Machine Learning with R - Third Edition

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 governments in many countries carefully monitor the fairness of lending practices, executives must be able to explain why one applicant was rejected for a loan while another was approved. This information is also useful for customers hoping to determine why their credit ...