The growing popularity of P2P lending has attracted more borrowers and lenders to the sector. With the growth in the popularity of P2P lending, there have been many studies focusing on analyzing credit risk in P2P lending. However, the credit risk is only a part of the story. The higher interest rates are allocated to the riskier loans, and the higher interest rates may or may not in fact compensate for the defaults expected. Therefore, the profit of a loan depends on both the interest rate and the default probability. Since investors are ultimately concerned with return on investment, models should help investors to predict the profit as accurately as possible. We develop a model that predicts the expected profit of a loan using survival analysis-based monthly default probability. Our approach extends previous profit scoring approaches, since it can be applied to any loan data set, including current data sets with many ongoing loans.
|Title of host publication||Intelligent Decision Technologies 2019|
|Publication status||Published - 2019|
|MoE publication type||A4 Article in a conference publication|
|Event||International Conference on Intelligent Decision Technologies - International Conference on Intelligent Decision Technologies|
Duration: 1 Jan 2019 → …
|Conference||International Conference on Intelligent Decision Technologies|
|Period||01/01/19 → …|