Abstract
Convolution neural networks (CNNs) have proven effectiveness, but they are not applicable to all datasets, such as those with heterogeneous attributes, which are often used in the finance and banking industries. Such datasets are difficult to classify, and to date, existing high-accuracy classifiers and rule-extraction methods have not been able to achieve sufficiently high classification accuracies or concise classification rules. This study aims to provide a new approach for achieving transparency and conciseness in credit scoring datasets with heterogeneous attributes by using a one-dimensional (1D) fully-connected layer first CNN combined with the Recursive-Rule Extraction (Re-RX) algorithm with a J48graft decision tree (hereafter 1D FCLF-CNN). Based on a comparison between the proposed 1D FCLF-CNN and existing rule extraction methods, our architecture enabled the extraction of the most concise rules (6.2) and achieved the best accuracy (73.10%), i.e., the highest interpretability–priority rule extraction. These results suggest that the 1D FCLF-CNN with Re-RX with J48graft is very effective for extracting highly concise rules for heterogeneous credit scoring datasets. Although it does not completely overcome the accuracy–interpretability dilemma for deep learning, it does appear to resolve this issue for credit scoring datasets with heterogeneous attributes, and thus, could lead to a new era in the financial industry.
Highlights
A comparison of the test dataset accuracy (TS ACC) and the average number of extracted rules for the German dataset is shown in Tables 4 and 5, respectively
A comparison of the TS ACC and the average number of extracted rules for the Australian dataset is shown in Tables 6 and 7, respectively
Even if Deep Learning (DL)-inspired techniques are effective in improving the classification accuracy, these methods could not be expected to transform the “black box” nature of deep neural networks (DNNs) trained using DL into a “white box” nature consisting of a series of interpretable classification rules
Summary
The banking industry faces numerous types of risk that affect banks and customers. A key element of risk management in the banking industry is the need for appropriate customer selection. Credit scoring is an effective approach used by banks to analyze money borrowing and lending [1]. Banks need to collect information from customers and other financial institutions to be able to make sound decisions in terms of whether to lend money to clients; to this end, collecting financial information can help differentiate safe from risky borrowers. The extraordinary increases in computing speed coupled with considerable theoretical advances in machine learning algorithms have created a renaissance in high modeling capabilities, with credit scoring being one of numerous examples. With advanced modeling capabilities, researchers have achieved very high performances in making financial risk predictions [2]
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