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Customer churn describes terminating a relationship with a business orreducing customer engagement over a specific period. Customer acquisition costcan be five to six times that of customer retention, hence investing incustomers with churn risk is wise. Causal analysis of the churn model canpredict whether a customer will churn in the foreseeable future and identifyeffects and possible causes for churn. In general, this study presents aconceptual framework to discover the confounding features that correlate withindependent variables and are causally related to those dependent variablesthat impact churn. We combine different algorithms including the SMOTE,ensemble ANN, and Bayesian networks to address churn prediction problems on amassive and high-dimensional finance data that is usually generated infinancial institutions due to employing interval-based features used inCustomer Relationship Management systems. The effects of the curse and blessingof dimensionality assessed by utilising the Recursive Feature Eliminationmethod to overcome the high dimension feature space problem. Moreover, a causaldiscovery performed to find possible interpretation methods to describe causeprobabilities that lead to customer churn. Evaluation metrics on validationdata confirm the random forest and our ensemble ANN model, with %86 accuracy,outperformed other approaches. Causal analysis results confirm that someindependent causal variables representing the level of super guaranteecontribution, account growth, and account balance amount were identified asconfounding variables that cause customer churn with a high degree of belief.This article provides a real-world customer churn analysis from current statusinference to future directions in local superannuation funds.

Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions