Most lending institutions are aware of the importance of having a well-performing credit scoring model or scorecard and know that, in order to remain competitive in the credit industry, it is necessary to continuously improve their scorecards. This is because better scorecards result in substantial monetary savings that can be stated in terms of millions of dollars. Thus, there has been increasing interest in the application of new classifiers in credit scoring from both practitioners and researchers in the last few decades. Most of the recent work in this field has focused on the use of new and innovative techniques to classify applicants as either 'credit-worthy' or 'non-credit-worthy', with the aim of improving scorecard performance. In this thesis, we investigate the suitability of intelligent systems techniques for credit scoring. In particular, intelligent systems that use immunological metaphors are examined and used to build a learning and evolutionary classification algorithm. Our model, named Simple Artificial Immune System (SAIS), is based on the concepts of the natural immune system. The model uses applicants' credit details to classify them as either 'credit-worthy' or 'non-credit-worthy'. As part of the model development, we also investigate several techniques for selecting variables from the applicants' credit details. Variable selection is important as choosing the best set of variables can have a significant effect on the performance of scorecards. Interestingly, our results demonstrate that the traditional stepwise regression variable selection technique seems to perform better than many of the more recent techniques. A further contribution offered by this thesis is a detailed description of the scorecard development process. A detailed explanation of this process is not readily available in the literature and our description of the process is based on our own experiences and discussions with industry credit risk practitioners. We evaluate our model using both publicly available datasets as well as a very large set of real-world consumer credit scoring data obtained from a leading Australian bank. The evaluation results reveal that SAIS is a competitive classifier and is appropriate for developing scorecards which require a class decision as an outcome. Another conclusion reached is one confirmed by the existing literature, that even though more sophisticated scorecard development techniques, including SAIS, perform well compared to the traditional statistical methods, their performances are not statistically significantly different from the statistical methods. As with other intelligent systems techniques, SAIS is not explicitly designed to develop practical scorecards which require the generation of a score that represents the degree of confidence that an applicant will belong to a particular group. However, it is comparable to other intelligent systems techniques which are outperformed by statistical techniques for generating p ractical scorecards. Our final remark on this research is that even though SAIS does not seem to be quite suitable for developing practical scorecards, we still believe that there is room for improvement and that the natural immune system of the body has a number of avenues yet to be explored which could assist with the development of practical scorecards.
| Identifer | oai:union.ndltd.org:ADTP/246468 |
| Date | January 2009 |
| Creators | Leung Kan Hing, Kevin, kleung19@yahoo.com |
| Publisher | RMIT University. Business Information Technology |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://www.rmit.edu.au/help/disclaimer, Copyright Kevin Leung Kan Hing |
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