基于消费个性的循环神经网络聚类细分
Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality
摘要：
长期以来，客户细分一直是银行业的一个富有成效的领域。然而，随着对传统问题的新方法的出现，也带来了新的机会。细粒度的客户细分是出了名的难以捉摸，获得它们的一种方法是通过特征提取。可以将标准人格特质的系数分配给随时间聚集的金融交易类别。然而，我们已经发现，所形成的聚类对于微分割来说并不具有足够的区分性。在一种新的方法中，我们从神经网络的隐藏状态中提取具有连续值的时间特征，从客户的金融交易中预测客户的消费个性。我们分别使用长短期记忆（LSTM）和前馈神经网络来考虑时间模型和非序列模型。我们发现，循环神经网络产生微段，而前馈网络只产生粗段。最后，我们表明，使用这些提取的功能进行分类至少以及定制模型的两个常见的指标，即贷款违约率和客户流动性指数。
英文摘要：
Customer segmentation has long been a productive field in banking. However, with new approaches to traditional problems come new opportunities. Fine-grained customer segments are notoriously elusive and one method of obtaining them is through feature extraction. It is possible to assign coefficients of standard personality traits to financial transaction classes aggregated over time. However, we have found that the clusters formed are not sufficiently discriminatory for micro-segmentation. In a novel approach, we extract temporal features with continuous values from the hidden states of neural networks predicting customers' spending personality from their financial transactions. We consider both temporal and non-sequential models, using long short-term memory (LSTM) and feed-forward neural networks, respectively. We found that recurrent neural networks produce micro-segments where feed-forward networks produce only coarse segments. Finally, we show that classification using these extracted features performs at least as well as bespoke models on two common metrics, namely loan default rate and customer liquidity index.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
