{"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/improved-churn-causal-analysis-through","title":"Improved Churn Causal Analysis Through Restrained High‑Dimensional Feature Space Efects in Financial Institutions","arxiv_id":null,"date":"2022-07-27","proceeding":"Human-Centric Intelligent Systems 2022 7","authors":["David Hason Rudd","Huan Huo","Guandong Xu"],"abstract":"Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific \r\nperiod. Customer acquisition cost can be five to six times that of customer retention, hence investing in customers with \r\nchurn risk is wise. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future \r\nand identify effects and possible causes for churn. In general, this study presents a conceptual framework to discover the \r\nconfounding features that correlate with independent variables and are causally related to those dependent variables that \r\nimpact churn. We combine different algorithms including the SMOTE, ensemble ANN, and Bayesian networks to address \r\nchurn prediction problems on a massive and high-dimensional finance data that is usually generated in financial institutions \r\ndue to employing interval-based features used in Customer Relationship Management systems. The effects of the curse and \r\nblessing of dimensionality assessed by utilising the Recursive Feature Elimination method to overcome the high dimension \r\nfeature space problem. Moreover, a causal discovery performed to find possible interpretation methods to describe cause \r\nprobabilities that lead to customer churn. Evaluation metrics on validation data confirm the random forest and our ensemble \r\nANN model, with %86 accuracy, outperformed other approaches. Causal analysis results confirm that some independent \r\ncausal variables representing the level of super guarantee contribution, account growth, and account balance amount were \r\nidentified as confounding variables that cause customer churn with a high degree of belief. This article provides a real-world \r\ncustomer churn analysis from current status inference to future directions in local superannuation funds.\r\nKeywords Churn analysis · Bayesian networks · Deep neural networks · Data mining · Data sampling","url_abs":"https://link.springer.com/article/10.1007/s44230-022-00006-y","url_pdf":"https://link.springer.com/content/pdf/10.1007/s44230-022-00006-y.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":"improved-churn-causal-analysis-through","repo_url":"https://github.com/DavidHason/CausalAnalysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"causal-discovery","task_name":"Causal Discovery"},{"task_slug":"management","task_name":"Management"}],"methods":[{"method_slug":"smote","method_name":"SMOTE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}