Consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

Operations research in consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking both because of the amount of money being lent and the impact of such credit on the global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring,-the way of assessing risk in consumer finance- and what is meant by a credit score. It then outlines ten challenges for Operational Research to support modelling in consumer finance. Some of these are to developing more robust risk assessment systems while others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer financ

A literature review on the application of evolutionary computing to credit scoring

The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant decisions for financial institutions has caused growing interest in using a variety of computational intelligence techniques. This paper concentrates on evolutionary computing, which is viewed as one of the most promising paradigms of computational intelligence. Taking into account the synergistic relationship between the communities of Economics and Computer Science, the aim of this paper is to summarize the most recent developments in the application of evolutionary algorithms to credit scoring by means of a thorough review of scientific articles published during the period 2000–2012.This work has partially been supported by the Spanish Ministry of Education and Science under grant TIN2009-14205 and the Generalitat Valenciana under grant PROMETEO/2010/028

Feature selection in a credit scoring model

This article belongs to the Special Issue Mathematics and Mathematical Physics Applied to Financial Markets.This paper proposes different classification algorithms—logistic regression, support vector machine, K-nearest neighbors, and random forest—in order to identify which candidates are likely to default for a credit scoring model. Three different feature selection methods are used in order to mitigate the overfitting in the curse of dimensionality of these classification algorithms: one filter method (Chi-squared test and correlation coefficients) and two wrapper methods (forward stepwise selection and backward stepwise selection). The performances of these three methods are discussed using two measures, the mean absolute error and the number of selected features. The methodology is applied for a valuable database of Taiwan. The results suggest that forward stepwise selection yields superior performance in each one of the classification algorithms used. The conclusions obtained are related to those in the literature, and their managerial implications are analyzed

Using neural networks and support vector machines for default prediction in South Africa

A thesis submitted to the Faculty of Computer Science and Applied Mathematics, University of Witwatersrand, in fulfillment of the requirements for the Master of Science (MSc) Johannesburg Feb 2017This is a thesis on credit risk and in particular bankruptcy prediction. It investigates the application of machine learning techniques such as support vector machines and neural networks for this purpose. This is not a thesis on support vector machines and neural networks, it simply looks at using these functions as tools to preform the analysis. Neural networks are a type of machine learning algorithm. They are nonlinear mod- els inspired from biological network of neurons found in the human central nervous system. They involve a cascade of simple nonlinear computations that when aggre- gated can implement robust and complex nonlinear functions. Neural networks can approximate most nonlinear functions, making them a quite powerful class of models. Support vector machines (SVM) are the most recent development from the machine learning community. In machine learning, support vector machines (SVMs) are su- pervised learning algorithms that analyze data and recognize patterns, used for clas- si cation and regression analysis. SVM takes a set of input data and predicts, for each given input, which of two possible classes comprises the input, making the SVM a non-probabilistic binary linear classi er. A support vector machine constructs a hyperplane or set of hyperplanes in a high or in nite dimensional space, which can be used for classi cation into the two di erent data classes. Traditional bankruptcy prediction medelling has been criticised as it makes certain underlying assumptions on the underlying data. For instance, a frequent requirement for multivarate analysis is a joint normal distribution and independence of variables. Support vector machines (and neural networks) are a useful tool for default analysis because they make far fewer assumptions on the underlying data. In this framework support vector machines are used as a classi er to discriminate defaulting and non defaulting companies in a South African context. The input data required is a set of nancial ratios constructed from the company's historic nancial statements. The data is then Divided into the two groups: a company that has defaulted and a company that is healthy (non default). The nal data sample used for this thesis consists of 23 nancial ratios from 67 companies listed on the jse. Furthermore for each company the company's probability of default is predicted. The results are benchmarked against more classical methods that are commonly used for bankruptcy prediction such as linear discriminate analysis and logistic regression. Then the results of the support vector machines, neural networks, linear discriminate analysis and logistic regression are assessed via their receiver operator curves and pro tability ratios to gure out which model is more successful at predicting default.MT 201

Supervised classification and mathematical optimization

Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data.Ministerio de Ciencia e InnovaciónJunta de Andalucí

Binarized support vector machines

The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in Data Mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables, and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals which are critical for the classification. The method involves the optimization of a Linear Programming problem, with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard Column-Generation strategy leads to a classification method which, in terms of classification ability, is competitive against the standard linear SVM and Classification Trees. Moreover, the proposed method is robust, i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler, still competitive, classifiers