The Journal of Bigdata (한국빅데이터학회지)

Korea Bigdata Society (한국빅데이터학회)
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A Securities Company's Customer Churn Prediction Model and Causal Inference with SHAP Value

증권 금융 상품 거래 고객의 이탈 예측 및 원인 추론

  • Received : 2020.11.26
  • Accepted : 2020.12.31
  • Published : 2020.12.31
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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.

산업 분야를 막론하고 머신러닝의 관심이 매우 높아지고 있으나, 머신러닝이 지닌 설명 불가능성은 여전히 문제로 남아있어 적극적인 업무 적용에 어려움이 있다. 본고에서는 증권사 금융 고객을 대상으로 이탈예측 모델 개발 사례를 소개하고 SHAP Value 기법을 사용하여 설명 가능한 머신러닝 모델 개발 시도와 해석 가능성 도출에 대한 연구 결과를 소개한다. 총 6가지 고객이탈 모델을 비교 분석하였으며, SHAP Value와 고객의 자산 변화에 따른 유형 분류 및 데이터 분석을 통해 고객 이탈 원인을 추론한다. 본 연구 결과를 토대로, 향후 마케팅 담당자의 실제 고객 마케팅 수행에 있어 원인 추론이 가능한 이탈 예측 결괏값을 사용하고 고객별 마케팅 여부를 점검하는 등의 종합적 판단 지표로 활용할 수 있을 것으로 판단된다.

Keywords

References

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