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http://dx.doi.org/10.36498/kbigdt.2020.5.2.215

A Securities Company's Customer Churn Prediction Model and Causal Inference with SHAP Value  

Na, Kwangtek (데이터애널리틱스랩)
Lee, Jinyoung (데이터애널리틱스랩)
Kim, Eunchan (데이터애널리틱스랩)
Lee, Hyochan (데이터애널리틱스랩)
Publication Information
The Journal of Bigdata / v.5, no.2, 2020 , pp. 215-229 More about this Journal
Abstract
The interest in machine learning is growing in all industries, but it is difficult to apply it to real-world tasks because of inexplicability. This paper introduces a case of developing a financial customer churn prediction model for a securities company, and introduces the research results on an attempt to develop a machine learning model that can be explained using the SHAP Value methodology and derivation of interpretability. In this study, a total of six customer churn models are compared and analyzed, and the cause of customer churn is inferred through the classification and data analysis of SHAP Value and the type of customer asset change. Based on the results of this study, it would be possible to use it as a basis for comprehensive judgment, such as using the Value of the deviation prediction result that can infer the cause of the marketing manager's actual customer marketing in the future and establishing a target marketing strategy for each customer.
Keywords
Churn Prediction Model; Machine Learning; XAI; Causal Inference; SHAP Value;
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Times Cited By KSCI : 4  (Citation Analysis)
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