[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121509-en":3,"doc-seo-121509-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121509,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Development of a Web-Based Machine Learning Money Laundering Detection and Prevention Model","A web-based machine learning system for detecting and preventing money laundering is developed through a structured research methodology grounded in AML concepts, data mining, and machine learning theory. The work defines problem, objectives, scope, and a conceptual and theoretical framework, then builds the system via data acquisition, feature engineering, preprocessing, model architecture design, and iterative modelling. Validation and hyperparameter tuning guide training, while evaluation uses detection and prevention results with relevant metrics and statistical analysis. The study concludes with recommendations and areas for further research.","DEVELOPMENT OF A WEB-BASED MACHINE LEARNING MONEY LAUNDERING DETECTION AND PREVENTION MODEL  \nBY  \nHAMPO, JOHNPAUL ANENECHUKWU CHUKWUNONSO  \nJULY, 2024.  \nDEVELOPMENT OF A WEB-BASED MACHINE LEARNING MONEY LAUNDERING DETECTION AND PREVENTION MODEL  \nBY  \nHAMPO, JOHNPAUL ANENECHUKWU CHUKWUNONSO  \n(B.Sc.)  \n20164023558  \nA THESIS SUBMITTED TO  \nTHE POSTGRADUATE SCHOOL  \nFEDERAL UNIVERSITY OF TECHNOLOGY, OWERRI  \nIN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF SCIENCE (M.SC) DEGREE IN COMPUTER  \nSCIENCE.  \nJULY, 2024.  \nDEDICATION  \nThis research is dedicated to the memory of my father, Mr. Hampo, A. Cyprian.  \nACKNOWLEDGEMENTS  \nI am grateful to my supervisors Dr (Mrs.) E. C. Nwokorie and Dr (Mrs.) J. N. Odii for their guidance, suggestions and corrections to this work.  \nI will not fail to acknowledge all my lecturers: Prof. E. N. Erumaka, Prof. Aloy C. Onyeka, Prof.  \nE.O. Nwachukwu, Prof. Prince Asagba, Dr. (Mrs.) Juliet N. Odii, Dr. Celestine Njoku, Mr. Joseph I. Eke, Dr. Stanley A. Okolie, Dr. Jacinta C. Odirichukwu, Dr. (Mrs.) Uchenna C. Onyemauche, Dr. (Mrs.) Mercy E. Benson-Emenike, Mr. Stanley O. Diala, Dr. Chinwe G. Onukwugha, Dr. Chukwuma D. Anyiam, Dr. (Mrs.) Chidimma I. Okpalla, Mr. Godson E. Ahamba, Dr. Francisca O. Nwokoma, Dr. Donatus O. Njoku, Dr. Kelechi A. Douglas, Mr. Peter K. Joseph, Mr. Friday J. Oforma, Mrs. Ezi Achama Eke, Mr. Chukwudi N. Akujobi, Mrs. Idu E. Iheukwumere, Mr. Ikechukwu Onyeanu, Mrs. Stella Egwu-Ahaotu, Dr. C. P. Oleji in Computer Science Department and the able Dean of SICT, Prof. (Mrs.) U. F. Eze for their educational and moral support throughout my programme.  \nFinally, I sincerely appreciate my mother - Mrs Maria O. Hampo, siblings and in-laws who encouraged and supported me at diverse times throughout this programme and thesis.  \nI express my heartfelt thanks to all my colleagues notably Mr. Charles, Mr Kanu and Mrs Faith.  \nAbove all, I thank Yahweh for His mercies and grace upon my life and educational journey.  \nTABLE OF CONTENTS  \nTITLE PAGE  \nCOVER PAGE II  \nCERTIFICATION III  \nDEDICATION IV  \nACKNOWLEDGEMENTS V  \nTABLE OF CONTENTS VI  \nLIST OF TABLES X  \nLIST OF FIGURES XI  \nABSTRACT XII  \nCHAPTER ONE: INTRODUCTION 1  \n1.1 Background Information 1  \n1.2 Problem Statement 5  \n1.3 Objectives 6  \n1.4 Justification of Study 7  \n1.5 Scope of Study 7  \nCHAPTER TWO: LITERATURE REVIEW 8  \n2.1 Conceptual Framework 8  \n2.1.1 Money Laundering 8  \n2.1.2 Money Laundering Process 11  \n2.1.3 Anti-Money Laundering (AML) 13  \n2.1.4 Data Mining 14  \n2.1.5 Machine Learning (ML) 18  \n2.1.6 Methods for Money Laundering Detection 24  \n2.1.7 Regulatory and Compliance Measure 27  \n2.1.8 Regulatory and Compliance Measure – Issues 32  \n2.1.9 Financial Transaction in Nigeria 32  \n2.1.10 Money Laundering Cases in Nigeria 35  \n2.2 Theoretical Framework 37  \n2.3 Empirical Framework 42  \n2.4 Summary of Literatures Reviewed 46  \n2.5 Research Gap 49  \nCHAPTER THREE: RESEARCH METHODOLOGY 50  \n3.1 Software Methodology 50  \n3.2 Research Instrument 51  \n3.3 System Analysis 51  \n3.4 Model Analysis 53  \n3.5 Analysis Tools 54  \n3.6 Model Development 54  \n3.6.1 Model Architecture 54  \n3.6.2 Data Acquisition 55  \n3.6.3 Building the Model 57  \n3.6.4 Feature Engineering 58  \n3.6.5 Data Preprocessing 58  \n3.6.6 Modelling 58  \n3.6.7 Validation and Hyperparameter Tuning 58  \n3.6.8 Detection 59  \n3.6.9 Prevention 59  \n3.7 Model Design 59  \n3.7.1 Input Design 60  \n3.7.2 Interface Design 61  \n3.7.3 Program Design 61  \n3.7.4 Process Design 62  \n3.7.5 Database Design 65  \n3.7.6 Output/Report Design 66  \nCHAPTER FOUR: RESULT AND DISCUSSION 67  \n4.1 Model Detection and Prevention 67  \n4.1.1 Money Laundering Detection 67  \n4.1.2 Money Laundering Prevention 68  \n4.2 Results of Model Training and Testing 70  \n4.2.1 Money Laundering Detection 71  \n4.2.2 Money Laundering Prevention 72  \n4.3 Model Evaluation 73  \n4.4 Statistical Analysis and Finding 74  \nCHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS 77  \n5.1 Conclusion","cbCaieBe6tXrwO9y","https://ap.wps.com/l/cbCaieBe6tXrwO9y","pdf",2417207,1,118,"English","en",105,"# Chapter One: Introduction\n## Background Information\n## Problem Statement\n## Objectives\n## Justification of Study\n## Scope of Study\n# Chapter Two: Literature Review\n## Conceptual Framework\n## Theoretical Framework\n## Empirical Framework\n## Research Gap\n# Chapter Three: Research Methodology\n## Software Methodology\n## System Analysis\n## Model Development\n## Model Architecture and Data Processing\n## Validation and Hyperparameter Tuning\n# Chapter Four: Result and Discussion\n## Model Detection and Prevention\n## Results of Model Training and Testing\n## Model Evaluation\n## Statistical Analysis and Finding\n# Chapter Five: Conclusion and Recommendations\n## Conclusion\n## Recommendations\n## Suggestions for Further Studies\n## Contributions to Knowledge","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis addresses the need to detect and prevent money laundering using a machine learning approach implemented in a web-based system.\"},{\"question\":\"How is the model developed?\",\"answer\":\"Development involves data acquisition, feature engineering, data preprocessing, designing the model architecture, modelling, and then validation with hyperparameter tuning before detection and prevention components are finalized.\"},{\"question\":\"What is evaluated in the results section?\",\"answer\":\"The results include model training and testing outcomes for both detection and prevention, along with evaluation using performance metrics and statistical analysis, plus supporting analyses such as gender, profession, and experience.\"}]","Development of a Web-Based Machine Learning Money Laundering Detection and Prevention Model | 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