Credit scoring: Does XGboost outperform logistic regression?A test on Italian SMEs.

The old-fashioned logistic regression is still the most used method for credit scoring. Recent developments have evolved new instruments coming from the machine learning approach, including random forests. In this paper, we tested the efficiency of logistic regression and XGBoost methods for default forecasting on a sample of 35,535 cases from 7 different business sectors of Italian SMEs, on a set of 28 banking variables and 55 balance sheet ratios for verifying which approach is better supporting the lending decisions. With this aim, we developed an efficiency index for measuring each model's capability to correctly select good borrowers, balancing the different effects of refusing the loan to a good customer and lending to a defaulter. Also, we computed the balancing spread to quantify the different models' efficiency in terms of credit costs for the borrower firms. Results show that different sectors report different results. However, generally speaking, the two methods report similar capabilities, while the cutoff setting can make a substantial difference in the actual use of those models for lending decisions. [Display omitted] • Logistic regression and XGBoost methods performances for default forecasting are compared: on a sample of 35,535 cases from 7 different business sectors of Italian SMEs. • The analysis is performed no a set of 28 banking variables and 55 balance sheet ratios for verifying which approach is mostly able to support the lending decisions. • The expected gross income of the lending activity is computed based on each model outcome. • An efficiency measure is proposed, obtained as the relative position of the considered estimator from 0 % of the always wrong model to 100 % of the perfect forecasting model. • Different cutoff values are considered for both models, evidencing the subsequent different results. [ABSTRACT FROM AUTHOR]