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Renter-focused Open Banking Risk-profiling Model Using Big Data and Machine Learning, improving Credit Scores and a 10–20% Reduction in the Poverty Premium

以租户为中心的%20开放%20银行%20风险分析%20模型%20使用%20大%20数据%20和%20机器%20学习，%20改善%20信贷%20分数%20和%20a%2010-20%%20减少%20in%20the%20贫困

基本信息

批准号：
10004498
负责人：
金额：
$ 41.57万
依托单位：
INSURESTREET LIMITED
依托单位国家：
英国
项目类别：
Collaborative R&D
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=10004498
关键词：
Renter focused Open Banking Risk

项目摘要

InsureStreet Limited (Canopy), with a core team of Tahir Farooqui, Matt Hobbs, Jon Holloway and Lee Power, is a UK fintech SME revolutionising risk assessment modelling for the rental industry. Through the expansion of an open banking-based risk-profiling model, Canopy will allow renters, letting agents and financial institutions to fairly and accurately predict affordability and risk levels, simplifying the process and increasing financial stability for those most vulnerable.During months 1--3, Canopy's proprietary data-driven risk rating model powered by open banking data will be built. At month 3, the beta product will be launched and immediately start ensuring that there are increased tenancies for renters impacted by COVID-19\. This will ultimately reduce homelessness and welfare dependency. During months 3--9, the beta product will be iterated on during the remainder of the project. At month 6, Renters will achieve greater visibility to the financial services sector and as such, improved access to financial products that fit into stretched budgets. This will lead to lower rent arrears in the private sector and less pressure on social housing supply. By month 12 and 18, the project will have delivered its proposition. The impact will be improved accessibility to housing and a reduction to the poverty premium.

InsureStreet Limited（Canopy）是一家英国金融科技中小企业，其核心团队包括Tahir Farooqui、Matt Hobbs、Jon霍洛威和Lee Power，为租赁行业的风险评估建模带来了革命性的变化。通过扩展基于开放银行的风险分析模型，Canopy将允许租房者，让代理商和金融机构公平准确地预测负担能力和风险水平，简化流程并提高最脆弱人群的金融稳定性。在1- 3个月期间，Canopy将建立由开放银行数据驱动的专有数据驱动风险评级模型。在第三个月，测试版产品将推出，并立即开始确保受COVID-19影响的租户的租赁增加。这将最终减少无家可归和对福利的依赖。在3- 9个月期间，测试版产品将在项目的剩余部分迭代。在第6个月，租房者将获得更大的金融服务部门的可见度，因此，更好地获得适合紧张预算的金融产品。这将降低私营部门的拖欠租金，减轻社会住房供应的压力。到12月和18月，该项目将提交其提案。其影响将是改善获得住房的机会和减少贫困补贴。