The Effectiveness of Artificial Credit Scoring Models in Predicting NPLs using Micro Accounting Data

摘要

In this paper we study the effectiveness of artificial credit scoring models in predicting SMEs default. We use a unique accounting dataset of small business loans granted by one of four systemic Greek Banks during expansion period. Comparing a neural network model (multilayer perceptron) and a decision tree model with the credit scoring model applied by the bank, we find that the bank’s model had the relative worse performance in predicting loans default. Moreover the effectiveness of all models decreased significantly during the recession, indicating that the loan performance is no longer depended only on the quality of the borrowers but also on the economic conditions of the country.