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

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