MACHINE LEARNING GOES TO THE BANK

Abstract

This paper describes how the APT system has been applied to loan analysis to generalized and refine the knowledge previously used by an expert system, in order to increase the efficiency and the compactness of the decision rule base. The decision to lend money to industrial companies is a complex and risky activity for financial institutions. They need much expertise to deal with the large amount of information that has to be considered for this process, and the analysis must be carefully done in order to avoid misjudgments that would result in severe losses of unrecoverable credit. An expert system named SPAC had been developed to deal with this task without fulfilling the user's expectations. This paper presents the drawbacks of SPAC's approach and how APT, an integrated machine learning system, has been used to acquire and refine domain knowledge and general decision rules from basic descriptions of cases provided by SPAC. The learning methodology is detailed, and a complete example of a learning session with APT is given. The final results are then compared with those obtained with SPAC