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Semiparametric models hold promise to address many challenges to statistical inference that arise from real‐world applications, but their novelty and theoretical complexity create challenges for estimation. Taking advantage of the broad applicability of semiparametric models, we propose some novel and improved methods to estimate the regression coefficients of generalized partially linear models (GPLM). This model extends the generalized linear model by adding a nonparametric component. Like in parametric models, variable selection is important in the GPLM to single out the inactive covariates for the response. Instead of deleting inactive covariates, our approach uses them as auxiliary information in the estimation procedure. We then define two models, one that includes all the covariates and another that includes the active covariates only. We then combine these two model estimators optimally to form the pretest and shrinkage estimators. Asymptotic properties are studied to derive the asymptotic biases and risks of the proposed estimators. We show that if the shrinkage dimension exceeds two, the asymptotic risks of the shrinkage estimators are strictly less than those of the full model estimators. Extensive Monte Carlo simulation studies are conducted to examine the finite‐sample performance of the proposed estimation methods. We then apply our proposed methods to two real data sets. Our simulation and real data results show that the proposed estimators perform with higher accuracy and lower variability in the estimation of regression parameters for GPLM compared with competing estimation methods. [ABSTRACT FROM AUTHOR]

Peer‐to‐peer lending, as a novel lending model, has challenged investors to make effective investment decisions. Issued loans are grouped into default and nondefault. Therefore, different classification methods can be utilized to predict the status of loans in the future. Our study aim is to propose an investment decision model based on the nondefault loans predicted using three different classifiers, including random forest (RF) that is a multitude of decision trees, support vector machine, and naïve Bayes. In fact, we combine each of these classifiers with the portfolio optimization problem to understand which combination leads to the best portfolio concerning the risk and return. In order to find the best combined model, numerical studies are conducted based on real‐world data. In addition, the performances of these classifiers are evaluated based on different performance measures using the 10‐fold cross‐validation method. The results indicate that combining the RF algorithm and portfolio optimization problem leads to the least level of risk. Moreover, it is concluded that RF is the best classifier in terms of performance analysis. [ABSTRACT FROM AUTHOR]

Summary: Credit scoring is one the most important parts of credit risk management in reducing the risk of client defaults and bankruptcies. Deep learning has received much attention in recent years, but it has not been implemented so intensively in credit scoring compared to other financial domains. In this article, stacked unidirectional and bidirectional LSTM (long short‐term memory) networks as a complex area of deep learning are applied in solving credit scoring problems for the first time. The proposed robust model exploits the full potential of the three‐layer stacked LSTM and BDLSTM (bidirectional LSTM) architecture with the treatment and modeling of public datasets in a novel way since credit scoring is not a time sequence problem. Attributes of each loan instance were transformed into a sequence of the matrix with a fixed sliding window approach with a one‐time step. Our proposed models outperform existing and much more complex deep learning solutions thus we succeeded in preserving simplicity. In this article, measures of different types are employed to carry out consistent conclusions. The results by applying three hidden layers on the German Credit dataset showed an accuracy of 87.19%, for Kaggle dataset accuracy reached 93.69%, and for Microcredit dataset accuracy of 97.80%. [ABSTRACT FROM AUTHOR]

The main issue in a classification problem is classifying observations into various disjoint classes. Different classification techniques generate a continuous number between a and b, usually between 0 and 1; thus, the optimal cut‐off value(s) must be carefully selected to discriminate classes precisely. The decision is about setting a threshold value and transforming the continuous score into a binary output. Therefore, in addition to using the so‐called sophisticated classification methods to have a more accurate classification, there is a need to identify and choose the optimal threshold value(s). However, the latter has not been thoroughly investigated. Hence, this study proposes an approach based on a Genetic Algorithm (GA) and Neural Networks (NNs) to automatically find customized cut‐off values, considering different performance criteria and given datasets. Since credit scoring is a binary classification problem, two popular credit scoring datasets, namely "Australian" and "German" credit datasets, are used to test the proposed approach. Our numerical results revealed that the proposed GA‐NN model could successfully find customized acceptance thresholds, considering predetermined performance criteria, including Accuracy, Estimated Misclassification Cost (EMC), and Area under ROC Curve (AUC) for the tested datasets. Furthermore, the best‐obtained results and the paired‐samples t‐test results show that utilizing the customized cut‐off points leads to a more accurate classification than the commonly‐used threshold value of 0.5. [ABSTRACT FROM AUTHOR]

Online consumer lending has recently been growing rapidly, but it faces high credit risk. For this problem, developing powerful credit scoring models has become an effective solution and can be achieved from three aspects: modeling approach, data source, and evaluation measure. This paper proposes a novel model that departs from those in previous studies in threefold. First, a heterogeneous deep forest model that combines deep learning architecture and tree‐based ensemble classifiers is proposed as the modeling approach. Second, a Bayesian‐based macroeconomic variable optimization method is developed to determine the macroeconomic variables and the corresponding lag term, and the selected macroeconomic variables are used as supplementary data source for modeling. Lastly, a series of capital charge error measures is proposed to evaluate credit scoring models from a regulatory perspective. The proposal is evaluated on multiple large datasets under performance measures on predictive accuracy, profitability, and capital charge errors. Frequentist and Bayesian nonparametric significance tests are used to examine the statistical significance of heterogeneous deep forest and benchmarks. Three main conclusions can be reached from the comparison. First, heterogeneous deep forest significantly outperforms the industry benchmarks over all the evaluation measures. Second, the predictive performance is enhanced after incorporating the selected macroeconomic variables and the corresponding lag, and the result remains robust under cross‐validation and forward‐chaining validation. Third, the capital charge errors reflect the model performance from a regulatory perspective and thus lead to different rankings from those when evaluating predictive accuracy and profitability. [ABSTRACT FROM AUTHOR]

Rural microfinance must contend with the triple challenges of isolation, small‐scale transactions, and risk. These challenges result in information asymmetries and transaction costs that render markets for financial services costly or missing. This paper examines how emerging digital technologies (e.g., mobile money, digital credit scoring, and earth observation) can reshape rural markets for savings, credit, and insurance services, especially in developing countries. Although our synthesis of the literature suggests reason for hope in all three domains, the imperfections of these digital technologies require evaluation and oversight if the resulting rural financial system is to be more efficient and equitable than its predecessor. JEL CLASSIFICATION: D14; F63; R51 Related Content: Digital Transformation of the Agricultural Sector: Pathways, Drivers and Policy Implications Related Content: Who drives the digital revolution in agriculture? A review of supply-side trends, players and challenges Related Content: E-commerce's fast-tracking diffusion and adaptation in developing countries [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have successfully been applied to classification problems including credit scoring. The tuning of the structural parameters of the NNs has a direct impact on their accuracy. In this paper, a hybrid approach based on the genetic algorithm (GA) is proposed to adjust the structural parameters of a classifier NN to achieve high accuracy. Two well‐known credit scoring datasets—Australian and German datasets—are used to test the proposed approach. The results indicate that the proposed hybrid approach is able to successfully tune the structural parameters, while the accuracy of classification is enhanced and its complexity dramatically diminished in comparison with other existing approaches. The performance of the proposed algorithm has been investigated through statistical analysis The best‐known solutions achieved by the proposed approach have an accuracy equal to 97.78% and 87.1% for Australian and German datasets, respectively. The results indicate 2.68% and 0.1% improvement in comparison with the best results reported in the literature, respectively. This improvement is important for real cases in which millions of loans are allocated using credit scoring approaches. [ABSTRACT FROM AUTHOR]

The act of lending is based on trust in the borrower to honour the obligation of paying back the lender. Greater spreads on credit operations may help predict the expected recovery of the credit, based on the sufficiency and liquidity of the guarantee. This study aims to understand how predictive models can provide different estimations of expected recovery based on the same data sets. It classifies credit by the formulation of a rule that describes the values of a categorical variable according to some specified definition. It finds that a simple logistic regression model can easily be extended to a multiple logistic regression model by integrating more than one prediction variable, which indicates increasing difficulty in obtaining multiple observations with an increasing number of independent variables. It compares the efficiency of the logistic regression with that of a linear regression in predicting whether recovery is due in a credit operation, and, thus, identifies the best model for this purpose. [ABSTRACT FROM AUTHOR]

In the domain of credit scoring, the number of bad clients is far less than that of good ones. So imbalanced data classification is a realisitc and critical issue in the credit scoring process. In this study, a novel heterogeneous ensemble credit scoring model is proposed for the problem of imbalanced data classification. This proposed model is on basis of five standard classifiers, namely LSVM, KNN, MDA, DT, LR, and adaptively selects the base classifiers with highest AUC according to the data distribution, then integrates all base classifiers to obtain a prediction. Finally, by using five comprehensive performance measures and four classical credit datasets, we find that the proposed model is better than other baseline models. This novel model can be applied to actual credit scoring and assist financial institutions in credit risk management. [ABSTRACT FROM AUTHOR]

The credit scoring is a statistical analysis performed by financial institutions to represent the creditworthiness of an individual or small and medium‐sized enterprise. A credit score is a numerical quantity that can be qualified by a rating label showing the potential risk. In order to determine which customers are likely to bring in the most revenue at the exact interest rate and credit limits, the lenders take into accounts these credit scores. In this context, the robust statistical models are inevitable to reduce the number of wrong decisions in the credit evaluation process. Although the machine learning approaches provide superior performance to conventional statistical methods, they are mostly criticized due to the selection of model structure, model complexity, tuning parameters, time consumption in the high‐dimensional and excessive nonlinear cases. For this reason, this study introduces an efficient model estimation and feature selection procedure for artificial neural network (ANN) classifiers in the context of credit scoring. Essentially, this procedure hybridizes training of ANNs with a novel feature selection approach based on genetic algorithms and information complexity criterion. In the application, the proposed procedure was performed on a couple of benchmark credit scoring datasets. According to analysis results, the proposed approach not only estimates robust models from ANNs in terms of model complexity, feature selection, and time consumption, but also outperforms the traditional training procedure for the classification accuracies, false positive, and false negative errors overtraining and test datasets. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) lending is facing severe information asymmetry problems and depends highly on the internal credit scoring system. This paper provides a novel credit scoring model, which forecasts the probability of default for each applicant and guides the lenders' decision-making in P2P lending. The proposal is expected to improve the existing credit scoring models in P2P lending from two aspects, namely the classifier and the usage of narrative data. We utilize an advanced gradient boosting decision tree technique (i.e., CatBoost) to predict default loans. Moreover, a soft information extraction technique based on keyword clustering is developed to compensate for the insufficient hard credit data. Validated on three real-world datasets, the experimental results demonstrate that variables extracted from narrative data are powerful features, and the utilization of narrative data significantly improves the predictability relative to solely using hard information. The results of sensitivity analysis reveal that CatBoost outperforms the industry benchmark under different cluster numbers of extracted soft information; meanwhile a small number of clusters (e.g., three) is preferred for consideration of model performance, computational cost, and comprehensibility. We finally facilitate a discussion on practical implication and explanatory considerations. [ABSTRACT FROM AUTHOR]

Summary: Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach based on NeuroEvolution of Augmenting Topologies to early predict financial distress of Tunisian companies using an important number of inputs. Our sample covers the period of the Jasmin Revolution that led to an increase of the number of bankruptcies, making early previsions even more difficult. Furthermore, we aim to identify the factors that explain financial distress as our approach does not need a process of preselection. All the financial ratios will be used as inputs for the model, and only the ratios with the highest threshold value will remain in the final classifier. To test the accuracy of our model, we will compute a linear discriminant analysis to compare the previsions of the two approaches. Results show that our approach outperforms the traditional one. [ABSTRACT FROM AUTHOR]

Credit scoring focuses on the development of empirical models to support the financial decision‐making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real‐world credit scoring datasets, namely, Australian, Japanese, German‐categorical, and German‐numerical datasets. [ABSTRACT FROM AUTHOR]

In banking and peer‐to‐peer loan applicant firms, customer credit scores have numerous applications in risk control and precision marketing. Numerous credit scoring techniques act as classification methods. In this paper, the main issue is simultaneous and hybrid utilization of the feature selection (FS) algorithm and ensemble learning classification algorithms with respect to their parameter settings to achieve higher performance in the proposed credit scoring model. As a result, this paper reports a hybrid data mining model of generalized fuzzy soft sets (GFSS) theory‐based ensemble learning classification algorithms based on three stages. The first stage addresses data gathering and preprocessing. The second stage uses the adaptive elastic net‐based FS algorithm to eliminate irrelevant or weakly correlated variables. After the appropriate variables are chosen, they are applied to the proposed ensemble model. In the third stage, GFSS theory is exploited to construct a novel weight assignment mechanism for each individual credit scoring model in the ensemble model according to performance. Several comparisons are conducted to validate the proposed model using real world credit datasets. The experimental results, analysis, and statistical tests prove the ability of the proposed approach to improve classification performance against all of the base classifier, hybrid, and combination methods in terms of average accuracy, area under the curve, H‐measure, and Brier score. [ABSTRACT FROM AUTHOR]

The heart of this study is particularly on risk assessment of financial decision support systems (FDSSs), to advance the model performance and improve classification accuracy. To conquer the downsides of the classical models, statistical intelligence (SI) technologies, for example, multilayer perceptrons (MLPs) and support vector machines (SVMs), have been deliberated in FDSS applications. Recently, the prestigiousness of SI approaches has been confronted by the latest prediction learners. Therefore, to ensure the competitive performance of SI mechanisms, the current investigation scrutinizes the topological applications of MLPs and SVMs over eight different databases with equivalent combinations in credit scoring and bankruptcy predictions example sets. The experimental results reveal that MLP5‐5 and MLP4‐4, that is, the sigmoid activation function with five and four hidden layers, are the feasible topologies for the MLP algorithm, and on all databases in all performance criterions, SVM trained with the linear kernel function (SVM‐1) achieves better prediction results. From the "Baseline" family, random forest learner brings significant improvements in financial decisions. Lastly, FDSSs are found to be correlated with the nature of databases and the performance criterions of the trained algorithms. The results of this study, however, have practical and managerial implications to make a range of financial and nonfinancial strategies. With these contributions, therefore, our study not only supplements earlier evidence but also enhances the predictive performance of SI algorithms for financial decision support applications. [ABSTRACT FROM AUTHOR]

SUMMARY Microfinance institutions' (MFIs') peculiar lending methodology is characterized by an unchallenged decision-making predominance from the part of loan officers. Indeed, the latter are in charge of providing a great deal of diagnostic information regarding the entrepreneur's psychological traits likely to help them run a business. This paper constitutes an initial attempt towards exploring the role of borrowers' psychological traits in predicting future default occurrences. It builds on a nonparametric credit scoring model, based on a decision tree, including borrowers' quantitative behavioural traits as input for the final scoring model. On applying data collected from a Tunisian microfinance bank, the major depicted result lies in the fact that borrowers' psychological traits constitute a major information source in predicting their creditworthiness. Actually, the variables deployed have helped reduce the proportion of bad loans classified as good loans by 3.125%, which leads to a decrease in MFIs' losses by 4.8%. In addition, the results indicate that the scoring model based on a classification and regression tree (CART) outperforms the classic techniques. Actually, implementing this CART model might well help MFIs reduce misclassification costs by 6.8% and 13.5% in comparison with the discriminant analysis and logistic regression models respectively. Our conceived model, we consider, would be of great practical implication for microfinance and may provide a means for securing competitive advantage over other MFIs that fail to implement such a methodology. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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]

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]

SUMMARY Credit scoring has been regarded as a core appraisal tool of different institutions during the last few decades and has been widely investigated in different areas, such as finance and accounting. Different scoring techniques are being used in areas of classification and prediction, where statistical techniques have conventionally been used. Both sophisticated and traditional techniques, as well as performance evaluation criteria, are investigated in the literature. The principal aim of this paper, in general, is to carry out a comprehensive review of 214 articles/books/theses that involve credit scoring applications in various areas but in particular primarily in finance and banking. This paper also aims to investigate how credit scoring has developed in importance and to identify the key determinants in the construction of a scoring model, by means of a widespread review of different statistical techniques and performance evaluation criteria. Our review of literature revealed that there is no overall best statistical technique used in building scoring models and the best technique for all circumstances does not yet exist. Also, the applications of the scoring methodologies have been widely extended to include different areas, and this subsequently can help decision makers, particularly in banking, to predict their clients' behaviour. Finally, this paper also suggests a number of directions for future research. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Bankruptcy prediction and credit scoring are the two important problems facing financial decision support. The multilayer perceptron (MLP) network has shown its applicability to these problems and its performance is usually superior to those of other traditional statistical models. Support vector machines (SVMs) are the core machine learning techniques and have been used to compare with MLP as the benchmark. However, the performance of SVMs is not fully understood in the literature because an insufficient number of data sets is considered and different kernel functions are used to train the SVMs. In this paper, four public data sets are used. In particular, three different sizes of training and testing data in each of the four data sets are considered (i.e. 3:7, 1:1 and 7:3) in order to examine and fully understand the performance of SVMs. For SVM model construction, the linear, radial basis function and polynomial kernel functions are used to construct the SVMs. Using MLP as the benchmark, the SVM classifier only performs better in one of the four data sets. On the other hand, the prediction results of the MLP and SVM classifiers are not significantly different for the three different sizes of training and testing data. [ABSTRACT FROM AUTHOR]

With the liberalization of the financial service sector mandated by China's access to the WTO, China's credit card market has received a great deal of attention from global financial institutions. This paper examines the enormous growth opportunities and key barriers facing the development of the credit card industry in China, and discusses the importance and tools of consumer credit risk management. In the process of rapid expansion of China's consumer credit card industry, credit risk management should be treated as a top priority to avoid a pile up of bad debt in credit card receivables. This requires the development of an updated and comprehensive national consumer credit database and the use of credit risk modeling and scoring in predicting consumer behavior. As structured finance develops in China, the securitization of credit card receivables into asset-backed securities might also serve as an alternative to traditional credit risk management. [ABSTRACT FROM AUTHOR]

In recent decades many highly sophisticated methods have been developed for supervised classification. These developments involve complex models requiring complicated iterative parameter estimation schemes, and can achieve unprecedented performance in terms of misclassification rate. However, in focusing efforts on the single performance criterion of misclassification rate, researchers have abstracted the problem beyond the bounds of practical usefulness, to the extent that the supposed performance improvements are irrelevant in comparison with other factors influencing performance. Examples of such factors are given. An illustration is provided of a new method which, for the particular problem of credit scoring, improves a relevant measure of classification performance while maintaining interpretability. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A bank employs logistic regression with state-dependent sample selection to identify loans that may go wrong. The data consist of some 20 000 loans for which a number of conventional accounting ratios of the debtor firm are known; after two years just over 600 have gone wrong. Inspection shows that the state-dependent sampling technique does not work because the data do not satisfy the standard logit model. Several variants on this model are considered, and it is found that a bounded logit with a ceiling of (far) less than 1 fits the data better. When it comes to their performance in an independent data-set, however, the differences between the various methods of analysis are negligible. [ABSTRACT FROM AUTHOR]

This paper estimates that credit scoring is associated with about a $3,900 increase in small business lending per sample banking organization, per low- and moderate-income (LMI) area served, and this effect is roughly equivalent to that estimated for higher-income areas. For our sample, this corresponds to a $536 million increase in small business credit in LMI areas in 1997 than otherwise would have been the case. This effect appears to be driven by increased out-of-market lending by banking organizations, as in-market lending generally declines. Overall, it does not appear that credit scoring has a disparate impact on LMI areas. [ABSTRACT FROM AUTHOR]

Credit scoring is the term used to describe methods utilized for classifying applicants for credit into classes of risk. This paper evaluates two induction approaches, rough sets and decision trees, as techniques for classifying credit (business) applicants. Inductive learning methods, like rough sets and decision trees, have a better knowledge representational structure than neural networks or statistical procedures because they can be used to derive production rules. If decision trees have already been used for credit granting, the rough sets approach is rarely utilized in this domain. In this paper, we use production rules obtained on a sample of 1102 business loans in order to compare the classification abilities of the two techniques. We show that decision trees obtain better results with 87.5% of good classifications with a pruned tree, against 76.7% for rough sets. However, decision trees make more type–II errors than rough sets, but fewer type–I errors. [ABSTRACT FROM AUTHOR]

The purpose of this article is to show that Bayesian methods, coupled with Markov chain Monte Carlo computational techniques, can be successfully employed in the analysis of highly dimensional complex datasets, such as those occurring in data mining applications. Our methodology employs conditional independence graphs to localize model specification and inferences, thus allowing a considerable gain in flexibility of modelling and efficiency of the computations. Copyright © 2001 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The credit card industry is awakening to the fact that the vast amounts of data it has on its customers should be treated as a valuable asset and that extracting knowledge from this data is crucial if one wants to optimize ones credit book. The company I work for—Barclaycard has over 7 million customers and over 9 million credit cards in circulation and deals with over 300 million credit card transactions per annum—a tremen dous amount of data. This paper is intended to provide a flavour of some of the statistical work that is required to take advantage of this asset. The suggestions are only the tip of the iceberg. Credit cards are a complex business. A factor that makes up this complexity is that credit card customers use their cards for a number of non-credit reasons, namely: payment convenience, smoothing of finances, paying regular bills, emergencies and spontaneous spending. Moreover, managing customers implies balancing revenue, churn and bad debt issues, and the explosion of competetion in recent years is making it more difficult to attract and retain profitable, low-risk customers. In the paper I will examine the traditional statistical methodology behind the issue of credit, along with some of the problems that have been solved and some that are still outstanding. The paper then provides an overview of the new Customer Value Management paradigm, also known as Customer Relationship Management (CRM), highlighting the new challenges that have emanated from this and the implications for statisticians. Copyright © 2001 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Our paper challenges the conventional wisdom that the flat maximum inflicts the 'curse of insensitivity' on the modelling of judgement and decision processes. In particular, we argue that this widely demonstrated failure on the part of conventional statistical methods to differentiate between competing models has a useful role to play in the development of accessible and economical applied systems, since it allows a low cost choice between systems which vary in their cognitive demands on the user and in their ease of development and implementation. To illustrate our thesis, we take two recent applications of linear scoring models used for credit. scoring and for the prediction of sudden infant death. The paper discusses the nature and determinants of the flat maximum as well as its role in applied cognition. Other sections mention certain unanswered questions about the development of linear, scoring models and briefly describe competing formulations for prediction. [ABSTRACT FROM AUTHOR]

The constant need to assess loans makes risk evaluation a very important problem for the banking sector. A crucial function of the banks is to fund households and companies from various industries in the economy. Risk is taken by the banks as soon as a loan is given to an entity. Currently, there are sector‐and‐experience based methods of analysis employed by the banks to estimate the risks to be taken. For the credit process, there exist a large number of studies in the literature on scoring individual clients but there are very few studies on scoring small and medium enterprises (SME) commercial corporate customers. In this study, we propose an objective risk measurement method for the lending process of SME commercial corporate customers and performed classification task of data mining by collecting current customer data on credit evaluation process of a bank. For this purpose, we first create a risk measure by looking into the risks identified for existing customers by the analysts of a bank. These scores are used as target variable in the classification process. Then, we extract rules for estimating these scores using Weka software. We used six different algorithms, and compared results in terms of test accuracy, the number of rules, recall, precision and Kappa statistic. We obtained high accuracy rates on real life data by our approach. As a result, we showed that an objective evaluation strategy is possible to use in the lending process for SME commercial corporate customers in the banking system using data mining. This article is categorized under: Application Areas > Business and IndustryTechnologies > ClassificationApplication Areas > Industry Specific Applications Classification in credit scoring processes of SME‐commercial‐corporate customers. [ABSTRACT FROM AUTHOR]

Although microfinance organizations play an important role in developing economies, decision support models for microfinance credit scoring have not been sufficiently covered in the literature, particularly for microcredit enterprises. The aim of this paper is to create a three‐class model that can improve credit risk assessment in the microfinance context. The real‐world microcredit data set used in this study includes data from retail, micro, and small enterprises. To the best of the authors' knowledge, existing research on microfinance credit scoring has been limited to regression and genetic algorithms, thereby excluding novel machine learning algorithms. The aim of this research is to close this gap. The proposed models predict default events by analysing different ensemble classification methods that empower the effects of the synthetic minority oversampling technique (SMOTE) used in the preprocessing of the imbalanced microcredit data set. Initial results have shown improvement in the prediction results for certain classes when the oversampling technique with homogeneous and heterogeneous ensemble classifier methods was applied. A prediction improvement for all classes was achieved via application of SMOTE and the Consolidated Trees Construction algorithm together with Rotation Forest. To obtain a complete view of all aspects, an additional set of metrics is used in the evaluation of performance. [ABSTRACT FROM AUTHOR]