{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-branch-visits-and-credit-card-up","title":"Predicting Branch Visits and Credit Card Up-selling using Temporal Banking Data","arxiv_id":"1607.06123","date":"2016-07-20","proceeding":null,"authors":["Sandra Mitrović","Gaurav Singh"],"abstract":"There is an abundance of temporal and non-temporal data in banking (and other\nindustries), but such temporal activity data can not be used directly with\nclassical machine learning models. In this work, we perform extensive feature\nextraction from the temporal user activity data in an attempt to predict user\nvisits to different branches and credit card up-selling utilizing user\ninformation and the corresponding activity data, as part of \\emph{ECML/PKDD\nDiscovery Challenge 2016 on Bank Card Usage Analysis}. Our solution ranked\n\\nth{4} for \\emph{Task 1} and achieved an AUC of \\textbf{$0.7056$} for\n\\emph{Task 2} on public leaderboard.","url_abs":"http://arxiv.org/abs/1607.06123v2","url_pdf":"http://arxiv.org/pdf/1607.06123v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-branch-visits-and-credit-card-up","repo_url":"https://github.com/SandraMNE/ECMLChallenge2016","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"task-2","task_name":"Task 2"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}