The last step in building a scorecard in practice is model approval. The model must be approved by the financial institution; however, this step cannot be applied to this research simply because it depends on the business rules of financial institutions. Small but limited adjustments are usually made to the scorecard in response to the business rules and two such examples are:
1. Remove predictor variables which cannot be used because of business standards.
3.8
Discussion
The complete process of scorecard development has been explained primarily based on infor- mation from credit risk practitioners but also from the credit risk literature. It was interesting to discover that the current literature on the entire process of the development of credit scoring models is quite limited since most related studies focus mainly on the methods for developing scorecards. It should also be noted that credit scoring models are usually static in nature in that they are built from a couple of point-in-time snapshots. There are therefore various reasons for a scorecard to deviate from its expected performance, i.e. performance at the time it was developed. For example, a typical scorecard might lose its predictive ability during a recession if the predictor variables are sensitive to the economic cycle. This is the reason why scorecards need to be updated frequently, usually every 18-24 months.
It is also worth mentioning that when a scorecard is ready for use, a cut-off value needs to be specified. This value determines whether or not the score obtained for a particular applicant warrants the approval of the credit application, thereby classifying applicants into either ‘good’ or ‘bad’. The cut-off value may be varied depending on the level of risk that financial institu- tions are willing to take. For instance, if they want to be more risky, the cut-off value will be lowered so that more applicants with lower scores can be granted credit.
Financial institutions need to have a strong credit policy. Indeed, Mueller (1994, p. 29) asserts:
‘For a bank, credit policy is like an anchor from which a boat swings with wind and
tide. While the boat’s position shifts with changing conditions, the anchor keeps it from drifting. So too, can a strong credit policy keep a bank tied to the bedrock of basic credit standards.’
Nowadays, most banks are abiding by the new Basel II Accord (Basel Committee on Banking Supervision 2006), which requires them to have robust policies and regulations for validating the accuracy and consistency of rating systems and processes.
3.9
Summary
The aim of this chapter was to explain how a scorecard can be created. A valid and well- performing scorecard was developed from a large set of real-world raw data. All the processes from data cleaning and discretisation to the development and validation of the scorecard were
explained in detail. The motivation behind this chapter stems from the fact that the literature describing these processes is fairly limited, which is probably because of the scarcity of real- world data in that area.
The processes explained in this study are very similar to those usually performed in practice. Two minor points worth mentioning are: 1) reject inference was not included in the model de- velopment because the data was not available, and 2) model approval could not be applied in the scorecard development process of this research study since it depends on the business rules specific to financial institutions.
By developing a practical scorecard, a better insight into credit scoring models was gained. Im- portant ingredients in making accurate and realistic scorecards are accurate predictor variables of individual risk and a systematic methodology to generate them (Galindo & Tamayo 2000). When implemented effectively, scorecards should be able to rank the entire population of ap- plicants by risk (Leonard 1995).
While logistic regression was used as the main classification technique for developing the score- card, there are many other classifiers that can potentially be used. One such classifier is based on the concept of an artificial immune system. Since this classifier has had successful applications in many different fields, it will be investigated in the next chapter as a potential classification technique for credit scoring.
BUILDING AN ARTIFICIAL IMMUNE
SYSTEM CLASSIFIER
†
4.1
Introduction
Classification is an important business decision making task. It involves assigning an object to a predefined group or class based on a number of observed attributes related to that object. While there have been many studies on statistical classifiers such as discriminant analysis and logistic regression (Hand & Henley 1997, Srinivasan & Kim 1987), researchers are now focusing more on intelligent systems techniques, such as genetic algorithms and artificial neural networks, as classifier systems (Malhotra & Malhotra 2003, Desai et al. 1997).
In the previous chapter, logistic regression, which is the most commonly used classification technique in credit scoring, was used for developing a practical scorecard. However, in this chapter, we investigate a new machine learning technique based on the concepts of AIS as a classification technique in scorecard development. AIS has been used in a wide array of appli- cations (de Castro & Timmis 2002), including classification (Watkins 2001). However, based on our review of current AIS classifier systems, two fundamental problems were found.
The first problem is related to the population control mechanism. It has been found that the number of B-cells1, which match some antigens, increases through cloning and mutation to
†Part of the work presented in this chapter has been previously published (Leung, Cheong & Cheong 2007b,
Leung & Cheong 2006).
such an amount that it usually overtakes the entire population of B-cells (Nasraoui et al. 2002). The second problem is concerned with the way AIS classifiers are optimised. Most AIS clas- sifier systems use populations of B-cell pools and the problem identified is that optimising one B-cell (which is only a part of the classifier) at a time does not necessarily guarantee that the B-cell pool (which is the complete classifier) will be optimised.
This chapter introduces a new AIS algorithm and classifier system that addresses the two afore- mentioned problems without sacrificing the classification performance of the system. The new AIS classifier is named Simple Artificial Immune System (SAIS). An explanation of its algo- rithm and implementation as well as its differences from current AIS classifiers is provided. Our classifier is evaluated by testing it on six publicly available benchmark datasets obtained from the machine learning repository of the University of California Irvine (Blake & Merz 1998). The performance of SAIS is compared with that of other classifiers which used the same six datasets for performance evaluation. Given that only the ‘percent correctly classified’ perfor- mance measure of the other classifiers is available from the current literature, the performance of SAIS is quantified in terms of the ‘percent correctly classified’ measure for comparison pur- poses.