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

Award ID: 2052165

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 applicants 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 applicants 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 applicants 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.

NSF Program Director: Peter Atherton