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Credit scoring is a mathematical and statistical tool that aids financial institutions in deciding suitable candidates for the issuance of loans, based on the analysis of the borrower’s financial history. Distinct groups of borrowers have unique characteristics that must be identified and trained on to increase the accuracy of classification models for all credit borrowers that financial institutions serve. Numerous studies have shown that models based on diverse base-classifier models outperform other statistical and AI-based techniques for related classification problems. This paper proposes a novel multi-layer clustering and soft-voting-based ensemble classification model, aptly named Self Organizing Map Clustering with Metaheuristic Voting Ensembles (SCMVE) which uses a self-organizing map for clustering the data into distinct clusters with their unique characteristics and then trains a sailfish optimizer powered ensemble of SVM-KNN base classifiers for classification of each distinct identified cluster. We train and evaluate our model on the standard public credit scoring datasets—namely the German, Australian and Taiwan datasets and use multiple evaluation scores such as precision, F1 score, recall to compare the results of our model with other prominent works in the field. On evaluation, SCMVE shows outstanding results (95% accuracy on standard datasets) when compared with popular works in the field of credit scoring. [ABSTRACT FROM AUTHOR]

Credit risk scorecard models are utilized by lending institutions to optimize decisions on credit approvals. In recent years, ensemble learning has often been deployed to reduce misclassification costs in credit risk scorecards. In this paper, we compared the risk estimation of 26 widely used machine learning algorithms based on commonly used statistical metrics. The best-performing algorithms were then used for model selection in ensemble learning. For the first time, we proposed financial criteria that assess the impact of losses associated with both false positive and false negative predictions to identify optimal ensemble learning. The German Credit Dataset (GCD) is augmented with simulated financial information according to a hypothetical mortgage portfolio observed in UK, European and Australian banks to enable the assessment of losses arising from misclassification costs. The experimental results using the simulated GCD show that the best predictive individual algorithm with the accuracy of 0.87, Gini of 0.88 and Area Under the Receiver Operating Curve of 0.94 was the Generalized Additive Model (GAM). The ensemble learning method with the lowest misclassification cost was the combination of Random Forest (RF) and K-Nearest Neighbors (KNN), totaling USD 417 million in costs (USD 230 for default costs and USD 187 for opportunity costs) compared to the costs of the GAM (USD 487, USD 287 and USD 200). Implementing the proposed financial criteria has led to a significant USD 70 million reduction in misclassification costs derived from a small sample. Thus, the lending institutions' profit would considerably rise as the number of submitted credit applications for approval increases. [ABSTRACT FROM AUTHOR]

The emergence of credit has generated a wealth of data on consumer lending behavior. In recent years, financial institutions have also started to use such data to make informed lending decisions based on fine-grained customer data, but conventional risk assessment models are inadequate in meeting the risk control requirements of the financial industry. Therefore, this paper proposes a credit scoring ensemble model incorporating fuzzy clustering particle swarm optimization (PSO) algorithm to obtain better credit risk prediction capability. First, a weighted outlier detection method based on the Induced Ordered Weighted Average Operator is proposed to preprocess the data to reduce noisy data's misleading effect on model training. Then, an undersampling method combined with fuzzy clustering PSO is proposed to overcome the negative effect of category imbalance on model training by resampling the data. In addition, a hyperparameter optimization framework is introduced to adaptively adjust important parameters in the ensemble model considering the impact of parameter settings on the training performance of the model. Based on the evaluation metrics of F-score, AUC, and Kappa coefficient, an empirical analysis was conducted on five credit risk datasets. The results show that the proposed method outperforms the comparative model with an improvement of 10% to 50% in terms of F-score and AUC. The highest achieved F-score is 0.9488, and the maximum AUC is 0.9807, demonstrating the effectiveness of the proposed method. The kappa coefficient results indicate a high level of consistency in the predicted classification results of the model. [ABSTRACT FROM AUTHOR]

The paper discusses an approach to credit scoring of retail customers in the banking industry. The approach is based on the development of recurrent neural network models on customer's credit history data. The advantages and disadvantages of the approach are discussed. A couple of solutions aimed to serve as a baseline for the approach are implemented using traditional approaches to credit scoring and evaluated over two datasets from large European banks. [ABSTRACT FROM AUTHOR]

The subject matter of this paper is measuring the risk of lending to Small and Medium-sized Enterprises (SMEs) from the point of view of the existing banking regulations. The paper starts from the hypothesis that an increase in the transparency of the credit risk measurement process would enable the timely detection of problems and leave room for the actions necessary for the management of small and mediumsized enterprises, as well as all creditors, and generate an opportunity for SMEs to provide more favorable sources of financing. In the research study, the well-known Altman Z-Score model was used to assess the probability of default and rank a company. The results of the application of the Z-Score model indicate that, to a certain extent, they can detect the companies in which bankruptcy may occur in the two years following the assessment, on the one hand, but they cannot be considered as reliable for the assessment of the probability of the bankruptcy of SMEs in the Republic of Serbia, on the other. [ABSTRACT FROM AUTHOR]

Purpose – This paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs). Design/methodology/approach – The research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs). Findings – The findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs. Practical implications – The results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs. Originality/value – The economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs. [ABSTRACT FROM AUTHOR]

Banks and financial industries evaluate the creditworthiness of their customers through credit-scoring models before allocating loans to them. The performance of credit-scoring models significantly degrades due to class imbalance data, in which the class of defaulters is underrepresented as compared to that of non-defaulters, which is one of the major challenging tasks. In this paper, we propose a novel adaptive representative and density-based oversampling technique (A-RDBOTE) to deal with imbalanced credit-scoring datasets. First, the reverse k-nearest neighbor algorithm is applied to eliminate the noisy samples from the training set. Next, a semi-unsupervised clustering method is applied to cluster the minority instances. Then, from each sub-cluster, the representativeness of an instance is determined by considering its degree of similarity with respect to inter and intra-cluster. Subsequently, from each sub-cluster, the instances having high representative values are selected as anchor instances. Finally, artificial minority instances are generated around each anchor instance within the same sub-cluster. The experimental results showed that A-RDBOTE has achieved significantly better results than eight oversampling methods in terms of F1-score, AUC, and G-mean. [ABSTRACT FROM AUTHOR]

Credit scoring is a typical example of imbalanced classification, which poses a challenge to conventional machine learning algorithms and statistical classifiers when attempting to accurately predict outcomes for defaulting customers. In this paper, we propose a credit scoring classifier called Decision Tree Ensemble model (DTE). This model effectively addresses the challenge of imbalanced data and identifies significant features that influence the likelihood of credit status. An experiment demonstrates that DTE exhibits superior performance metrics in comparison to well-known based-tree ensemble classifiers such as Bagging, Random Forest, and AdaBoost, particularly when integrated with resampling techniques for handling imbalanced data. [ABSTRACT FROM AUTHOR]

It is crucial to correctly assess whether a potential borrower can repay the loan in the credit scoring model. The credit loan data has a serious data imbalance because the number of defaulters is far less than the nondefaulters. However, most current methods for dealing with data imbalance are designed to improve the classification performance of minority data, which will reduce the performance of majority data. For a financial institution, the economic loss caused by the decrease in the classification performance of nondefaulters (majority data) cannot be ignored. This paper proposes an ensemble learning method based on one-class and binary classification (EMOBC) for credit scoring. The purpose is to improve the classification accuracy of the minority class while mitigating the loss of classification accuracy of the majority class as much as possible. EMOBC uses undersampling for the majority class (nondefault samples in credit scoring) and perform binary-class learning on the balanced data to improve the classification accuracy of the minority. To alleviate the decline in classification performance of the majority class, EMOBC uses one-class and binary collaborative classification to train classifiers. The classification result is determined by the average of one-class and binary-class classifiers. The experimental results show that EMOBC has good comprehensive performance compared with the existing methods. [ABSTRACT FROM AUTHOR]

Credit risk is the most significant risk by impact for any bank and financial institution. Accurate credit risk assessment affects an organisation's balance sheet and income statement, since credit risk strategy determines pricing, and might even influence seemingly unrelated domains, e.g. marketing, and decision-making. This article aims at providing a systemic review of the most recent (2016--2021) articles, identifying trends in credit scoring using a fixed set of questions. The survey methodology and questionnaire align with previous similar research that analyses articles on credit scoring published in 1991--2015. We seek to compare our results with previous periods and highlight some of the recent best practices in the field that might be useful for future researchers. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a transformer into the field of credit scoring based on user online behavioral data and develop an end-to-end feature embedded transformer (FE-Transformer) credit scoring approach. The FE-Transformer neural network is composed of two parts: a wide part and a deep part. The deep part uses the transformer deep neural network. The output of the deep neural network and the feature data of the wide part are concentrated in a fusion layer. The experimental results show that the FE-Transformer deep learning model proposed in this paper outperforms the LR, XGBoost, LSTM, and AM-LSTM comparison methods in terms of area under the receiver operating characteristic curve (AUC) and the Kolmogorov–Smirnov (KS). This shows that the FE-Transformer deep learning model proposed in this paper can accurately predict user default risk. [ABSTRACT FROM AUTHOR]

This paper compares model development strategies based on different performance metrics. The study was conducted in the area of credit risk modeling with the usage of diverse metrics, including general-purpose Area Under the ROC curve (AUC), problem-dedicated Expected Maximum Profit (EMP) and the novel case-tailored Calculated Profit (CP). The metrics were used to optimize competitive credit risk scoring models based on two predictive algorithms that are widely used in the financial industry: Logistic Regression and extreme gradient boosting machine (XGBoost). A dataset provided by the American Fannie Mae agency was utilized to conduct the study. In addition to the baseline study, the paper also includes a stability analysis. In each case examined the proposed CP metric that allowed us to achieve the most profitable loan portfolio. [ABSTRACT FROM AUTHOR]

Credit scoring is widely used by financial institutions for default prediction, however, a significant portion of online credit loan customers have inadequate or unverifiable credit histories, making it difficult for financial institutions to make effective credit decisions. Since the widespread use of smartphones and the popularity of mobile applications, it is worth investigating whether mobile application usage behaviors (App behaviors) of customers can effectively predict online loan defaults. This paper proposes a combined algorithm of CNN and LightGBM, and establishes credit scoring models with App behaviors to evaluate the default risk of online credit loans based on logistic regression, LightGBM, CNN and the combined algorithm, respectively. The experimental results suggest that App behaviors have an obvious effect on the default prediction of customers applying for online credit loans, and the combined model outperforms the other models in terms of the area under the curve (AUC). Furthermore, integrated credit scoring models are developed by combining App behaviors with traditional scoring features. A comparison of the integrated models and the traditional scoring model indicates that the integrated models have achieved a significant improvement in classification performance and App behaviors can be a powerful complement to the traditional credit scoring model. [ABSTRACT FROM AUTHOR]

Credit is a part of external image of enterprises, and it directly affects interests of enterprises. Nowadays, most of researches on predictions of enterprises credit use a single algorithm model or optimize a single model to predict an enterprises credit score. The accuracy of each model is different, and the generalization ability is generally weak. In order to improve generalization ability of models and accuracy of prediction results, a parallel double-layer prediction model is proposed in this paper. The model is based on Stacking and Bagging methods, which can improve generalization ability with high accuracy. Through experiments, we compare three single algorithm models, four integrated learning models with other combination strategies and parallel double-layer prediction model. Average value of four evaluation indexes are increased by 4.2349%, 63.1464%, 34.11837%, 1.26104%, 15.7862%, 10.1457% and 25.6310% respectively. The results show that the parallel double-layer prediction model is accurate and feasible. [ABSTRACT FROM AUTHOR]

Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007–2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results. [ABSTRACT FROM AUTHOR]

Credit markets have experienced phenomenal growth in the last years, and there is no evidence to expect that this trend will invert in the next decade. Given that, virtually every segment of the industry is called to improve its skills in credit risk management with the threefold aim of managing concentration risk, meeting regulatory requirements and enhancing revenues. Clearly, for financial institutions, exposure to credit risk continues to be the leading source of problems and the challenging issue of managing credit risk is considered a crucial topic. This paper demonstrates the use of a stochastic DEA model for credit scoring which is one of the prevailing analytical technique to evaluate credit risk. Although the principles contained in this paper are most clearly applicable to the business of lending, they can be applied to all activities where credit risk is present. [ABSTRACT FROM PUBLISHER]

This paper shows how to improve the measurement of credit scoring by means of factor clustering. The improved measurement applies, in particular, to small and medium enterprises (SMEs) involved in P2P lending. The approach explores the concept of familiarity which relies on the notion that the more familiar/similar things are, the closer they are in terms of functionality or hidden characteristics (latent factors that drive the observed data). The approach uses singular value decomposition to extract the factors underlying the observed financial performance ratios of SMEs. We then cluster the factors using the standard k-mean algorithm. This enables us to segment the heterogeneous population into clusters with more homogeneous characteristics. The result shows that clusters with relatively fewer number of SMEs produce a more parsimonious and interpretable credit scoring model with better default predictive performance. [ABSTRACT FROM AUTHOR]

Credit scoring is an important process for peer-to-peer (P2P) lending companies as it determines whether loan applicants are likely to default. The aim of most credit scoring models is to minimize the classification error rate, which implies that all classification errors bear the same cost; however, in reality, there is a significant cost-sensitive problem in credit scoring methods. Therefore, in this paper, a new cost-sensitive logistic regression credit scoring model based on a multi-objective optimization approach is proposed that has two objectives in the cost-sensitive logistic regression process. The cost-sensitive logistic regression parameters are solved using a multiple objective particle swarm optimization (MOPSO) algorithm. In the empirical analysis, the proposed model was applied to the credit scoring of a Chinese famous P2P company, from which it was found that compared with other common credit scoring models, the proposed model was able to effectively reduce type II error rates and total classification error costs, and improve the AUC, the F1 values (reconciliation average of Recall and Precision), and the G-means. The proposed model was compared with other multi-objective optimization algorithms to further demonstrate that MOPSO is the best approach for cost-sensitive logistic regression credit scoring models. [ABSTRACT FROM AUTHOR]

Summary: Gender is prohibited from use in decision making in many countries. This does not necessarily benefit females in situations of automated algorithmic decisions, e.g. when a credit scoring model is used as a decision tool for loan granting. By analysing a unique proprietary data set on car loans from a European bank, the paper shows that gender as a variable in a credit scoring model is statistically significant. Its removal does not alter the predictive accuracy of the model, yet the proportions of accepted women/men depend on whether gender is included. The paper explores the association between predictors in the model with gender, to demonstrate the omitted variable bias and how other variables proxy for gender. It points to inconsistencies of the existing regulations in the context of automated decision making. [ABSTRACT FROM AUTHOR]

Application scorecards allow to assess the creditworthiness of loan applicants and decide on acceptance. The accuracy of scorecards is of crucial importance for minimizing bad debt loss and maximizing returns. In this paper, we extend upon prior benchmarking studies that experimentally compare the performance of classification techniques to discriminate between good and bad applications. We evaluate a range of cost-sensitive learning methods in terms of their ability to boost the profitability of scorecards. These methods allow to take into account the variable misclassification costs that are involved in rejecting good loan applications and accepting bad loan applications. An approach is proposed to estimate these misclassification costs, and various approaches to handle missing credit bureau scores are evaluated. The results of a case study involving a Romanian nonbanking financial institution (NBFI) indicate that cost-sensitive learning complements the existing state-of-the-art scorecard of the NBFI. The best performing cost-sensitive models are found to increase profitability across the three business channels, with a single-digit improvement for two of the channels and a double-digit increase for the other one. The result is partly explained by the default rate, which is higher for this latter channel and therefore offers greater potential for improving profitability. [ABSTRACT FROM AUTHOR]