“TrustScore” fintech machine-learning: growing the UK economy from enabling underserved communities to access mainstream financial services by building a credit-file through their established practice of saving clubs

–TrustScore – 金融科技机器学习：通过储蓄俱乐部的既定做法建立信用档案，使服务不足的社区获得主流金融服务，从而发展英国经济

• 批准号: 10052433
• 金额: $ 25.41万
• 依托单位: BLOOM GROUP HOLDING LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10052433
• 关键词: TrustScore; fintech; machine; learning; growing

Exclusion from financial-services blights the lives of millions in the UK. It damages the UK economy: creating financial-insecurity, stunting entrepreneurism, increasing unemployment, social security and healthcare costs.Lack of a credit-rating is a key barrier to financial-inclusion. Overcoming this problem would help users to access lower-cost, more affordable, loans and other financial services. TrustScore provides an opportunity to address this through recording users' trustworthiness, reflected in their participation in saving-circles.TrustScore's benefits are for all UK users of saving-circles.

被排除在金融服务业之外，影响了英国数百万人的生活。这将损害英国经济：造成金融不安全，阻碍金融发展，增加失业、社会保障和医疗成本。缺乏信用评级是金融包容性的主要障碍。克服这一问题将有助于用户获得成本更低、更负担得起的贷款和其他金融服务。TrustScore提供了一个机会，通过记录用户的可信度来解决这个问题，这反映在他们参与储蓄圈。TrustScore的好处是所有英国储蓄圈用户。