Financial inclusion ensures that individuals have access to financial products and services that meet their needs. As a key contributing factor to economic growth and investment opportunity, financial inclusion increases consumer spending and consequently business development. It has been shown that institutions are more profitable when they provide marginalised social groups access to financial services. Customer segmentation based on consumer transaction data is a well-known strategy used to promote financial inclusion. While the required data is available to modern institutions, the challenge remains that segment annotations are usually difficult and/or expensive to obtain. This prevents the usage of time series classification models for customer segmentation based on domain expert knowledge. As a result, clustering is an attractive alternative to partition customers into homogeneous groups based on the spending behaviour encoded within their transaction data. In this paper, we present a solution to one of the key challenges preventing modern financial institutions from providing financially inclusive credit, savings and insurance products: the inability to understand consumer financial behaviour, and hence risk, without the introduction of restrictive conventional credit scoring techniques. We present a novel time series clustering algorithm that allows institutions to understand the financial behaviour of their customers. This enables unique product offerings to be provided based on the needs of the customer, without reliance on restrictive credit practices.

翻译：金融普惠确保个人能够获得满足其需求的金融产品和服务。作为经济增长和投资机会的关键驱动因素，金融普惠提升了消费支出，进而促进商业发展。研究表明，当机构为边缘化社会群体提供金融服务时，其盈利能力会增强。基于消费者交易数据的客户细分是推动金融普惠的常见策略。尽管现代机构能够获取所需数据，但细分标注通常难以获取或成本高昂，这阻碍了基于领域专家知识的时间序列分类模型在客户细分中的应用。因此，聚类成为一种颇具吸引力的替代方案，可根据交易数据中蕴含的消费行为将客户划分为同质群体。本文针对现代金融机构在提供普惠性信贷、储蓄及保险产品时面临的核心挑战——即在不引入限制性传统信用评分技术的情况下，无法理解消费者金融行为及其风险——提出解决方案。我们提出一种新颖的时间序列聚类算法，使机构能够洞察客户金融行为，从而基于客户需求提供个性化产品方案，摆脱对限制性信用实践的依赖。