[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118445-en":3,"doc-seo-118445-105":30,"detail-sidebar-cat-0-en-105":83},{"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},118445,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Modelling A Multi-Channel Messaging Framework - A Machine Learning Approach","Multi-channel messaging (MCM) systems integrate heterogeneous channels to deliver messages to Financial Services Institutions’ customers, while machine learning enables channel availability prediction, dynamic assignment, and ongoing monitoring of customer behavior. The research evaluates machine learning algorithms for optimal channel selection, emphasizing the multi-armed bandit setting and related Tug of War and Upper Confidence Bound approaches. A unified framework integrates web channels including Facebook, WhatsApp, Instagram, SMS, and Email with a decision module that learns over time using acknowledgement feedback, minimal memory, and lightweight learning. The work specifies a supporting software architecture, employs a simulation-based evaluation, and uses an Enterprise Service Bus to update channel weights dynamically based on frequently used channels to improve resource utilization and support scheduling and mobility.","MODELLING A MULTI-CHANNEL MESSAGING FRAMEWORK: A MACHINE LEARNING  \nAPPROACH  \nby  \nOLUSOLA OLUSEUN SALAMI  \nsubmitted in accordance with the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nin the subject  \nCOMPUTER SCIENCE  \nat the  \nUNIVERSITY OF SOUTH AFRICA  \nSUPERVISOR: Prof E Mnkandla  \n09th January 2023  \nDECLARATION  \nName: Olusola Oluseun Salami  \nStudent Number: 49920847  \nDegree: PhD Computer Science  \nExact wording of the title of the thesis as appearing on the electronic copy submitted for examination:  \nModelling A Multi-Channel Messaging Framework: A Machine Learning Approach  \n\n|  |\n| --- |\n|  |\n|  |\n\nI declare that the above thesis is my own work and that all the sources that I have used or quoted have been indicated and acknowledged by means of complete references.  \nI further declare that I submitted the thesis to originality checking software and that it falls within the accepted requirements for originality.  \nI further declare that I have not previously submitted this work, or part of it, for examination at Unisa for another qualification or at any other higher education institution.  \n(The thesis will not be examined unless this statement has been submitted.)  \n________________________  \n09-Jan-2023  \nSIGNATURE DATE  \nACKNOWLEDGEMENTS  \nSpecial thanks to Almighty God for the strength, knowledge and understanding that he bestowed on me his creation during this study. In addition, many thanks and gratitude to my amiable supervisor Prof. Ernest Mnkandla for his advice, support and guidance on this journey, in this emerging area of machine learning and artificial intelligence. My appreciation also goes to my wife Oyenike and children (Olusolafunmi Francis, Oluwadara Anne-Marie and Oluwaseyi Zoe) for their support and time taken in family way to progress this study, I will make this up with all of you. I also thank my Parents, Surv. & Dr. (Mrs. ) Y.A Salami (FNIS) for their indelible believe in education for all your children (Dr. (Mrs. ) Abiola Olubiyi, Olugbenga, Abiodun Salami and Oluwafolakemi Adebayo) and most especially the sacrifice for the family. To Dr. Olalekan Samuel Ogunleye for your support and encouragement all through my study. To Dr. Debo Owoseni and Oluwaseun Mary (BHM), for your encouragement assistance in reviewing my papers including insights and inputs on the flow which were adopted. Sincere thanks to Dr. Funmilade Faniyi for his valuable external perspective on this research area as it applies to behavioural learning and adaptability. Without all of you and your valuable contributions this study might not have been possible.  \nABSTRACT  \nMulti-channel messaging (MCM) systems have been implemented to integrate heterogeneous messaging channels for message delivery to Financial Services Institutions (FSIs) customers worldwide. However, implementations utilizing machine learning techniques to determine channel availability, dynamic assignment, and monitoring of customer patterns are being explored with newer technological advancements. Such approaches are used to investigate how Integrated multi-channel messaging can be implemented using machine learning algorithms to enable effective and efficient dynamic channel selection and integration methods.  \nThis research explored and investigated the various machine learning algorithms for optimal channel selection. The study delved into applying these algorithms and their use to channel selection, including the end-user context, focusing on the multi-armed bandit (MAB) problem, Tug of War and Upper Confidence Bound algorithm in providing a novel approach to solving this problem.  \nThe study presented a framework that fully integrates different web channels (Facebook, WhatsApp, Instagram IM, SMS, and Email) with a decision-making module and machine learning for the model to learn customer patterns over time. This framework uses a minimal memory and computation capability which employs simple learning procedures from the machine learning alg","cbCaidBPbVZNS8qi","https://ap.wps.com/l/cbCaidBPbVZNS8qi","pdf",11921736,1,209,"English","en",105,"# Table of Contents\n## Declaration\n## Acknowledgements\n## Abstract\n## Table of Contents","[{\"question\":\"How is the effectiveness of the framework evaluated?\",\"answer\":\"Effectiveness is assessed using a simulation-based performance analysis method. The architecture is also described in support of an MCM-enabled customer alert system used by financial service institutions.\"}]","Modelling A Multi-Channel Messaging Framework - A Machine Learning Approach | PDF",1785683652,527,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"modelling-a-multi-channel-messaging-framework-a-machine-learning-approach","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/modelling-a-multi-channel-messaging-framework-a-machine-learning-approach/118445/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How is the effectiveness of the framework evaluated?","Question",{"text":75,"@type":76},"Effectiveness is assessed using a simulation-based performance analysis method. 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