Skip to main content

Table 2 Out-of-sample performance of credit and profit scoring models—Bondora

From: Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets

Model % invested loans Performance across invested loans Performance across all loans Total profit in mil. EUR
   Average return SD Average return SD  
Panel A: Credit scoring models       
 LR 59.8 19.57 33.06 11.70 27.30 0.53
 \(LR^{{\lambda_{\min } ,\alpha = 1}}\) 59.9 19.72 32.98 11.82 27.30 0.54
 \(LR^{{\lambda_{\min } ,\alpha = 0}}\) 59.3 19.69 32.96 11.69 27.17 0.53
 \(LR^{{\lambda_{\min } ,\alpha_{\min } }}\) 59.7 19.58 33.17 11.70 27.37 0.53
 RFC 75.8 19.49 38.48 14.78 34.53 0.69
 NNC 60.6 17.61 36.85 10.66 29.93 0.47
Panel B: Profit scoring models       
 LM 75.7 19.34 39.76 14.65 35.58 0.66
 \(LM^{{\lambda_{\min } ,\alpha = 1}}\) 76.3 19.66 39.54 15.00 35.53 0.70
 \(LM^{{\lambda_{\min } ,\alpha = 0}}\) 76.2 19.69 39.29 15.02 35.32 0.70
 \(LM^{{\lambda_{\min } ,\alpha_{\min } }}\) 76.2 19.67 39.55 15.00 35.53 0.70
 RFR 75.4 20.55 38.80 15.49 34.83 0.72
 NNR 71.8 20.35 38.82 14.62 34.15 0.68
  1. \(LR^{{\lambda_{\min } ,\alpha = 1}}\), \(LR^{{\lambda_{\min } ,\alpha = 0}}\), \(LR^{{\lambda_{\min } ,\alpha_{\min } }}\) are lasso, ridge and elastic net versions of logistic regression, \(LM^{{\lambda_{\min } ,\alpha = 1}}\), \(LM^{{\lambda_{\min } ,\alpha = 0}}\), \(LM^{{\lambda_{\min } ,\alpha_{\min } }}\) are lasso, ridge and elastic net versions of linear regression
  2. SD standard deviation, LR logistic regression-based models, LM linear regression-based models, RFC random forest classification, NNC neural network classification, RRR denotes random forest regression, NNR neural network regression
  3. Denotes a model that belongs to the set of superior models