2025/10/08 by Aoxue Wang, Di Wang, Albert, Ye Du +1
Business, Management and Accounting · #Artificial Intelligence (cs.AI) #Computational Engineering #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #FinTech, Crowdfunding, Digital Finance #Finance #Financial Distress and Bankruptcy Prediction #Mathematical Finance (q-fin.MF) #Portfolio Management (q-fin.PM) #Private Equity and Venture Capital #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2510.07444
openalex publication_date 2025/10/08 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28
Risk management is a prominent issue in peer-to-peer lending. An investor may naturally reduce his risk exposure by diversifying instead of putting all his money on one loan. In that case, an investor may want to minimize the Value-at-Risk (VaR) or Conditional Value-at-Risk (CVaR) of his loan portfolio. We propose a low degree of freedom deep neural network model, DeNN, as well as a high degree of freedom model, DSNN, to tackle the problem. In particular, our models predict not only the default probability of a loan but also the time when it will default. The experiments demonstrate that both models can significantly reduce the portfolio VaRs at different confidence levels, compared to benchmarks. More interestingly, the low degree of freedom model, DeNN, outperforms DSNN in most scenarios.