Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation

Charl Maree, C. Omlin
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引用次数: 5

Abstract

Micro-segmentation of customers in the finance sector is a nontrivial task and has been an atypical omission from recent scientific literature. Where traditional segmentation classifies customers based on coarse features such as demographics, micro-segmentation depicts more nuanced differences between individuals, bringing forth several advantages including the potential for improved personalization in financial services. AI and representation learning offer a unique opportunity to solve the problem of micro-segmentation. Although ubiquitous in many industries, the proliferation of AI in sensitive industries such as finance has become contingent on the explainability of deep models. We had previously solved the micro-segmentation problem by extracting temporal features from the state space of a recurrent neural network (RNN). However, due to the inherent opacity of RNNs, our solution lacked an explanation. In this study, we address this issue by extracting a symbolic explanation for our model and providing an interpretation of our temporal features. For the explanation, we use a linear regression model to reconstruct the features in the state space with high fidelity. We show that our linear regression coefficients have not only learned the rules used to recreate the features, but have also learned the relationships that were not directly evident in the raw data. Finally, we propose a novel method to interpret the dynamics of the state space by using the principles of inverse regression and dynamical systems to locate and label a set of attractors.
理解消费行为:循环神经网络的解释和诠释
金融行业客户的微观细分是一项重要任务,在最近的科学文献中,这是一个典型的遗漏。传统的细分是基于粗糙的特征(如人口统计数据)对客户进行分类,而微细分则描述了个人之间更细微的差异,带来了几个优势,包括改善金融服务个性化的潜力。人工智能和表示学习为解决微分割问题提供了一个独特的机会。尽管人工智能在许多行业中无处不在,但在金融等敏感行业,人工智能的扩散已经取决于深度模型的可解释性。我们以前通过从递归神经网络(RNN)的状态空间中提取时间特征来解决微分割问题。然而,由于rnn固有的不透明性,我们的解决方案缺乏解释。在本研究中,我们通过为我们的模型提取符号解释并提供我们的时间特征的解释来解决这个问题。为了解释这一点,我们使用线性回归模型高保真地重建状态空间中的特征。我们表明,我们的线性回归系数不仅学习了用于重建特征的规则,而且还学习了原始数据中不直接明显的关系。最后,我们提出了一种新的方法来解释状态空间的动力学,利用逆回归和动力系统的原理来定位和标记一组吸引子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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