Application of Causal Inference to Analytical Customer Relationship Management in Banking and Insurance
因果推断在银行与保险分析型客户关系管理中的应用AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确将因果推断、干预和反事实解释用于客户关系管理；银行保险场景本身不构成资产定价、收益预测或金融风险计量依据。
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因果推断在银行与保险分析型客户关系管理中的应用
- 研究问题
- 研究银行与保险的客户关系管理模型如何提供因果可理解的决策解释，以及改变输入特征后能否生成面向客户的反事实说明。
- 方法与思路
- 作者研究改变数据特征分布对目标特征的影响，并在贷款违约、保险欺诈、信用卡欺诈和客户流失数据上生成反事实解释。
- 材料陈述的贡献
- 摘要称，除信用卡客户流失数据外，其余任务生成了作者认为质量较好的反事实，所涉及的特征变化不超过三个。
- 阅读关注点
- 应核对特征改变是否对应可识别干预、反事实的可行性与质量标准，以及模型输出变化和真实结果变化之间的关系。
- 阅读边界
- 这是预印本，同行评审或期刊接收未核实。摘要未说明因果结构、识别条件或质量指标，预测模型的反事实解释不能直接保证真实干预效果。
原文摘要
Of late, in order to have better acceptability among various domain, researchers have argued that machine intelligence algorithms must be able to provide explanations that humans can understand causally. This aspect, also known as causability, achieves a specific level of human-level explainability. A specific class of algorithms known as counterfactuals may be able to provide causability. In statistics, causality has been studied and applied for many years, but not in great detail in artificial intelligence (AI). In a first-of-its-kind study, we employed the principles of causal inference to provide explainability for solving the analytical customer relationship management (ACRM) problems. In the context of banking and insurance, current research on interpretability tries to address causality-related questions like why did this model make such decisions, and was the model's choice influenced by a particular factor? We propose a solution in the form of an intervention, wherein the effect of changing the distribution of features of ACRM datasets is studied on the target feature. Subsequently, a set of counterfactuals is also obtained that may be furnished to any customer who demands an explanation of the decision taken by the bank/insurance company. Except for the credit card churn prediction dataset, good quality counterfactuals were generated for the loan default, insurance fraud detection, and credit card fraud detection datasets, where changes in no more than three features are observed.