Accuracy of machine learning models versus "hand crafted" expert systems – A credit scoring case study

Abstract

Relatively few publications compare machine learning models with expert systems when applied to the same problem domain. Most publications emphasize those cases where the former beat the latter. Is it a realistic picture of the state of the art?

Some other findings are presented here. The accuracy of a real world “mind crafted” credit scoring expert system is compared with dozens of machine learning models. The results show that while some machine learning models can surpass the expert system’s accuracy with statistical significance, most models do not. More interestingly, this happened only when the problem was treated as regression. In contrast, no machine learning model showed any statistically significant advantage over the expert system’s accuracy when the same problem was treated as classification. Since the true nature of the class data was ordinal, the latter is the more appropriate setting. It is also shown that the answer to the question is highly dependent on the meter that is being used to define accuracy.