[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128735-en":3,"doc-seo-128735-105":31,"detail-sidebar-cat-0-en-105":92},{"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},128735,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Applying Machine Learning to Analyze ATM Cash Withdrawal Patterns and Develop Optimization Model - Insights from Credit and Debit Card Transactions in India","Data analysis through machine learning is increasingly critical for banks seeking resource optimization and cost reduction. With ATMs providing cost-effective access for both institutions and customers, this study groups Indian scheduled commercial banks into clusters based on ATM cash withdrawals using credit and debit cards, examines withdrawal distributions within clusters, and analyzes how the number of ATMs relates to withdrawal volume. The findings support strategy formulation for efficient resource allocation and improved banking accessibility.","Applying Machine Learning to Analyze ATM Cash Withdrawal Patterns and Develop Optimization Model: Insights from Credit and Debit Card Transactions in India  \nSEEJPH Volume XXVI, S1, 2025, ISSN: 2197-5248; Posted:05-01-2025  \nApplying Machine Learning to Analyze ATM Cash Withdrawal Patterns and Develop Optimization Model: Insights from Credit and Debit Card Transactions in India  \nDr Riddhi Dave1, Dr. Nili Shah2, Rani Niral Shah3, Dipti Gadhavi4  \n1Professor, Som-Lalit Institute of Management Studies, Ahmedabad  \nMail ID: [riddhidave@somlalit.org](riddhidave@somlalit.org)  \n[ORCID iD: 0009-0002-6456-0288](ORCID iD: 0009-0002-6456-0288)  \n2Assistant Professor, Som-Lalit Institute of Management Studies, Ahmedabad  \nMail ID: [nilishah@somlalit.org](nilishah@somlalit.org)  \n3Assistant Professor, Som-Lalit Institute of Management Studies, Ahmedabad  \nMail ID: [ranishah@somlalit.org](ranishah@somlalit.org)  \n4Assistant Professor, Som-Lalit Institute of Management Studies, Ahmedabad [Mail ID: dpgadhavi81@gmail.com](Mail ID: dpgadhavi81@gmail.com)  \nKEYWORDS  \nMachine learning, ATM, Credit card, Debit card, Clustering  \nABSTRACT  \nData analysis through Machine learning is becoming very pivotal and valuable for organizations. Banks are considered to be the backbone of our Economy. Banks in India are striving for resource optimization and cost cutting and use of ATMs has proved to be very cost effective strategy deployed by banks in India which benefits not only the banks but also customers. Indian scheduled commercial banks are reckoned globally for their best practices. The study has been undertaken with the objective of : Grouping the banks into clusters according to how their ATMs are used to withdraw cash using credit cards and debit cards; understanding the distribution of ATM cash withdrawals through credit cards and debit cards within each clusters and analyzing the relationship between the number of ATMs and ATM cash withdrawals made through credit and debit cards within each cluster, and to gain insights into how this relationship varies within and between clusters. In a nutshell, the research paper aims at analyzing the pattern of ATMs cash withdrawals of scheduled commercial banks in India done through credit card and debit cards through various machine learning techniques. The Viksit Bharat Mission envisions a developed India by fostering financial inclusivity, economic growth, and technological advancement. Optimizing the use of ATM machines in India can significantly contribute to these goals by ensuring efficient resource allocation and enhanced accessibility to banking services. The outcome of this research are expected to enable banks in framing strategies for optimizing their resources.  \nApplying Machine Learning to Analyze ATM Cash Withdrawal Patterns and Develop Optimization Model: Insights from Credit and Debit Card Transactions in India  \nSEEJPH Volume XXVI, S1, 2025, ISSN: 2197-5248; Posted:05-01-2025  \nIntroduction:  \nIndia's banks are using ATMs (Automated Teller Machines) more and more to streamline their business processes and improve client support.  \nBecause ATMs are so widely used, banks are able to cut the operational expenses that come with having physical locations. Banks can cut expenses by minimizing the need for physical branches by encouraging clients to utilise ATMs for tasks like cash withdrawals, balance inquiries, and fund transfers. ATM usage enables banks to optimize their operations, reduce costs, enhance customer service, and drive strategic growth. By encouraging customers to use ATMs for their banking needs, banks can achieve operational efficiency and deliver a seamless banking experience to their customers.  \nWhen using an ATM, credit and debit cards are both frequently utilised to provide consumers with rapid access to cash and other financial services. When consumers use their credit cards rather than debit cards to make ATM withdrawals, banks stand to gain more. Banks receive payment ","cbCainGvsvdvTjLP","https://ap.wps.com/l/cbCainGvsvdvTjLP","pdf",604275,3,1,13,"English","en",105,"# Introduction\n## Purpose of using ATMs in banking operations\n## Credit vs debit card ATM withdrawal economics\n## Optimization via linear programming and machine learning\n# Review of Literature\n## Background on ATM functionality and service quality","[{\"question\":\"What is the main objective of the study on Indian ATMs?\",\"answer\":\"To group scheduled commercial banks into clusters based on how ATMs are used for cash withdrawals via credit and debit cards, then analyze withdrawal distributions and the relationship between ATM counts and withdrawal volume across clusters.\"},{\"question\":\"How does the study compare credit-card versus debit-card ATM withdrawals for banks?\",\"answer\":\"It explains that credit-card withdrawals can generate transaction fees and interest charges, while debit-card withdrawals directly reduce customer accounts and typically provide lower revenue since banks do not earn interest on these withdrawals.\"},{\"question\":\"What optimization approach does the paper propose to improve ATM utilization?\",\"answer\":\"It discusses using advanced optimization techniques, including linear programming and machine learning, to identify underutilized ATMs and reallocate resources toward higher-demand areas.\"}]","Applying Machine Learning to Analyze ATM Cash Withdrawal Patterns and Develop Optimization Model - 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