{"id":{"repo_id":"the-open-u","oai_identifier":"oai:oro.open.ac.uk:54554"},"canonical_url":"https://search.dev.ndltd.org/etd/the-open-u/oai:oro.open.ac.uk:54554","repository":{"repo_id":"the-open-u","name":"The Open University","base_url":"https://oro.open.ac.uk/cgi/oai2"},"display":{"title":"Tackling change and uncertainty in credit scoring","abstract":"Credit scoring methods summanse information on credit applicants. An assessment of creditworthiness is derived from this summary. This thesis is concerned with statistical methods of credit scoring. Much of the existing literature on credit scoring is concerned with comparing the predictive power of a wide variety of classification techniques. However, much of the published work concludes that classifier performance on credit data is relatively insensitive to the choice of statistical technique. Consequently, the techniques used in commercial credit scoring have remained broadly similar during recent years. This thesis investigates credit scoring from a more fundamental level, by considering the formulation of the credit problem. A review of the credit literature is given, focusing on areas that have been subjected to much recent research activity. Details of the data sets used throughout this thesis are provided and analysed using techniques common to the credit industry. Methods that capitalise on the uncertainty and flexibility in the definitions of the classes used to represent 'good' and 'bad' credit risks are proposed. Firstly, a class of models is described that permits the choice of class definition to be deferred until the time at which the classification is required. Secondly, a strategy for choosing a suitable definition which optimises some external criterion is introduced. In addition, an approach is presented that combats classifier deterioration resulting from the evolution of the underlying populations. This thesis is essentially concerned with the uncertainties and change inherent in credit scoring. We present novel ways in which these properties may be incorporated in the formulation of the credit problem.","abstract_html":"Credit scoring methods summanse information on credit applicants. An assessment of creditworthiness is derived from this summary. This thesis is concerned with statistical methods of credit scoring. Much of the existing literature on credit scoring is concerned with comparing the predictive power of a wide variety of classification techniques. However, much of the published work concludes that classifier performance on credit data is relatively insensitive to the choice of statistical technique. Consequently, the techniques used in commercial credit scoring have remained broadly similar during recent years. This thesis investigates credit scoring from a more fundamental level, by considering the formulation of the credit problem. A review of the credit literature is given, focusing on areas that have been subjected to much recent research activity. Details of the data sets used throughout this thesis are provided and analysed using techniques common to the credit industry. Methods that capitalise on the uncertainty and flexibility in the definitions of the classes used to represent 'good' and 'bad' credit risks are proposed. Firstly, a class of models is described that permits the choice of class definition to be deferred until the time at which the classification is required. Secondly, a strategy for choosing a suitable definition which optimises some external criterion is introduced. In addition, an approach is presented that combats classifier deterioration resulting from the evolution of the underlying populations. This thesis is essentially concerned with the uncertainties and change inherent in credit scoring. 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An assessment of creditworthiness is derived from this summary. This thesis is concerned with statistical methods of credit scoring. Much of the existing literature on credit scoring is concerned with comparing the predictive power of a wide variety of classification techniques. However, much of the published work concludes that classifier performance on credit data is relatively insensitive to the choice of statistical technique. Consequently, the techniques used in commercial credit scoring have remained broadly similar during recent years. This thesis investigates credit scoring from a more fundamental level, by considering the formulation of the credit problem. A review of the credit literature is given, focusing on areas that have been subjected to much recent research activity. Details of the data sets used throughout this thesis are provided and analysed using techniques common to the credit industry. Methods that capitalise on the uncertainty and flexibility in the definitions of the classes used to represent 'good' and 'bad' credit risks are proposed. Firstly, a class of models is described that permits the choice of class definition to be deferred until the time at which the classification is required. Secondly, a strategy for choosing a suitable definition which optimises some external criterion is introduced. In addition, an approach is presented that combats classifier deterioration resulting from the evolution of the underlying populations. This thesis is essentially concerned with the uncertainties and change inherent in credit scoring. We present novel ways in which these properties may be incorporated in the formulation of the credit problem."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Tackling change and uncertainty in credit scoring"]}]}],"canonical_facts":{"dc:creator":["Kelly, Mark Gerard"],"dc:date":["1998"],"dc:date.issued":["1998"],"dc:description.abstract":["Credit scoring methods summanse information on credit applicants. An assessment of creditworthiness is derived from this summary. This thesis is concerned with statistical methods of credit scoring. Much of the existing literature on credit scoring is concerned with comparing the predictive power of a wide variety of classification techniques. However, much of the published work concludes that classifier performance on credit data is relatively insensitive to the choice of statistical technique. Consequently, the techniques used in commercial credit scoring have remained broadly similar during recent years. This thesis investigates credit scoring from a more fundamental level, by considering the formulation of the credit problem. A review of the credit literature is given, focusing on areas that have been subjected to much recent research activity. Details of the data sets used throughout this thesis are provided and analysed using techniques common to the credit industry. Methods that capitalise on the uncertainty and flexibility in the definitions of the classes used to represent 'good' and 'bad' credit risks are proposed. Firstly, a class of models is described that permits the choice of class definition to be deferred until the time at which the classification is required. Secondly, a strategy for choosing a suitable definition which optimises some external criterion is introduced. 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