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With the coming into force of the standard IFRS 9 – Financial Instruments, in January 2018, financial institutions passed from an incurred loss model to a forward-looking model for the computation of impairment losses. As such, the IFRS 9 models use point-in-time, estimates of Probability of Default and Loss Given Default and provide a more faithful representation of the credit risk at a given as they are based on past experiences as well as the most recent and forecasted economic conditions. However, given the short-term fluctuations in the macroeconomic conditions, the final outcome of the Expected credit loss models is highly volatile due to their sensitivity to the business cycle. With regard to Probability of Default estimation under IFRS 9, the most commonly methods are: Markov Chains, Survival Analysis and single-factor models (Vasicek and Z-Shift). The development of the score-cards is still the same as in the case of the Internal Ratings Based Probability of Default models, encouraging institutions to use the already available credit rating systems and perform adjustment to the calibration. This paper outlines a non-exhaustive list of quantitative validation tests would satisfy the requirements of the IFRS 9 standard.

In recent years, credit scoring studies have been considered by researchers. In this paper, with a new approach based on statistical methods and rough set theory, the rules are extracted for classification. To this aim, the credit scoring data set of UCI University, Australia has been used. The proposed algorithm is very simple and comprehensible and is very efficient for the problem under study, in comparison to other known methods. [ABSTRACT FROM AUTHOR]

The problem of unpaid bank debts is becoming increasingly important in developed countries. Many empirical works are being published in an attempt to find a model capable of determining as accurately as possible whether an individual requesting a loan will be able to pay it back. This paper analyses the predicting capability of one non-parametric and two parametric models. As regards the former, the often-overlooked problem of overlearning is also tackled using the cross-validation technique. Furthermore, a three-level grading of loan applications is proposed depending on their likely performance: grant, refuse, or doubtful hence subject to manual consideration by bank staff. [ABSTRACT FROM AUTHOR]

This paper presents two cases of random banking data generators based on migration matrices and scoring rules. The banking data generator is a breakthrough in researches aimed at finding a method to compare various credit scoring techniques. These data are very useful for various analyses to understand the complexity of banking processes in a better way and are also of use for students and their researches. Another application can be in the case of small samples, e.g. when historical data are too fresh or are connected with the processing of a small number of exposures. In these cases a data generator can extend a sample to an adequate size for advanced analysis. The influence of one cyclic macro-economic variable on client characteristics and their stability over time is analyzed. Some stimulating conclusions for crisis behavior are presented, namely that if a crisis is impacted by both factors: application and behavioral, then it is very difficult to clearly indicate these factors in a typical scoring analysis and the crisis becomes widespread in every kind of risk report. [ABSTRACT FROM AUTHOR]

Abstract: The most commonly used techniques for credit scoring is logistic regression, and more recent research has proposed that the support vector machine is a more effective method. However, both logistic regression and support vector machine suffers from curse of dimension. In this paper, we introduce a new way to address this problem which is defined as orthogonal dimension reduction. We discuss the related properties of this method in detail and test it against other common statistical approaches—principal component analysis and hybridizing logistic regression to better solve and evaluate the data. With experiments on German data set, there is also an interesting phenomenon with respect to the use of support vector machine, which we define as ‘Dimensional interference’, and discuss in general. Based on the results of cross-validation, it can be found that through the use of logistic regression filtering the dummy variables and orthogonal extracting feature, the support vector machine not only reduces complexity and accelerates convergence, but also achieves better performance. [Copyright &y& Elsevier]

Copyright of International Business & Global Economy is the property of Jagiellonian University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: The databases of the banks around the world have accumulated large quantities of information about clients and their financial and payment history. These databases can be used for the credit risk assessment, but they are commonly high dimensional. Irrelevant features in a training dataset may produce less accurate results of classification analysis. Data preprocessing is required to prepare the data for classification to increase the predictive accuracy. Feature selection is a preprocessing technique commonly used on high dimensional data and its purposes include reducing dimensionality, removing irrelevant and redundant features, facilitating data understanding, reducing the amount of data needed for learning, improving predictive accuracy of algorithms, and increasing interpretability of models. In this paper we investigate the extent to which the total data, owned by a bank, can be a good basis for predicting the borrower’s ability to repay the loan on time. We propose a feature selection technique for finding an optimum feature subset that enhances the classification accuracy of neural network classifiers. Experiments were conducted on the credit dataset collected at a Croatian bank to assess the accuracy of our technique. We found that the hybrid system with genetic algorithm is competitive and can be used as feature selection technique to discover the most significant features in determining risk of default. [Copyright &y& Elsevier]

In this paper, we proposed a new three-parameter long-term lifetime distribution induced by a latent complementary risk framework with decreasing, increasing and unimodal hazard function, the long-term complementary exponential geometric distribution. The new distribution arises from latent competing risk scenarios, where the lifetime associated scenario, with a particular risk, is not observable, rather we observe only the maximum lifetime value among all risks, and the presence of long-term survival. The properties of the proposed distribution are discussed, including its probability density function and explicit algebraic formulas for its reliability, hazard and quantile functions and order statistics. The parameter estimation is based on the usual maximum-likelihood approach. A simulation study assesses the performance of the estimation procedure. We compare the new distribution with its particular cases, as well as with the long-term Weibull distribution on three real data sets, observing its potential and competitiveness in comparison with some usual long-term lifetime distributions. [ABSTRACT FROM PUBLISHER]

Abstract: Many techniques have been proposed for credit risk assessment, from statistical models to artificial intelligence methods. During the last few years, different approaches to classifier ensembles have successfully been applied to credit scoring problems, demonstrating to be generally more accurate than single prediction models. The present paper goes one step beyond by introducing composite ensembles that jointly use different strategies for diversity induction. Accordingly, the combination of data resampling algorithms (bagging and AdaBoost) and attribute subset selection methods (random subspace and rotation forest) for the construction of composite ensembles is explored with the aim of improving the prediction performance. The experimental results and statistical tests show that this new two-level classifier ensemble constitutes an appropriate solution for credit scoring problems, performing better than the traditional single ensembles and very significantly better than individual classifiers. [Copyright &y& Elsevier]

Abstract: Many techniques have been proposed for credit risk assessment, from statistical models to artificial intelligence methods. During the last few years, different approaches to classifier ensembles have successfully been applied to credit scoring problems, demonstrating to be more accurate than single prediction models. However, it is still a question what base classifiers should be employed in each ensemble in order to achieve the highest performance. Accordingly, the present paper evaluates the performance of seven individual prediction techniques when used as members of five different ensemble methods. The ultimate aim of this study is to suggest appropriate classifiers for each ensemble approach in the context of credit scoring. The experimental results and statistical tests show that the C4.5 decision tree constitutes the best solution for most ensemble methods, closely followed by the multilayer perceptron neural network and logistic regression, whereas the nearest neighbour and the naive Bayes classifiers appear to be significantly the worst. [Copyright &y& Elsevier]

Consumer credit scoring is considered as a crucial issue in the credit industry. SVM has been successfully utilized for classification in many areas including credit scoring. Kernel function is vital when applying SVM to classification problem for enhancing the prediction performance. Currently, most of kernel functions used in SVM are single kernel functions such as the radial basis function (RBF) which has been widely used. On the basis of the existing kernel functions, this paper proposes a multi-kernel function to improve the learning and generalization ability of SVM by integrating several single kernel functions. Chaos particle swarm optimization (CPSO) which is a kind of improved PSO algorithm is utilized to optimize parameters and to select features simultaneously. Two UCI credit data sets are used as the experimental data to evaluate the classification performance of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model () and a logistic regression with state-dependent sample selection model () applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. [Copyright &y& Elsevier]

Credit scoring computation essentially involves taking into account various financial factors and the previous behavior of the credit requesting person. There is a strong degree of correlation between the compliance level and the credit score of a given entity. The concept of trust has been widely used and applied in the existing literature to determine the compliance level of an entity. However it has not been studied in the context of credit scoring literature. In order to address this shortcoming, in this paper we propose a six-step bio-inspired methodology for trust-based credit lending decisions by credit institutions. The proposed methodology makes use of an artificial neural network-based model to classify the (potential) customers into various categories. To show the applicability and superiority of the proposed algorithm, it is applied to a credit-card dataset obtained from the UCI repository. Due to the varying spectrum of trust levels, we are able to solve the problem of binary credit lending decisions. A trust-based credit scoring approach allows the financial institutions to grant credit-based on the level of trust in potential customers. [ABSTRACT FROM AUTHOR]

Copyright of REGE Revista de Gestão is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: As the credit industry has been growing rapidly, credit scoring models have been widely used by the financial industry during this time to improve cash flow and credit collections. However, a large amount of redundant information and features are involved in the credit dataset, which leads to lower accuracy and higher complexity of the credit scoring model. So, effective feature selection methods are necessary for credit dataset with huge number of features. In this paper, a novel approach, called RSFS, to feature selection based on rough set and scatter search is proposed. In RSFS, conditional entropy is regarded as the heuristic to search the optimal solutions. Two credit datasets in UCI database are selected to demonstrate the competitive performance of RSFS consisted in three credit models including neural network model, J48 decision tree and Logistic regression. The experimental result shows that RSFS has a superior performance in saving the computational costs and improving classification accuracy compared with the base classification methods. [Copyright &y& Elsevier]

ABSTRACT Due to growing competition, over-indebtedness, and economic crises, microfinance institutions have to pursue their social and financial objectives in an increasingly constrained environment. Developing powerful risk management tools becomes more than ever crucial to survive. Therefore this paper analyzes whether microfinance institutions can benefit from credit scoring, which has been successfully adopted in retail banking. An extensive literature overview is provided, indicating a lack of quantitative evidence, in particular for markets in Eastern Europe-Central Asia and the Middle East-Northern Africa. Two logistic regression-based scoring models are developed using data from a Bosnia-Herzegovinian microlender. The models are assessed in terms of stability, readability, and discriminatory power, indicating that credit scoring is not yet able to fully replace the human-intensive microfinance credit process. However, it is recommendable to introduce credit scoring as a refinement tool in the lending process, in order to combine both statistical and human best practices. Moreover, a microlender staff can learn from credit scoring models to validate or contrast practical intuition. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A credit scoring method for a large problem not only achieves a certain accuracy but also its speed, interpretability, simplicity and other performance are also very important. In this paper, a novel method called an improved SVM learning algorithm based on three-variable working set (ISVMTV) is presented. This algorithm is derived by solving a series of the QP problems with only three points and the corresponding relaxation subproblems are solved analytically so that the proposed method approaches to the optimal solution more quickly. The proposed method is introduced to credit scoring and three datasets from UCI machine learning datasets are selected to demonstrate the method's competitive performance. Moreover, ISVM-TV shows a superior performance in saving the computational cost and improving classification accuracy. [ABSTRACT FROM AUTHOR]

Abstract: Most studies have concentrated on building an accurate credit scoring model to decide whether or not to grant credit to new applicants and the efforts to build more accurate credit scoring model seems to be not significant. In this paper, we proposed a hybrid data mining technique which contains two processing stages. In the clustering stage, the samples of the accepted and new applicants are grouped into homogeneous clusters, the isolated samples are deleted and inconsistent samples are relabeled. In the classification stage, support vector machines used samples with new labels to build the scoring model. The difference from the other credit scoring model is that the samples were classified into three or four classes, rather than two the good and the bad credit classes. Experimental results based on the credit data set provided by a local bank in China showed that by choosing a proper cut-off point, super classification accuracy of the good and the bad credit is obtained. Risk management strategies are developed according to the characteristic of each class. [Copyright &y& Elsevier]

This paper outlines an approach to improving credit score modeling using random forests and compares random forests with logistic regression. It is shown that on data sets where variables have multicollinearity and complex interrelationships random forests provide a more scientific approach to analyzing variable importance and achieving optimal predictive accuracy. In addition it is shown that random forests should be used in econometric and credit risk models as they provide a powerful too to assess meaning of variables not available in standard regression models and thus allow for more robust findings. [ABSTRACT FROM AUTHOR]

Abstract: Credit scoring and evaluation is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. Artificial neural networks (NNs) have been considered to be accurate tools for credit analysis among others in the credit industry. Lately, emotional neural networks (EmNNs) have been suggested and applied successfully for pattern recognition. In this paper we investigate the efficiency of EmNNs and compare their performance to conventional NNs when applied to credit risk evaluation. In total 12 neural networks; based equally on emotional and conventional neural models; are arbitrated under three learning schemes to classify whether a credit application is approved or declined. The learning schemes differ in the ratio of training-to-validation data used during training and testing the neural networks. The emotional and conventional neural models are trained using real world credit application cases from the Australian credit approval datasets which has 690 cases; each case with 14 numerical attributes; based on which an application is accepted or rejected. The performance of the 12 neural networks will be evaluated using certain criteria. Experimental results suggest that both emotional and conventional neural models can be used effectively for credit risk evaluations, however the emotional models outperform their conventional counterparts in decision making speed and accuracy, thus, making them ideal for implementation in fast automatic processing of credit applications. [Copyright &y& Elsevier]

Abstract: Credit scoring model have been developed by banks and researchers to improve the process of assessing credit worthiness during the credit evaluation process. The objective of credit scoring models is to assign credit risk to either a “good risk” group that is likely to repay financial obligation or a “bad risk” group who has high possibility of defaulting on the financial obligation. Construction of credit scoring models requires data mining techniques. Using historical data on payments, demographic characteristics and statistical techniques, credit scoring models can help identify the important demographic characteristics related to credit risk and provide a score for each customer. This paper illustrates using data mining to improve assessment of credit worthiness using credit scoring models. Due to privacy concerns and unavailability of real financial data from banks this study applies the credit scoring techniques using data of payment history of members from a recreational club. The club has been facing a problem of rising number in defaulters in their monthly club subscription payments. The management would like to have a model which they can deploy to identify potential defaulters. The classification performance of credit scorecard model, logistic regression model and decision tree model were compared. The classification error rates for credit scorecard model, logistic regression and decision tree were 27.9%, 28.8% and 28.1%, respectively. Although no model outperforms the other, scorecards are relatively much easier to deploy in practical applications. [Copyright &y& Elsevier]

Abstract: Credit scoring modelling comprises one of the leading formal tools for supporting the granting of credit. Its core objective consists of the generation of a score by means of which potential clients can be listed in the order of the probability of default. A critical factor is whether a credit scoring model is accurate enough in order to provide correct classification of the client as a good or bad payer. In this context the concept of bootstraping aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the fitted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper we propose a new bagging-type variant procedure, which we call poly-bagging, consisting of combining predictors over a succession of resamplings. The study is derived by credit scoring modelling. The proposed poly-bagging procedure was applied to some different artificial datasets and to a real granting of credit dataset up to three successions of resamplings. We observed better classification accuracy for the two-bagged and the three-bagged models for all considered setups. These results lead to a strong indication that the poly-bagging approach may promote improvement on the modelling performance measures, while keeping a flexible and straightforward bagging-type structure easy to implement. [Copyright &y& Elsevier]

Numerous studies have contributed to efforts to boost the accuracy of the credit scoring model. Especially interesting are recent studies which have successfully developed the hybrid approach, which advances classification accuracy by combining different machine learning techniques. However, to achieve better credit decisions, it is not enough merely to increase the accuracy of the credit scoring model. It is necessary to conduct meaningful supplementary analyses in order to obtain knowledge of causal relations, particularly in terms of significant conceptual patterns or structures involving attributes used in the credit scoring model. This paper proposes a solution of integrating data preprocessing strategies and the Bayesian network classifier with the tree augmented Na"ıve Bayes search algorithm, in order to improve classification accuracy and to obtain improved knowledge of causal patterns, thus enhancing the validity of credit decisions. [ABSTRACT FROM AUTHOR]

The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring. [ABSTRACT FROM AUTHOR]

The development of an effective credit scoring model has become a very important issue as the credit industry is confronted with ever-intensifying competition and aggravating bad debt problems. During the past few years, a substantial number of studies in the field of statistics have been conducted to improve the accuracy of credit scoring models. In order to refine the classification and decrease misclassification, this paper presents a two-stage model. Focusing on classification, the first stage aims at constructing an artificial neural network (ANN)-based credit scoring model to categorize applicants into the group of accepted (good) credit and the group of rejected (bad) credit. Switching from classification to reassignment, the second stage proceeds to reduce the Type I error by retrieving the originally rejected good credit applicants to conditional acceptance using the Case-Based Reasoning (CBR) classification technique. The proposed model (RST-ANN-CBR) is applied to a credit card dataset to verify its effectiveness. As the results indicate, the proposed model is able to achieve more accurate credit scoring than four other methods; more importantly, it is validated to recover potentially lost customers and to increase business revenues. [ABSTRACT FROM AUTHOR]

This paper provides empirical confirmation for Petersen and Rajan's (J Finance 57:2533-2570, ) widely accepted conjecture that information technology was the primary driver of the observed increase in small business borrower-lender distances in the U.S. in recent years. Using a different data source for small business loans, we show that annual increases in borrower-lender distances were slow and steady prior to 1993 (the end point in Petersen and Rajan's data) but accelerated rapidly after that. Importantly, we are able to assign at least half of this acceleration to the adoption of credit scoring technologies by the lending banks. Our tests also reveal strong statistical associations between lending distances and borrower characteristics, lender characteristics, market conditions, regulatory constraints, moral hazard incentives, and principal-agent incentives. [ABSTRACT FROM AUTHOR]

The literature has documented a positive relationship between the use of credit scoring for small business loans and small business credit availability, broadly defined. However, this literature is hampered by the fact that all of the studies are based on a single 1998 survey of the very largest U.S. banking organizations. This paper addresses a number of deficiencies in the extant literature by employing data from a new survey of the use of credit scoring in small business lending, primarily by community banks. The survey evidence suggests that the use of credit scores in small business lending by community banks is surprisingly widespread. Moreover, the scores employed tend to be the consumer credit scores of the small business owners, rather than the more encompassing small business credit scores that include data on the firms as well as on the owners. Our empirical analysis suggests that credit scoring is associated with an initial increase in small business lending activity that moderates over time and no change in the quality of the loan portfolio. Supplementary analysis suggests that the use of credit scores for small business lending has a negative initial effect on community bank profitability that moderates over time. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, more and more people, especially young people, begin to use credit card with the changing of consumption concept in China so that the business on credit cards is growing fast. Therefore, it is significative that some effective tools such as credit-scoring models are created to help those decision makers engaged in credit cards. A novel credit-scoring model, called vertical bagging decision trees model (abbreviated to VBDTM), is proposed for the purpose in this paper. The model is a new bagging method that is different from the traditional bagging. The VBDTM model gets an aggregation of classifiers by means of the combination of predictive attributes. In the VBDTM model, all train samples and just parts of attributes take part in learning of every classifier. By contrast, classifiers are trained with the sample subsets in the traditional bagging method and every classifier has the same attributes. The VBDTM has been tested by two credit databases from the UCI Machine Learning Repository, and the analysis results show that the performance of the method proposed by us is outstanding on the prediction accuracy. [ABSTRACT FROM AUTHOR]

Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, because data-driven mining automatically generates classification outputs that are imprecise, meaningless, and beyond users' control. In this paper, we provide a novel domain-driven classification method that takes advantage of multiple criteria and multiple constraint-level programming for intelligent credit scoring. The method involves credit scoring to produce a set of customers' scores that allows the classification results actionable and controllable by human interaction during the scoring process. Domain knowledge and experts' experience parameters are built into the criteria and constraint functions of mathematical programming and the human and machine conversation is employed to generate an efficient and precise solution. Experiments based on various data sets validated the effectiveness and efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

The growth of microcredit worldwide along with international rules on capital requirements (Basel II) are increasing the competition between microfinance institutions (MFIs) and banks for this business segment. The bank system traditionally has relied on adequate credit scoring models to analyze the risk of payment failures, but this has not been the case in supervised MFIs. The objective of this research is to design a credit scoring model for any institution subjected to supervision and specialized in microcredit as the Development Agency for Small and Micro Enterprise (Entidad de Desarrollo de la Pequeña y Micro Empresa - Edpyme) of the financial system in Peru. The results of this research includes a methodology and the steps needed to design the model, and the assessment and validation process that can be applied in the business area, in particular, to establish an interest rate policy with customers. Eventually, the paper also explains how the model can be used to develop credit risk management under the Basel II IRB approaches. [ABSTRACT FROM AUTHOR]

Abstract: As the credit industry has been growing rapidly, huge number of consumers’ credit data are collected by the credit department of the bank and credit scoring has become a very important issue. Usually, a large amount of redundant information and features are involved in the credit dataset, which leads to lower accuracy and higher complexity of the credit scoring model. So, effective feature selection methods are necessary for credit dataset with huge number of features. In this paper, a novel approach, called FSRT, to feature selection based on rough set and tabu search is proposed. In FSRT, conditional entropy is regarded as the heuristic to search the optimal solutions. The proposed method is introduced to credit scoring and Japan credit dataset in UCI database is selected to demonstrate the competitive performance of the proposed method. Moreover, FSRT shows a superior performance in saving the computational costs and improving classification accuracy. [ABSTRACT FROM AUTHOR]

In developing a classification model for assigning observations of unknown class to one of a number of specified classes using the values of a set of features associated with each observation, it is often desirable to base the classifier on a limited number of features. Mathematical programming discriminant analysis methods for developing classification models can be extended for feature selection. Classification accuracy can be used as the feature selection criterion by using a mixed integer programming (MIP) model in which a binary variable is associated with each training sample observation, but the binary variable requirements limit the size of problems to which this approach can be applied. Heuristic feature selection methods for problems with large numbers of observations are developed in this paper. These heuristic procedures, which are based on the MIP model for maximizing classification accuracy, are then applied to three credit scoring data sets. [ABSTRACT FROM AUTHOR]

This paper introduces a novel family of indexes to describe borrowers' creditworthiness in retail credit products, both for fixed term loans and for open-ended products such as credit cards. Each index is the ratio at a given time of the net present value of actually received cashfiows to the contractual ones. Some interpretations of the indexes are given and it is also described how to link them to the profitability, of the credit financial operation. For open-ended products, a competing risks survival analysis methodology is proposed to estimate the cashflow returns and illustrated with a simulation. [ABSTRACT FROM AUTHOR]

The advent of Internet broking pages allows customers to 'apply' to a number of different companies at one time, leading to multiple offers made to a customer. The saturated condition of the personal financial products has led to falling 'take' rates. Financial institutions are trying to increase the 'take' rates of their personal financial products. Applicants for credit will have to provide information for risk assessment, which can be used to assess the probability of a customer accepting an offer. Interactive channels such as the Internet and telephone allow questions that are asked to depend on previous answers. The questions selected need to provide information to assess the probability of acceptance of a particular variant of financial product. In this paper, we investigate a model to predict the best offer to extend next to a customer based on the response for the questions, as well as the question selection itself. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a quick credibility scoring decision support system is developed for the banks to determine the credibility of manufacturing firms in Turkey. The proposed decision support system is expected to be used by the banks when they want to determine whether an applicant firm is worth a detailed credit check or not. Using such a quick credit scoring decision model reduces the banks’ workload. The proposed credit scoring model is based on the financial ratios and fuzzy TOPSIS approach. It obtains two separate scores which reflect the attractiveness of manufacturing industries within the overall economy and manufacturing firms’ performance with respect to its competitors belonging to the same industry. These two scores are then used to determine the credibility of applicant manufacturing firms. The developed decision support system is tested with various real cases and satisfactory results are obtained. An application is also provided in the paper for illustrative purposes. [Copyright &y& Elsevier]

In this paper the Fuzzy ARTMAP neural network is used to solve a credit scoring problem, based on a real world dataset. The FAM architecture and learning algorithm are presented. The network's parameters are found through a validation stage and the results are reported for different test sets. The critical step of data preprocessing is discussed and two preprocessing approaches are presented and compared. FAM's good classification accuracy is shown in the experimental section. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Credit scoring models are very important tools for financial institutions to make credit granting decisions. In the last few decades, many quantitative methods have been used for the development of credit scoring models with focus on maximizing classification accuracy. This paper proposes the credit scoring models with the area under receiver operating characteristics curve (AUC) maximization based on the new emerged support vector machines (SVM) techniques. Three main SVM models with different features weighted strategies are discussed. The weighted SVM credit scoring models are tested using 10-fold cross validation with two real world data sets and the experimental results are compared with other six traditional methods including linear regression, logistic regression, k nearest neighbor, decision tree, and neural network. Results demonstrate that weighted 2-norm SVM with radial basis function (RBF) kernel function and t-test feature weighting strategy has the overall better performance with very narrow margin than other SVM models. However, it also consumes more computational time. In considering the balance of performance and time, least squares support vector machines (LSSVM) with RBF kernel maybe a better choice for large scale credit scoring applications. [ABSTRACT FROM AUTHOR]

Survival analysis can be applied to build models for time to default on debt. In this paper, we report an application of survival analysis to model default on a large data set of credit card accounts. We explore the hypothesis that probability of default (PD) is affected by general conditions in the economy over time. These macroeconomic variables (MVs) cannot readily be included in logistic regression models. However, survival analysis provides a framework for their inclusion as time-varying covariates. Various MVs, such as interest rate and unemployment rate, are included in the analysis. We show that inclusion of these indicators improves model fit and affects PD yielding a modest improvement in predictions of default on an independent test set. [ABSTRACT FROM AUTHOR]

Abstract: Classification and regression models are widely used by mainstream credit granting institutions to assess the risk of customer default. In practice, the objectives used to derive model parameters and the business objectives used to assess models differ. Models parameters are determined by minimising some function or error or by maximising likelihood, but performance is assessed using global measures such as the GINI coefficient, or the misclassification rate at a specific point in the score distribution. This paper seeks to determine the impact on performance that results from having different objectives for model construction and model assessment. To do this a genetic algorithm (GA) is utilized to generate linear scoring models that directly optimise business measures of interest. The performance of the GA models is then compared to those constructed using logistic and linear regression. Empirical results show that all models perform similarly well, suggesting that modelling and business objectives are well aligned. [Copyright &y& Elsevier]

Abstract: Credit scoring has become a critical and challenging management science issue, as the credit industry has been facing fiercer competition in recent years. Many methods have been suggested to tackle this problem in the literature. In this paper, we proposed hybrid support vector machine technique based on three strategies: (1) using CART to select input features, (2) using MARS to select input features, (3) using grid search to optimize model parameters. In order to verify the feasibility and effectiveness of the proposed hybrid SVM model, one credit card dataset provided by a local bank in China is used in this study. Analytic results demonstrate that the hybrid SVM technique not only has the best classification rate, but also has the lowest Type II error in comparison with CART, MARS and SVM and justify the presumptions that SVM having better capability of capturing nonlinear relationship among variables. [Copyright &y& Elsevier]

Abstract: Banks provide a financial intermediary service by channeling funds efficiently between borrowers and lenders. Bank lending is subject to credit risk when loans are not paid back on a timely basis or are in default. The ability or possessing a methodology to evaluate the creditworthiness of a borrower is therefore crucial to managing the bank’s risk management and profitability. The aim of the paper is dichotomous classification of the individual borrowers to the groups of creditworthy or non-creditworthy clients. The recognition of borrowers is provided applying single and aggregated classification trees. Classification trees are a powerful alternative to the more traditional statistical models. This model has the advantage of being able to detect non-linear relationships and showing a good performance in presence of qualitative information as it happens in the creditworthiness evaluation of individual borrowers. As a result, they are widely used as base classifiers for ensemble methods. Aggregated classification trees are constructed employing two ensemble methods: Adaboost and bagging. AdaBoost constructs its base classifiers in sequence, updating a distribution over the training examples to create each base classifier. Bagging combines the individual classifiers built in bootstrap replicates of the training set. The research is conducted employing actual data regarding the individual borrowers that got a mortgage credit in one of the commercial banks that operate in Poland. Each of the clients is described by 11 variables. The grouping variable informs if the client pays off the credit regularly due to the credit agreement or he is back in loan redemption. Diagnostic variables describe the clients in terms of demographic features and characterize the credits that are to be paid back (i.e. value and currency of the credit, credit rate, etc.). [Copyright &y& Elsevier]

Credit scoring is playing an increasingly critical role with the rising number of lending operations for micro and small enterprises as well as individuals. A large number of research is primarily based on the combination and construction methods of credit scoring models by the experts. However, different credit data have distinct requirements for the models, and how to automatically search and construct credit scoring models according to credit data has become essential, that is the main concern of this paper. In response to the current challenges for credit scoring research, we proposed an Automatic Credit Scoring Strategy (ACSS), designed a credit assessment platform which includes data import, classification model automatic search, feature selection, hyperparameter optimization, data mining, classification output and other modules. Aiming at the problem of substantial imbalance in credit data, we propose an improved SMOTE algorithm that is capable of generating supplementary data for the lack of minority in credit data, thereby making the credit data distribution well balanced. As for classification model selection, features engineering, parameter optimization and other parts, we further incorporate automatic search ways to reduce manual interaction. We utilize public and self-owned credit data sets to conduct experiments and compare them with the latest credit assessment methods. Extensive experiments have demonstrated that our ACSS method achieves relatively noticeable performance improvements using the German credit dataset, the Taiwan credit dataset and the personal credit dataset for credit scoring. The best results achieved by our proposed method are 0.98, 0.99, 0.895 and 0.901 for MAE, RMSE, accuracy and precision respectively. In addition, the experimental results also show that our proposed improved SMOTE algorithm contributes to the credit scoring performance enhancement. The experimental findings suggest that our proposed ACSS balances automation and accuracy, which can be implemented to the consumption industry to enhance the reliability of credit assessments. • We utilize an improved SMOTE that could decrease the impact of data imbalance that exists in the credit data. • We propose to leverage the credit feature pruning algorithm and memetic optimization algorithm. • We propose an automated credit scoring strategy (ACSS) method with a cost-effective neural architecture search (C-NAS) scheme. [ABSTRACT FROM AUTHOR]

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This paper proposes a DEA-based approach to credit scoring. Compared with conventional models such as multiple discriminant analysis, logistic regression analysis, and neural networks for business failure prediction, which require extra a priori information, this new approach solely requires ex-post information to calculate credit scores. For the empirical evidence, this methodology was applied to current financial data of external audited 1061 manufacturing firms comprising the credit portfolio of one of the largest credit guarantee organizations in Korea. Using financial ratios, the methodology could synthesize a firm’s overall performance into a single financial credibility score. The empirical results were also validated by supporting analyses (regression analysis and discriminant analysis) and by testing the model’s discriminatory power using actual bankruptcy cases of 103 firms. In addition, we propose a practical credit rating method using the predicted DEA scores. [Copyright &y& Elsevier]

Abstract: Neural nets have become one of the most important tools using in credit scoring. Credit scoring is regarded as a core appraised tool of commercial banks during the last few decades. The purpose of this paper is to investigate the ability of neural nets, such as probabilistic neural nets and multi-layer feed-forward nets, and conventional techniques such as, discriminant analysis, probit analysis and logistic regression, in evaluating credit risk in Egyptian banks applying credit scoring models. The credit scoring task is performed on one bank’s personal loans’ data-set. The results so far revealed that the neural nets-models gave a better average correct classification rate than the other techniques. A one-way analysis of variance and other tests have been applied, demonstrating that there are some significant differences amongst the means of the correct classification rates, pertaining to different techniques. [Copyright &y& Elsevier]