Example – identifying risky bank loans using C5.0 decision trees
The global financial crisis of 2007-2008 highlighted the importance of transparency and rigor in banking practices. As the availability of credit was limited, banks tightened their lending systems and turned to machine learning to more accurately identify risky loans.
Decision trees are widely used in the banking industry due to their high accuracy and ability to formulate a statistical model in plain language. Since government organizations in many countries carefully monitor lending practices, executives must be able to explain why one applicant was rejected for a loan while the others were approved. This information is also useful for customers hoping to determine why their credit rating is unsatisfactory.
It is likely that automated credit scoring models are employed to instantly approve credit applications on the telephone and web. In this section, we will develop a simple credit approval model using C5.0 decision trees...
