Journals, 3(4).
[24] Bukhari, T. T., Oladimeji, O., & Etim, E. D. (2019b). Toward Zero-Trust Networking: A Holistic Paradigm Shift for Enterprise Security in Digital Transformation Landscapes. IRE Journals, 3(2).
[25] Bukhari, T. T., Oladimeji, O., Etim, E. D., & Ajayi, J. O. (2018). A Conceptual Framework for Designing Resilient Multi-Cloud Networks Ensuring Security, Scalability, and Reliability Across Infrastructures. IRE Journals, 1(8), 164–173.
[26] Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86–97. https://doi.org/10.1016/J.IJINFOMGT.2019.03.004
[27] Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly: Management Information Systems, 36(4), 1165–1188. https://doi.org/10.2307/41703503
[28] Cherdantseva, Y., Burnap, P., Blyth, A., Eden, P., Jones, K., Soulsby, H., & Stoddart, K. (2016). A review of cyber security risk assessment methods for SCADA systems. Computers and Security, 56, 1–27. https://doi.org/10.1016/j.cose.2015.09.009
[29] Collard, G., Ducroquet, S., Disson, E., & Talens, G. (2017). A definition of Information Security Classification in cybersecurity context. Proceedings - International Conference on Research Challenges in Information Science, 77–82. https://doi.org/10.1109/RCIS.2017.7956520
[30] Coventry, L., & Branley, D. (2018). Cybersecurity in healthcare: A narrative review of trends, threats and ways forward. Maturitas, 113, 48–52. https://doi.org/10.1016/J.MATURITAS.2018.04.008
[31] Dano, U. L., Balogun, A. L., Abubakar, I. R., & Aina, Y. A. (2020). Transformative urban governance: confronting urbanization challenges with geospatial technologies in Lagos, Nigeria. GeoJournal, 85(4), 1039–1056. https://doi.org/10.1007/S10708-019-10009-1/TABLES/3
[32] de Melo e Silva, A., Gondim, J. J. C., de Oliveira Albuquerque, R., & Villalba, L. J. G. (2020). A methodology to evaluate standards and platforms within cyber threat intelligence. Future Internet, 12(6). https://doi.org/10.3390/FI12060108
[33] Didi, P. U., Abass, O. S., & Balogun, O. (2020a). Integrating AI-Augmented CRM and SCADA Systems to Optimize Sales Cycles in the LNG Industry. IRE Journals, 3(7), 346–354.
[34] Didi, P. U., Abass, O. S., & Balogun, O. (2020b). Leveraging Geospatial Planning and Market Intelligence to Accelerate Off-Grid Gas-to-Power Deployment. IRE Journals, 3(10), 481–489.
[35] Engelking, B., Buchholz, W., & Köhne, F. (2020). Design principles for the application of machine learning in supply chain risk management: an action design research approach. 137–162. https://doi.org/10.1007/978-3-658-31898-7_8
[36] Essien, I. A., Cadet, E., Ajayi, J. O., Erigha, E. D., & Obuse, E. (2019). Integrated governance, risk, and compliance framework for multi-cloud security and global regulatory alignment. IRE Journals, 3(3), 215–221.
[37] Essien, I. A., Nwokocha, G. C., Erigha, E. D., Obuse, E., & Akindemowo, A. O. (2019a). A Digital Transformation Maturity Model for Driving Innovation in African Banking and Payments Infrastructure.
[38] Essien, I. A., Nwokocha, G. C., Erigha, E. D., Obuse, E., & Akindemowo, A. O. (2019b). AI-Driven Credit Scoring Systems and Financial Inclusion in Emerging Markets.
[39] Etim, E. D., Essien, I. A., Ajayi, J. O., Erigha, E. D., & Obuse, E. (2019a). AI-augmented intrusion detection: Advancements in real-time cyber threat recognition. IRE Journals, 3(3), 225–230.
[40] Etim, E. D., Essien, I. A., Ajayi, J. O., Erigha, E. D., & Obuse, E. (2019b). AI-augmented intrusion detection: Advancements in real-time cyber threat recognition. IRE Journals, 3(3), 225–231.
[41] Etim, E. D., Essien, I. A., Ajayi, J. O., Erigha, E. D., & Obuse, E. (2019c). AI-augmented intrusion detection: Advancements in real-time cyber threat recognition. IRE Journals, 3(3), 225–230.
[42] Evans, J., McKemmish, S., & Rolan, G. (2019). Participatory information governance: Transforming recordkeeping for childhood out-of-home Care. Records Management Journal, 29(1–2), 178–193. https://doi.org/10.1108/RMJ-09-2018-0041/FULL/PDF
[43] Evans-Uzosike, C. G., Evans-Uzosike, I. O., & Okatta. (2019). Strategic Human Resource Management: Trends, Theories, and Practical Implications. Iconic Research and Engineering Journals.
[44] Farounbi, B. O., Ibrahim, A. K., & Abdulsalam, R. (2020). Advanced Financial Modeling Techniques for Small and Medium-Scale Enterprises. International Journal of Multidisciplinary Research and Growth Evaluation, 1.
[45] Farounbi, B. O., Ibrahim, A. K., & Oshomegie, M. J. (2020a). Proposed Evidence-Based Framework for Tax Administration Reform to Strengthen Economic Efficiency. IRE Journals, 3(11), 318–327.
[46] Farounbi, B. O., Ibrahim, A. K., & Oshomegie, M. J. (2020b). Proposed evidence-based framework for tax administration reform to strengthen economic efficiency. Iconic Research and Engineering Journals, 3(11), 480–495.
[47] Ferrag, M. A., Babaghayou, M., & Yazici, M. A. (2020). Cyber security for fog-based smart grid SCADA systems: Solutions and challenges. Journal of Information Security and Applications, 52. https://doi.org/10.1016/j.jisa.2020.102500
[48] Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2016). Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing and Service Operations Management, 18(1), 69–88. https://doi.org/10.1287/MSOM.2015.0561
[49] Filani, O. M., Okpokwu, C. O., & Fasawe, O. (2020). Capacity Planning and KPI Dashboard Model for Enhancing Supply Chain Visibility and Efficiency.
[50] Framework for Improving Critical Infrastructure Cybersecurity, Version 1.1. (2018). https://doi.org/10.6028/NIST.CSWP.04162018
[51] Gbenle, T. P., Ogeawuchi, J. C., Abayomi, A. A., Agboola, O. A., & Uzoka, A. C. (2020). Advances in Cloud Infrastructure Deployment Using AWS Services for Small and Medium Enterprises. Iconic Research and Engineering Journals, 3(11), 365–381. https://www.irejournals.com/paper-details/1708522
[52] Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308–317. https://doi.org/10.1016/j.jbusres.2016.08.004
[53] Hahn, G. J., & Packowski, J. (2015). A perspective on applications of in-memory analytics in supply chain management. Decision Support Systems, 76, 45–52. https://doi.org/10.1016/J.DSS.2015.01.003
[54] Hasan, I., Jackowicz, K., Kowalewski, O., & Kozłowski, Ł. (2017). Do local banking market structures matter for SME financing and performance? New evidence from an emerging economy. Journal of Banking and Finance, 79, 142–158. https://doi.org/10.1016/J.JBANKFIN.2017.03.009
[55] Hashim, N. A., Abidin, Z. Z., Zakaria, N. A., Ahmad, R., & Puvanasvaran, A. P. (2018). Risk assessment method for insider threats in cyber security: A review. International Journal of Advanced Computer Science and Applications, 9(11), 126–130. https://doi.org/10.14569/IJACSA.2018.091119
[56] Hu, M., Babiskin, A., Wittayanukorn, S., Schick, A., Rosenberg, M., Gong, X., Kim, M. J., Zhang, L., Lionberger, R., & Zhao, L. (2019). Predictive Analysis of First Abbreviated New Drug Application Submission for New Chemical Entities Based on Machine Learning Methodology. Clinical Pharmacology and Therapeutics, 106(1), 174–181. https://doi.org/10.1002/CPT.1479
[57] Ike, P. N., Aifuwa, S. E., Nnabueze, S. B., Olatunde-Thorpe, J., & Ogbuefi, E. (2020). Utilizing Nanomaterials in Healthcare Supply Chain Management for Improved Drug Delivery Systems. [Journal Not Specified].
[58] Ilchenko, M. Y., Uryvsky, L. A., & Moshinskaya, A. V. (2017). Developing telecommunication strategies based on scenarios in the information community. Cybern. Syst. Analysis, 53(6), 905–913. https://doi.org/10.1007/s10559-017-9992-9
[59] Ilufoye, H., Akinrinoye, O. V, & Okolo, C. H. (2020a). A Scalable Infrastructure Model for Digital Corporate Social Responsibility in Underserved School Systems. International Journal of Multidisciplinary Research and Growth Evaluation, 1(3), 100–106.
[60] Ilufoye, H., Akinrinoye, O. V, & Okolo, C. H. (2020b). A strategic product innovation model for launching digital lending solutions in financial technology. International Journal of Multidisciplinary Research and Growth Evaluation, 1(3), 93–99.
[61] Jalali, M. S., & Kaiser, J. P. (2018). Cybersecurity in hospitals: A systematic, organizational perspective. Journal of Medical Internet Research, 20(5). https://doi.org/10.2196/10059
[62] Kabanda, S., Tanner, M., & Kent, C. (2018). Exploring SME cybersecurity practices in developing countries. Journal of Organizational Computing and Electronic Commerce, 28(3), 269–282. https://doi.org/10.1080/10919392.2018.1484598
[63] Kalkman, J. P., & Wieskamp, L. (2019). Cyber Intelligence Networks: A Typology. International Journal of Intelligence, Security, and Public Affairs, 21(1), 4–24. https://doi.org/10.1080/23800992.2019.1598092
[64] Kamau, E. N. (2018). Energy efficiency comparison between 2.1 GHz and 28 GHz based communication networks.
[65] Kamerer, J. L., & McDermott, D. (2020). Cybersecurity: Nurses on the Front Line of Prevention and Education. Journal of Nursing Regulation, 10(4), 48–53. https://doi.org/10.1016/S2155-8256(20)30014-4
[66] Kammoun, N., Bounfour, A., Özaygen, A., & Dieye, R. (2019). Financial market reaction to cyberattacks. Cogent Economics and Finance, 7(1). https://doi.org/10.1080/23322039.2019.1645584
[67] Kim, J., Kim, J., Jang, G. J., & Lee, M. (2017). Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection. Neural Networks, 87, 109–121. https://doi.org/10.1016/J.NEUNET.2016.12.002
[68] Kim, M., Jeong, J., & Bae, S. (2019). Demand forecasting based on machine learning for mass customization in smart manufacturing. ACM International Conference Proceeding Series, 6–11. https://doi.org/10.1145/3335656.3335658
[69] Košt’ál, K., Helebrandt, P., Belluš, M., Ries, M., & Kotuliak, I. (2019). Management and monitoring of IoT devices using blockchain. Sensors (Switzerland), 19(4). https://doi.org/10.3390/S19040856
[70] Kuehn, P., Riebe, T., Apelt, L., Jansen, M., & Reuter, C. (2020). Sharing of Cyber Threat Intelligence between States. Sicherheit & Frieden, 38(1), 22–28. https://doi.org/10.5771/0175-274X-2020-1-22
[71] Li, L., Liu, F., & Li, C. (2014). Customer satisfaction evaluation method for customized product development using Entropy weight and Analytic Hierarchy Process. Computers and Industrial Engineering, 77, 80–87. https://doi.org/10.1016/j.cie.2014.09.009
[72] Li, X., & Yao, R. (2020). A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour. Energy, 212. https://doi.org/10.1016/J.ENERGY.2020.118676
[73] Mei, Z., & Zirong, Y. (2016). Design of epidemic monitoring platform based on ArcGIS. Proceedings - 14th International Symposium on Distributed Computing and Applications for Business, Engineering and Science, DCABES 2015, 380–383. https://doi.org/10.1109/DCABES.2015.102
[74] Mgbame, A. C., Akpe, O. E., Abayomi, A. A., Ogbuefi, E., & Adeyelu, O. O. (2020). Barriers and Enablers of BI Tool Implementation in Underserved SME Communities. Iconic Research and Engineering Journals, 3(7), 211–226. https://www.irejournals.com/paper-details/1708221
[75] Mishra, S. (2018). Financial management and forecasting using business intelligence and big data analytic tools. Https://Doi.Org/10.1142/S2424786318500111, 05(02), 1850011. https://doi.org/10.1142/S2424786318500111
[76] Morah, O. O., Awanye, E. N., Ekpedo, L., & Adeyoyin, O. (2020). A Review of Leadership, Operational Efficiency, and Financial Strategy Integration in Corporations.
[77] MS Riaz, M. J. M. N. B. A. (2020). Predictive maintenance of textile machinery using machine learning techniques. SN Appl Sci, 2(7), 1–11. https://doi.org/10.1007/s42452-020-03427-5
[78] Mushinada, V. N. C., & Veluri, V. S. S. (2018). Investors overconfidence behaviour at Bombay Stock Exchange. International Journal of Managerial Finance, 14(5), 613–632. https://doi.org/10.1108/IJMF-05-2017-0093
[79] Nicholson, A., Webber, S., Dyer, S., Patel, T., & Janicke, H. (2012). SCADA security in the light of cyber-warfare. Computers and Security, 31(4), 418–436. https://doi.org/10.1016/j.cose.2012.02.009
[80] Nnaji, E. C., Adgidzi, D., Dioha, M. O., Ewim, D. R. E., & Huan, Z. (2019). Modelling and management of smart microgrid for rural electrification in sub-saharan Africa: The case of Nigeria. The Electricity Journal, 32(10).
[81] Nwafor, M. I., Uduokhai, D. O., & Ajirotutu, R. O. (2020a). Multi-Criteria Decision-Making Model for Evaluating Affordable and Sustainable Housing Alternatives. International Journal of Multidisciplinary Research and Growth Evaluation, 1.
[82] Nwafor, M. I., Uduokhai, D. O., & Ajirotutu, R. O. (2020b). Spatial Planning Strategies and Density Optimization for Sustainable Urban Housing Development. International Journal of Multidisciplinary Research and Growth Evaluation, 1.
[83] Nwafor, M. I., Uduokhai, D. O., Stephen, G., & Aransi, A. N. (2019a). Developing an Analytical Framework for Enhancing Efficiency in Public Infrastructure Delivery Systems. Iconic Research and Engineering Journals, 2(11), 657–670.
[84] Nwafor, M. I., Uduokhai, D. O., Stephen, G., & Aransi, A. N. (2019b). Quantitative Evaluation of Locally Sourced Building Materials for Sustainable Low-Income Housing Projects. Iconic Research and Engineering Journals, 3(4), 568–582.
[85] Nwani, S., Abiola-Adams, O., Otokiti, B. O., & Ogeawuchi, J. C. (2020). Building Operational Readiness Assessment Models for Micro, Small, and Medium Enterprises Seeking Government-Backed Financing. Journal of Frontiers in Multidisciplinary Research, 1(01), 38–43.
[86] Obuse, E., Erigha, E. D., Okare, B. P., Uzoka, A. C., Owoade, S., & Ayanbode, N. (2020a). Event-Driven Design Patterns for Scalable Backend Infrastructure Using Serverless Functions and Cloud Message Brokers. Iconic Research and Engineering Journals, 4(4), 300–318.
[87] Obuse, E., Erigha, E. D., Okare, B. P., Uzoka, A. C., Owoade, S., & Ayanbode, N. (2020b). Optimizing Microservice Communication with gRPC and Protocol Buffers in Distributed Low-Latency API-Driven Applications. Iconic Research and Engineering Journals, 4(3), 250–268.
[88] Obuse, E., Etim, E. D., Essien, I. A., Cadet, E., Ajayi, J. O., & Erigha, E. D. (2020). Explainable AI for cyber threat intelligence and risk assessment. Journal of Frontiers in Multidisciplinary Research, 1(2), 15–30.
[89] Ogunsola, O. E., Oshomegie, M. J., & Ibrahim, A. K. (2019). Conceptual model for assessing political risks in cross-border investments. Iconic Research and Engineering Journals, 3(4), 482–493.
[90] Okesiji, A., Oyasiji, O., Elebe, O., Imediegwu, C. C., Filani, O. M., & Umana, A. U. (2020). Blockchain-Enabled E-Governance: A Model for Enhancing Transparency in Developing Economies.
[91] Olufunke Omotayo, O. O. A., & Kuponiyi, A. (2020). Telehealth Expansion in Post-COVID Healthcare Systems: Challenges and Opportunities. Iconic Research and Engineering Journals, 3(10), 496–513.
[92] Omisola, J. O., Shiyanbola, J. O., & Osho, G. O. (2020a). A Predictive Quality Assurance Model Using Lean Six Sigma: Integrating FMEA, SPC, and Root Cause Analysis for Zero-Defect Production Systems. Unknown Journal.
[93] Omisola, J. O., Shiyanbola, J. O., & Osho, G. O. (2020b). A Predictive Quality Assurance Model Using Lean Six Sigma: Integrating FMEA, SPC, and Root Cause Analysis for Zero-Defect Production Systems. Unknown Journal.
[94] Omisola, J. O., Shiyanbola, J. O., & Osho, G. O. (2020c). A Systems-Based Framework for ISO 9000 Compliance: Applying Statistical Quality Control and Continuous Improvement Tools in US Manufacturing. Unknown Journal.
[95] Oneto, L., Fumeo, E., Clerico, G., Canepa, R., Papa, F., Dambra, C., Mazzino, N., & Anguita, D. (2017). Dynamic delay predictions for large-scale railway networks: Deep and shallow extreme learning machines tuned via thresholdout. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(10), 2754–2767. https://doi.org/10.1109/TSMC.2017.2693209
[96] Orlovskyi, D., & Kopp, A. (2020). A Business Intelligence Dashboard Design Approach to Improve Data Analytics and Decision Making.
[97] Osho, G. O. (2020a). Building Scalable Blockchain Applications: A Framework for Leveraging Solidity and AWS Lambda in Real-World Asset Tokenization. Unknown Journal.
[98] Osho, G. O. (2020b). Decentralized Autonomous Organizations (DAOs): A Conceptual Model for Community-Owned Banking and Financial Governance. Unknown Journal.
[99] Osho, G. O., Omisola, J. O., & Shiyanbola, J. O. (2020a). A Conceptual Framework for AI-Driven Predictive Optimization in Industrial Engineering: Leveraging Machine Learning for Smart Manufacturing Decisions. Unknown Journal.
[100] Osho, G. O., Omisola, J. O., & Shiyanbola, J. O. (2020b). An Integrated AI-Power BI Model for Real-Time Supply Chain Visibility and Forecasting: A Data-Intelligence Approach to Operational Excellence. Unknown Journal.
[101] Oshomegie, M. J., & Farounbi, B. O. (2020). Systematic Review of Tariff-Induced Trade Shocks and Capital Flow Responses in Emerging Markets. Iconic Research and Engineering Journals, 3(11), 504–521.
[102] Oshomegie, M. J., Farounbi, B. O., & Ibrahim, A. K. (2020). Proposed evidence-based framework for tax administration reform to strengthen economic efficiency. Journal of Frontiers in Multidisciplinary Research, 1(2), 131–141.
[103] Oshomegie, M. J., Ogunsola, O. E., & Olajumoke, B. (2019). Comprehensive Review of Quantitative Frameworks for Optimizing Fiscal Policy Response to Global Shocks.
[104] Owoade, S., Ogbuefi, E., Ubanadu, B. C., Daraojimba, A. I., & Akpe, O. E. (2020). Advances in Role-Based Access Control for Cloud-Enabled Operational Platforms. International Peer-Reviewed Journal, 4(2), 159–176.
[105] Palagin, A. V. (2017). Functionally oriented approach in research-related design. Cybern. Syst. Analysis, 53(6), 986–992. https://doi.org/10.1007/s10559-017-0001-0
[106] Park, K. T., Son, Y. H., & Noh, S. Do. (2020). The architectural framework of a cyber physical logistics system for digital-twin-based supply chain control. International Journal of Production Research, 1–22. https://doi.org/10.1080/00207543.2020.1788738
[107] Popescu, N. E. (2014). Entrepreneurship and SMEs Innovation in Romania. Procedia Economics and Finance, 16, 512–520. https://doi.org/10.1016/S2212-5671(14)00832-6
[108] Radziwill, N. M., & Benton, M. C. (2017). Cybersecurity Cost of Quality: Managing the Costs of Cybersecurity Risk Management. http://arxiv.org/abs/1707.02653
[109] Renaud, K., Flowerday, S., Warkentin, M., Cockshott, P., & Orgeron, C. (2018). Is the responsibilization of the cyber security risk reasonable and judicious? Computers and Security, 78, 198–211. https://doi.org/10.1016/J.COSE.2018.06.006
[110] Samtani, S., Abate, M., Benjamin, V., & Li, W. (2020). Cybersecurity as an industry: A cyber threat intelligence perspective. The Palgrave Handbook of International Cybercrime and Cyberdeviance, 135–154. https://doi.org/10.1007/978-3-319-78440-3_8
[111] Saxon, L. A., Varma, N., Epstein, L. M., Ganz, L. I., & Epstein, A. E. (2018). Factors influencing the decision to proceed to firmware upgrades to implanted pacemakers for cybersecurity risk mitigation. Circulation, 138(12), 1274–1276. https://doi.org/10.1161/CIRCULATIONAHA.118.034781
[112] Skopik, F., Settanni, G., & Fiedler, R. (2016). A problem shared is a problem halved: A survey on the dimensions of collective cyber defense through security information sharing. Computers and Security, 60, 154–176. https://doi.org/10.1016/j.cose.2016.04.003
[113] UL Dano, A. B. A. M. Y. K. I. A. M. M. Y. A. B. P. (2019). Flood susceptibility mapping using GIS-based analytic network process: A case study of Perlis, Malaysia. Water, 11(3), 615.
[114] Umoren, O., Didi, P. U., Balogun, O., & Abass, O. S. (2019). Evaluating the Strategic Role of Economic Research in Supporting Financial Policy Decisions and Market Performance Metrics. IRE Journals, 3(3), 248–258.
[115] Umoren, O., Didi, P. U., Balogun, O., & Abass, O. S. (2020a). A Conceptual Framework for Improving Marketing Outcomes Through Targeted Customer Segmentation and Experience Optimization Models. IRE Journals, 4(4), 347–357.
[116] Umoren, O., Didi, P. U., Balogun, O., & Abass, O. S. (2020b). Design and Execution of Data-Driven Loyalty Programs for Retaining High-Value Customers in Service-Focused Business Models. IRE Journals, 4(4), 358–371.
[117] Umoren, O., Didi, P. U., Balogun, O., Abass, O. S., & Akinrinoye, O. V. (2019). Linking Macroeconomic Analysis to Consumer Behavior Modeling for Strategic Business Planning in Evolving Market Environments. IRE Journals, 3(3), 203–213.
[118] V Jadhav, M. R. (2020). Optimization of fast moving consumer goods (FMCG) supply chain using machine learning approach. Int J Supply Chain Manag, 9(3), 221–230.
[119] Vishwanath, A., Neo, L. S., Goh, P., Lee, S., Khader, M., Ong, G., & Chin, J. (2020). Cyber hygiene: The concept, its measure, and its initial tests. Decision Support Systems, 128. https://doi.org/10.1016/J.DSS.2019.113160
[120] Wilbanks, B. A., & Langford, P. A. (2014). A review of dashboards for data analytics in nursing. CIN - Computers Informatics Nursing, 32(11), 545–549. https://doi.org/10.1097/CIN.0000000000000106
[121] Williams, C. M., Chaturvedi, R., & Chakravarthy, K. (2020). Cybersecurity risks in a pandemic. Journal of Medical Internet Research, 22(9). https://doi.org/10.2196/23692
How to cite this paper
@article{1713552,
author = {Oladapo Fadayomi, Adepeju Deborah Bello, Oghenemaiga Elebe, Nafiu Ikeoluwa Hammed, Gbenga Olumide Omoegun},
title = {An Integrated Cybersecurity and Anti-Money Laundering Governance Framework for Financial Crime Prevention},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {4},
number = {11},
pages = {584-600},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713552.pdf},
abstract = {Financial crime remains a persistent challenge for the global financial system, with cybersecurity breaches and money laundering schemes posing significant operational, regulatory, and reputational risks. While financial institutions have traditionally addressed these issues through discrete compliance, risk management, and IT security frameworks, increasing interconnectivity, digitisation, and sophistication of cybercriminal tactics have highlighted the need for integrated governance strategies. This paper proposes an integrated cybersecurity and anti-money laundering (AML) governance framework designed to prevent financial crimes through a cohesive, multi-layered approach. Drawing on contemporary literature and regulatory guidance, the framework synthesises organisational governance, technological safeguards, operational processes, compliance mechanisms, and stakeholder engagement into a unified model. The framework addresses both preventative and detective measures, incorporating risk assessment, threat intelligence, transaction monitoring, and employee training while ensuring alignment with existing legal and regulatory obligations. This study contributes to the literature by presenting a structured, conceptual model that bridges traditional AML controls with cybersecurity governance, emphasising proactive risk mitigation, real-time monitoring, and cross-functional integration. The findings have implications for financial institutions seeking to enhance their resilience to financial crimes and for regulators aiming to develop more effective oversight mechanisms.},
keywords = {Cybersecurity, Anti-Money Laundering, Financial Crime Prevention, Governance Framework, Risk Management, Compliance.},
month = {May},
doi = {https://doi.org/10.64388/IREV4I11-1713552}
}