Using a Multilayer Perceptron to Assess Credit Risk

A loan officer at a bank needs to be able to identify characteristics that are indicative of people who are likely to default on loans and use those characteristics to identify good and bad credit risks.

Suppose that information on 850 past and prospective customers is contained in bankloan.sav. See the topic Sample Files for more information. The first 700 cases are customers who were previously given loans. Use a random sample of these 700 customers to create a multilayer perceptron, setting the remaining customers aside to validate the analysis. Then use the model to classify the 150 prospective customers as good or bad credit risks.

Additionally, the loan officer has previously analyzed the data using logistic regression (in the Regression option) and wonders how the multilayer perceptron compares as a classification tool.

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