2018/02/11 by J. Christopher Westland, Westland, J. Christopher, Tuan Q. Phan +4
Business, Management and Accounting · Computer Science · Economics, Econometrics and Finance · #Banking stability, regulation, efficiency #FOS: Computer and information sciences #FOS: Economics and business #FinTech, Crowdfunding, Digital Finance #General Finance (q-fin.GN) #Microfinance and Financial Inclusion #Private Equity and Venture Capital #Risk Management (q-fin.RM) #Social and Information Networks (cs.SI) #cs.SI #q-fin.GN #q-fin.RM
paper · pdf · doi:10.48550/arxiv.1802.10000
31 pages, 9 tables, 6 figures
arxiv created 2018/02/11 · openalex publication_date 2018/02/11 · arxiv updated 2018/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a fashion that can be used to predict loan profitability. The projection of these private networks onto networks of mobile phone communication and geographical locations from mobile phone GPS potentially give loan providers access to private information through graph and location metrics which we used to predict loan profitability. Graph topology was found to be an important predictor of loan profitability, explaining over 5.5% of variability. Networks of borrower location information explain an additional 19% of the profitability. Machine learning algorithms were applied to the data set previously analyzed to develop the predictive model and resulted in a 4% reduction in mean squared error.