Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach

Andrew Estornell, Stylianos Loukas Vasileiou, William Yeoh, Daniel Borrajo, Rui Silva
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Abstract

In today's competitive financial landscape, understanding and anticipating customer goals is crucial for institutions to deliver a personalized and optimized user experience. This has given rise to the problem of accurately predicting customer goals and actions. Focusing on that problem, we use historical customer traces generated by a realistic simulator and present two simple models for predicting customer goals and future actions -- an LSTM model and an LSTM model enhanced with state-space graph embeddings. Our results demonstrate the effectiveness of these models when it comes to predicting customer goals and actions.
预测金融机构服务中的客户目标:数据驱动的 LSTM 方法
在当今竞争激烈的金融环境中,了解和预测客户目标对于机构提供个性化和优化的用户体验至关重要。这就产生了准确预测客户目标和行动的问题。针对这一问题,我们利用现实模拟器生成的历史客户痕迹,提出了预测客户目标和未来行动的两个简单模型--一个 LSTM 模型和一个用状态空间图嵌入增强的 LSTM 模型。我们的结果证明了这些模型在预测客户目标和行动方面的有效性。
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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