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SBIR Phase I: Development of prediction-driven credit scoring and ruling platform for behavioral lending

SBIR Phase I: Development of prediction-driven credit scoring and ruling platform for behavioral lending
SBIR 第一阶段:开发预测驱动的行为贷款信用评分和裁决平台
批准号:
2052165
负责人:
Gilad Gavlovski
金额:
$25.59万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-08-01 至 2022-04-30

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中文摘要
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英文摘要
The broader impact of this Small Business Innovation Research (SBIR) Phase I project will enable credit to currently under-served populations. Creditworthiness is largely determined by the quality of the applicant’s credit and financial history. However, limited information regarding "credit-invisible" (one out of ten American adults) and "thin-file" applicants (62 million Americans) prevents accurate evaluation of a credit score. The proposed artificial intelligence (AI) based personality-driven lending platform will provide lenders a reliable creditworthiness prediction without an extensive financial history. Moreover, the proposed system will include a transparency module, preventing biases and expanding credit equity and identifying new banking customers from under-represented groups. This Small Business Innovation Research (SBIR) Phase I project aims to develop the first personality-driven credit scoring and ruling platform. While traditional scoring models base their assessment on financial history data, the proposed algorithm will produce a reliable credit score based on an applicant’s personality. No history of financial transactions (such as gas, water, electricity, TV, phone, broadband services, or rent payment records) is needed to reach a conclusion about an applicant’s creditworthiness. Research efforts of this Phase I project will focus on 1) the evaluation of alternative data points such as psychometric testing questionnaires, telecommunications data, demographics, firmographics, and publicly available data, 2) the development of a semi-supervised machine learning approach to predict credit reliability for individuals, and 3) the development of an unsupervised clustering approach to identify customer clusters associated with high credit reliability. Moreover, the prevention of bias in the underlying training data of machine learning approaches will be a priority. The primary objective of this project is to develop the predictive engine as the core element of the proposed personality-driven lending platform and develop a prototype.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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