[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126448-en":3,"doc-seo-126448-105":31,"detail-sidebar-cat-0-en-105":93},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126448,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Machine Learning and Artificial Intelligence in FinTech - Driving Innovation in Digital Payments, Fraud Detection, and Financial Inclusion","This study investigates how machine learning (ML) and artificial intelligence (AI) reshape financial technology (FinTech) across three linked areas: digital payments, fraud detection, and financial inclusion. While AI-driven services have expanded rapidly, evidence connecting specific algorithmic approaches to measurable outcomes remains scattered across disciplines. Using a mixed-methods design with a 2018–2023 systematic literature review, quantitative adoption analysis across 45 countries and 125 institutions, and case studies of six implementations, the research assesses transaction-scale, incident, and inclusion metrics. Findings show strong performance gains, including faster processing, higher fraud-detection accuracy, and broader credit access with improved loan approvals.","Machine Learning and Artificial Intelligence in FinTech: Driving Innovation in Digital Payments, Fraud Detection, and Financial Inclusion  \nEdith Agberxonu, Abdulateef Disu, ChidinmaDike, Toyosi Mustapha, Lawrence Abakah Received : 23 July 2023/Accepted: 9 September 2023/Published: 19 September 2023  \nAbstract: This study examines how machine learning (ML) and artificial intelligence (AI) technologies are fundamentally reshaping financial technology (FinTech), with particular emphasis on three interconnected domains: digital payments, fraud detection, and financial inclusion. Despite the rapid proliferation of AI-driven financial services, comprehensive empirical evidence linking specific algorithmic approaches to measurable outcomes remains fragmented across disciplinary boundaries. We employ a mixed-methods research design combining systematic literature review (covering 2018– 2023), quantitative analysis of adoption patterns across 45 countries and 125 financial institutions, and detailed case study examination of six leading FinTech implementations. Our quantitative analysis incorporates transaction data from over 50 million digital payment events, fraud detection records encompassing 2.3 million documented incidents, and financial inclusion metrics from the World Bank’s Global Findex Database. Results demonstrate substantial performance improvements across all three domains. AIenhanced digital payment systems achieve 67% reduction in average processing time while maintaining enhanced security protocols. Machine learning-based fraud detection systems exhibit accuracy rates between 94–98% with false positive reductions approaching 70 % compared to rule-based alternatives. Alternative credit scoring models powered by ML algorithms expand financial access by 25–40% among previously underserved populations, with loan approval rates 67% higher than traditional methods while maintaining comparable or improved default rates. Our conceptual framework positions AI/ML as an enabling infrastructure that simultaneously  \ntransforms and is transformed by advances in payments, fraud detection, and inclusion, with feedback loops distinguishing our approach from linear input-output models common in earlier work.  \nKeywords: AI/ML, FinTech, Digital Payments, Fraud Detection, Financial Inclusion, Alternative Credit Scoring.  Edith Agberxonu  \nMcCombs School of Business, University of Texas at Dallas, Texas, USA [Email:](Email: kafuiedith1@gmail.com)[ ](Email: kafuiedith1@gmail.com)[kafuiedith1@gmail.com](Email: kafuiedith1@gmail.com)  \nAbdulateef Disu  \nDepartment of Computer Science, School of Computing and Engineering Sciences, Babcock University, Ilishan-Remo, Ogun State, Nigeria  \nEmail: [tosinekpetidisu@gmail.com](tosinekpetidisu@gmail.com)  \nChidinma Dike  \nDepartment of Business Administration, Faculty of Management Sciences, Imo State University, Imo State, Nigeria  \n[Email:](Email: Chimarv1234@gmail.com)[ ](Email: Chimarv1234@gmail.com)[Chimarv1234@gmail.com](Email: Chimarv1234@gmail.com)[ ](Email: Chimarv1234@gmail.com)Toyosi Mustapha  \nCollege of Business, Southern New Hampshire University, Manchester, New Hampshire, USA  \n[Email:](Email: Mtoyosi101@gmail.com)[ ](Email: Mtoyosi101@gmail.com)[Mtoyosi101@gmail.com](Email: Mtoyosi101@gmail.com)[ ](Email: Mtoyosi101@gmail.com)Lawrence Abakah  \nMcCombs School of Business, The University of Texas at Austin, Texas, USA  \nEmail: [lawrenceabakah715@gmail.com](lawrenceabakah715@gmail.com)  \n[1.0 Introduction](1.0 Introduction)  \nThe financial services landscape has undergone a profound transformation over the past decade. Traditional banking infrastructure, once characterized by extensive branch networks and face-to-face interactions, now coexists—often uneasily—with fully digital platforms that process  \nmillions of transactions per second without direct human intervention (Arner, Barberis,& Buckley, 2016; Ademilua & Areghan, 2022; Okolo 2021) . This transformation, commonly labeled as th","cbCaidlroYTYJqBL","https://ap.wps.com/l/cbCaidlroYTYJqBL","pdf",1256593,5,1,23,"English","en",105,"# Introduction\n## AI in the FinTech revolution\n## Why current academic understanding is fragmented\n## Need for integrated evidence","[{\"question\":\"What three domains does the study focus on in FinTech innovation?\",\"answer\":\"The study focuses on digital payments, fraud detection, and financial inclusion, treating them as interconnected domains influenced by AI/ML.\"},{\"question\":\"How does the research design combine evidence sources?\",\"answer\":\"It uses a mixed-methods approach: a systematic literature review (2018–2023), quantitative analysis of adoption patterns across 45 countries and 125 institutions, and detailed case studies of six leading FinTech implementations.\"},{\"question\":\"What performance improvements does the study report for digital payments, fraud detection, and credit access?\",\"answer\":\"It reports a 67% reduction in average processing time for AI-enhanced payments, fraud detection accuracy of about 94–98% with large false-positive reductions versus rule-based methods, and ML-powered alternative credit scoring that increases financial access by roughly 25–40% while improving loan approval rates.\"}]","Machine Learning and Artificial Intelligence in FinTech - Driving Innovation in Digital Payments, Fraud Detection, and Financial Inclusion | PDF",1785905119,58,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-and-artificial-intelligence-in-fintech-driving-innovation-in-digital-payments-fraud-detection-and-financial-inclusion","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-and-artificial-intelligence-in-fintech-driving-innovation-in-digital-payments-fraud-detection-and-financial-inclusion/126448/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What three domains does the study focus on in FinTech innovation?","Question",{"text":77,"@type":78},"The study focuses on digital payments, fraud detection, and financial inclusion, treating them as interconnected domains influenced by AI/ML.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the research design combine evidence sources?",{"text":82,"@type":78},"It uses a mixed-methods approach: a systematic literature review (2018–2023), quantitative analysis of adoption patterns across 45 countries and 125 institutions, and detailed case studies of six leading FinTech implementations.",{"name":84,"@type":75,"acceptedAnswer":85},"What performance improvements does the study report for digital payments, fraud detection, and credit access?",{"text":86,"@type":78},"It reports a 67% reduction in average processing time for AI-enhanced payments, fraud detection accuracy of about 94–98% with large false-positive reductions versus rule-based methods, and ML-powered alternative credit scoring that increases financial access by roughly 25–40% while improving loan approval rates.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]