Ultimately these statistical decisions must be translated into profit consideration for the bank. Let us assume that a correct decision of the bank would result in 35% profit at the end of 5 years. A correct decision here means that the bank predicts an application to be good or credit-worthy and it actually turns out to be credit worthy. When the opposite is true, i.e. bank predicts the application to be good but it turns out to be bad credit, then the loss is 100%. If the bank predicts an application to be non-creditworthy, then loan facility is not extended to that applicant and bank does not incur any loss (opportunity loss is not considered here). The cost matrix, therefore, is as follows:
Out of 1000 applicants, 70% are creditworthy. A loan manager without any model would incur [0.7*0.35 + 0.3 (-1)] = - 0.055 or 0.055 unit loss. If the average loan amount is 3200 DM (approximately), then the total loss will be 1760000 DM and per applicant loss is 176 DM.
Logistic regression model performance:
Tree-based classification and random forest show a per unit profit; other methods are not doing well.