[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124308-en":3,"doc-seo-124308-105":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":20,"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},124308,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning-Based Analysis of Technology Acceptance in FinTech - A Behavioral Study Using Digital Wallet Data","Rapid growth of FinTech services, especially robo-advisors, is changing how users interact with digital financial platforms. This behavioral study examines whether user-level behavioral and transactional signals can predict technology acceptance, using daily app usage as an adoption proxy. Grounded in TAM and UTAUT, the analysis models constructs through satisfaction, loyalty points, and lifetime value. A Kaggle dataset of 7,000 users is used with four machine learning algorithms to classify high vs. low acceptance.","SN Computer Science (2025) 6:674  \n[https://doi.org/10.1007/s42979-025-04214-8](https://doi.org/10.1007/s42979-025-04214-8)  \nMachine Learning‑Based Analysis of Technology Acceptance in FinTech: A Behavioral Study Using Digital Wallet Data  \nSayyed Khawar Abbas1 · Muzzammil Hussain2 · Yagya Nath Rimal3  \nReceived: 17 June 2025 / Accepted: 11 July 2025 © The Author(s) 2025  \nAbstract  \nThe rapid growth of FinTech services, particularly robo-advisors, has transformed how individuals engage with digital financial platforms. Understanding the behavioral drivers of technology acceptance in this context is critical for enhancing adoption and designing more effective user experiences. This study investigates whether user-level behavioral and transactional data can be leveraged to predict technology acceptance, operationalized through daily app usage. Grounded in the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), the study uses behavioral proxies such as customer satisfaction, loyalty points, and lifetime value to reflect constructs like perceived usefulness, performance expectancy, and facilitating conditions. Using a real-world dataset of 7000 FinTech users sourced from Kaggle, we applied four machine learning algorithms, Logistic Regression, Support Vector Machine, Random Forest, and XGBoost, to classify users into high and low acceptance categories. Results revealed that ensemble models, particularly XGBoost, outperformed linear classifiers, achieving moderate improvements in precision and recall for the high-acceptance class. However, overall predictive performance remained constrained by class imbalance and overlapping behavioral patterns. These findings suggest that while machine learning can reveal patterns linked to technology acceptance, predictive precision remains limited without richer temporal and psychographic features. The study contributes to the evolving discourse on FinTech adoption by offering a data-driven lens to complement intention-based models and inform adaptive engagement strategies.  \nKeywords FinTech · Robo-advisors · Technology acceptance · Machine learning · Behavioral prediction · User engagement · XGBoost · Random forest · Digital financial services · Imbalanced classification  \n* Sayyed Khawar Abbas [sayyedkhawar.abbas@uni-corvinus.hu](sayyedkhawar.abbas@uni-corvinus.hu); [Sayyedkhawarabbas@gmail.com](Sayyedkhawarabbas@gmail.com)  \nMuzzammil Hussain  \n[Muzzammil.hussain@kfupm.edu.sa](Muzzammil.hussain@kfupm.edu.sa)  \nYagya Nath Rimal  \n[Rimal.yagya@gmail.com](Rimal.yagya@gmail.com)  \n1 Institute of Data Analytics and Information Systems, Department of Information Systems, Corvinus University of Budapest, 8 Fovam Ter, Budapest 1093, Hungary  \n2 Interdisciplinary Research Center for Finance and Digital Economy, KBS, KFUPM, Dhahran, Saudi Arabia  \n3 Faculty of Science and Technology, Pokhara University, Pokhara, Nepal  \nIntroduction  \nThe advancement of financial technology (FinTech) has significantly reshaped the global financial landscape by enhancing the accessibility, efficiency, and personalization of financial services. A notable innovation within this domain is the rise of robo-advisors, automated digital platforms that use algorithms and artificial intelligence to provide investment advice and portfolio management with minimal human supervision [1]. These systems have gained increasing attention for their potential to democratize financial planning and deliver scalable, cost-effective solutions to a broad base of users.  \nDespite their rapid proliferation, the acceptance of robo-advisors among users remains inconsistent [2], often influenced by complex behavioral, economic, and technological factors. Understanding what drives or hinders user acceptance is critical for both FinTech developers  \nSN Computer Science  \nand policymakers, particularly as digital financial services continue to expand into diverse markets and demographic segments. 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