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

Financial Innovation

Table 5 Loan default classification comparison of credit and profit scoring models—lending club

Model	Accuracy (%)	Specificity (%)	Sensitivity (%)
Panel A: Credit scoring models
LR	67.5	67.2	67.6
\(LR^{{\lambda_{\min } ,\alpha = 1}}\)	67.5	66.7	67.7
\(LR^{{\lambda_{\min } ,\alpha = 0}}\)	67.9	66.7	68.1
\(LR^{{\lambda_{\min } ,\alpha_{\min } }}\)	67.6	66.4	67.9
RFC	75.7	42.6	83.7
NNC	72.1	55.1	76.2
Panel B: Profit scoring models
LM	69.1	51.3	73.4
\(LM^{{\lambda_{\min } ,\alpha = 1}}\)	72.4	49.7	77.9
\(LM^{{\lambda_{\min } ,\alpha = 0}}\)	71.4	47.9	77.0
\(LM^{{\lambda_{\min } ,\alpha_{\min } }}\)	72.6	49.5	78.1
RFR	72.0	46.4	78.1
NNR	73.8	33.8	83.5

\(LR^{{\lambda_{\min } ,\alpha = 1}}\), \(LR^{{\lambda_{\min } ,\alpha = 0}}\), \(LR^{{\lambda_{\min } ,\alpha_{\min } }}\) are lasso, ridge and elastic net version of logistic regression, \(LM^{{\lambda_{\min } ,\alpha = 1}}\), \(LM^{{\lambda_{\min } ,\alpha = 0}}\), \(LM^{{\lambda_{\min } ,\alpha_{\min } }}\) are lasso, ridge and elastic net version of linear regression
RFC random forest classification, NNC neural network classification, RRR random forest regression, NNR neural network regression.