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Credit scoring models are critical for financial institutions to assess borrower risk and maintain profitability. Although machine learning models have improved credit scoring accuracy, imbalanced class distributions remain a major challenge. The widely used Synthetic Minority Oversampling TEchnique (SMOTE) struggles with high-dimensional, non-linear data and may introduce noise through class overlap. Generative Adversarial Networks (GANs) have emerged as an alternative, offering the ability to model complex data distributions. Conditional Wasserstein GANs (cWGANs) have shown promise in handling both numerical and categorical features in credit scoring datasets. However, research on extracting latent features from non-linear data and improving model explainability remains limited. To address these challenges, this paper introduces the Non-parametric Oversampling Technique for Explainable credit scoring (NOTE). The NOTE offers a unified approach that integrates a Non-parametric Stacked Autoencoder (NSA) for capturing non-linear latent features, cWGAN for oversampling the minority class, and a classification process designed to enhance explainability. The experimental results demonstrate that NOTE surpasses state-of-the-art oversampling techniques by improving classification accuracy and model stability, particularly in non-linear and imbalanced credit scoring datasets, while also enhancing the explainability of the results. [ABSTRACT FROM AUTHOR]

Digital footprints provide crucial insights into individuals' behaviors and preferences. Their role in credit scoring is becoming increasingly significant. Therefore, it is crucial to combine digital footprint data with traditional data for personal credit scoring. This paper proposes a novel credit-scoring model. First, lasso-logistic regression is used to select key variables that significantly impact the prediction results. Then, digital footprint variables are categorized based on business understanding, and candidate models are constructed from various combinations of these groups. Finally, the optimal weight is selected by minimizing the Kullback–Leibler loss. Subsequently, the final prediction model is constructed. Empirical analysis validates the advantages and feasibility of the proposed method in variable selection, coefficient estimation, and predictive accuracy. Furthermore, the model-averaging method provides the weights for each candidate model, providing managerial implications to identify beneficial variable combinations for credit scoring. [ABSTRACT FROM AUTHOR]

This paper aims to compare the performance of four credit scoring models, namely logistic regression (LR), artificial neural network (ANN), fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) in predicting default probability. We use a sample of 1045 consumer credits (after oversampling the initial sample of 660 customers) granted by a Tunisian Islamic bank. The six explanatory variables retained to predict the probability of default are: residual wage, age, job tenure, profession, financing type and region of residence. Our findings reveal that ANFIS and LR have the highest discriminating power (AUC = 0.9). Regarding the type I error (false-positive) and the type II (false-negative) error, it has been proved that ANFIS has the lowest misclassification costs (MC = 0.15). The outperformance of the ANFIS comes from combining the advantages of neural networks with a fuzzy inference system. Thus, our results suggest that the ANFIS seems to be the most efficient and transparent technique for predicting credit risk in Islamic banks. Unlike ANN, the ANFIS allows bankers to justify the reasons behind the rejection of credit applications. [ABSTRACT FROM AUTHOR]

Psychometric-based credit scores measure important personality traits that are characteristic of good borrowers' behaviors. While such data can potentially improve credit models for underbanked consumers, the utility of psychometric data in consumer lending is still largely understudied. The present study contributes to the literature in this respect, as it is one of the first studies to evaluate the efficacy of psychometric-based credit scores for predicting future loan defaults among underbanked consumers. The results from two culturally diverse samples of loan applicants (Sub-Saharan Africa, n = 1113; Western Europe, n = 1033) found that psychometric scores correlated significantly with future loan defaults (Gini = 0.28–0.31) and were incrementally valid above and beyond the banks' own credit scorecards. These results highlight the theoretical basis for personality in financial behaviors, as well as the practical utility that psychometric scores can have for credit decisioning in general and the facilitation of financial inclusion for underbanked consumer groups in particular. [ABSTRACT FROM AUTHOR]

Credit scoring models are crucial tools for lenders to assess credit risks. Researchers from academia and the financial industry have shown intense interest in these models. However, real credit datasets often have high dimensionality and class imbalance, making it challenging to develop accurate and effective credit scoring models. To address these challenges, a new approach called the Multiple-Optimized Ensemble Learning (MOEL) method has been proposed. In MOEL, a technique called Multiple Diverse Optimized Subsets (MDOS) generates multiple diverse optimized subsets from various weighted random forests. From each subset, more effective and relevant features are selected. Then, a new evaluation measure is applied to each subset to determine the more optimized subsets. These subsets are applied to a novel Mahalanobis-based oversampling (MOS) technique to provide balanced subsets for the base classifier, which lessens the detrimental effects of imbalanced datasets. Finally, a stacking-based ensemble method is applied to the balanced subsets for integration of the base models. The proposed model was evaluated against six high-dimensional imbalanced credit scoring datasets, and it outperformed state-of-the-art methods, exhibiting a mean rank of 1.5 and 1.333 in terms of F1_score and G-mean, respectively. [ABSTRACT FROM AUTHOR]

Credit risk, stemming from the failure of a contractual party, is a significant variable in financial institutions. Assessing credit risk involves evaluating the creditworthiness of individuals, businesses, or entities to predict the likelihood of defaulting on financial obligations. While financial institutions categorize consumers based on creditworthiness, there is no universally defined set of attributes or indices. This research proposes Range control-based class imbalance and Optimized Granular Elastic Net regression (ROGENet) for feature selection in credit risk assessment. The dataset exhibits severe class imbalance, addressed using Range-Controlled Synthetic Minority Oversampling TEchnique (RCSMOTE). The balanced data undergo Granular Elastic Net regression with hybrid Gazelle sand cat Swarm Optimization (GENGSO) for feature selection. Elastic net, ensuring sparsity and grouping for correlated features, proves beneficial for assessing credit risk. ROGENet provides a detailed perspective on credit risk evaluation, surpassing conventional methods. The oversampling feature selection enhances the accuracy of minority class by 99.4, 99, 98.6 and 97.3%, respectively. [ABSTRACT FROM AUTHOR]

Abstract Credit scoring models are critical for financial institutions to assess borrower risk and maintain profitability. Although machine learning models have improved credit scoring accuracy, imbalanced class distributions remain a major challenge. The widely used Synthetic Minority Oversampling TEchnique (SMOTE) struggles with high-dimensional, non-linear data and may introduce noise through class overlap. Generative Adversarial Networks (GANs) have emerged as an alternative, offering the ability to model complex data distributions. Conditional Wasserstein GANs (cWGANs) have shown promise in handling both numerical and categorical features in credit scoring datasets. However, research on extracting latent features from non-linear data and improving model explainability remains limited. To address these challenges, this paper introduces the Non-parametric Oversampling Technique for Explainable credit scoring (NOTE). The NOTE offers a unified approach that integrates a Non-parametric Stacked Autoencoder (NSA) for capturing non-linear latent features, cWGAN for oversampling the minority class, and a classification process designed to enhance explainability. The experimental results demonstrate that NOTE surpasses state-of-the-art oversampling techniques by improving classification accuracy and model stability, particularly in non-linear and imbalanced credit scoring datasets, while also enhancing the explainability of the results.

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]

L’articolo analizza le sentenze della Corte di giustizia dell’Unione europea del 7 dicembre 2023 (cause riunite C-26/22 e C-64/22 e C-634/21). Le decisioni offrono infatti l’opportunità di riflettere su due temi principali: l’ampiezza del sindacato giurisdizionale esercitato su una decisione di reclamo adottata da un’autorità di controllo e la liceità della raccolta e del trattamento, anche automatizzato, dei dati personali. Le forme di tutela connesse al trattamento dei dati personali, sviluppandosi in più livelli e con modalità differenti, delineano nel loro complesso un sistema volto a garantire la massima protezione dei dati, che, anche in un’ottica di bilanciamento degli interessi, appare – almeno alla luce delle sentenze in esame – prevalere sugli interessi commerciali connessi all’utilizzo degli stessi.

This article examines how racial capitalism intersects with platform capitalism through the rise of rental platforms and corporate landlords in the post-apartheid housing market. Combining 18 months of fieldwork in Cape Town with the spatial analysis of sales and longitudinal census data, I demonstrate how rental platforms enabled the consolidation of the private rental sector and the emergence of corporate landlords through the classification of tenants centered upon credit scoring. To automate tenant screening solutions, rental platforms leveraged and extended the information dragnet knitted by credit bureaus. This dragnet of unprecedented depth and volume is built upon the infrastructures and devices that enabled the for-profit, racial classification of people, housing and neighborhoods during colonialism and apartheid, notably ID numbers. In the context of racialized indebtedness and housing inequalities engineered by racial property regimes, the use of platforms to sort the "good" from the "bad" tenant and manage rental portfolios shifts mechanisms of segregation and reproduces racialized patterns of capital accumulation across the post-apartheid city. The article argues that rental platforms extend the extractive logic of racial capitalism through two joint rentier mechanisms: the transformation of rental housing into a new asset class; the extraction and assetization of rental data. [ABSTRACT FROM AUTHOR]

The development of financial technology (Fintech) in emerging economies such as Indonesia has been rapid in the last few years, opening a great potential for loan businesses, from venture capital to micro and personal loans. To survive in such competitive markets, new companies need a robust credit-scoring model. However, building a reliable model requires large stable data. The challenge is that datasets are often small, covering only a few months (short-period datasets). Therefore, this study proposes a modified binning method, namely changing a variable's values into two groups with the smallest distribution differences possible. Modified binning can maintain data trends to avoid future shifting. The simulation was conducted using a real dataset from Indonesian Fintech, comprising 44,917 borrower-level observations with 396 variables. To match the actual conditions, the first three months of data were allocated for modeling and the remaining for testing. Implementing modified binning and logistics regression to testing data results in a more stable score band than standard binning. Compared with other classifier methods, the proposed method obtained the best AUC results on the testing data (0.73). In addition, the proposed method is highly applicable as it can provide a straightforward explanation to upper management or regulators. It is practical to use in real-case financial technology with short-period problems. [ABSTRACT FROM AUTHOR]

The AUC, i.e. the area under the receiver operating characteristic (ROC) curve, or its scaled version, the Gini coefficient, are the standard measures of the discriminatory power of credit scoring. Using binormal ROC curve models, we show how the shape of the curves affects the economic benefits of using scoring models with the same AUC. Based on the results, we propose that the shape parameter of the fitted ROC curve is reported alongside its AUC/Gini whenever the quality of a scorecard is discussed. [ABSTRACT FROM AUTHOR]

Background: An improved understanding of the reasoning behind model decisions can enhance the use of machine learning (ML) models in credit scoring. Although ML models are widely regarded as highly accurate, the use of these models in settings that require explanation of model decisions has been limited because of the lack of transparency. Especially in the banking sector, model risk frameworks frequently require a significant level of model interpretability. Aim: The aim of the article is to evaluate traditional model risk frameworks to determine their appropriateness when validating ML models in credit scoring and enhance the use of ML models in regulated environments by introducing a ML interpretability technique in model validation frameworks. Setting: The research considers model risk frameworks and regulatory guidelines from various international institutions. Method: The research is qualitative in nature and shows how through integrating traditional and non-traditional model risk frameworks, the practitioner can leverage trusted techniques and extend traditional frameworks to address key principles such as transparency. Results: The article proposes a model risk framework that utilises Shapley values to improve the explainability of ML models in credit scoring. Practical validation tests are proposed to enable transparency of model input variables in the validation process of ML models. Conclusion: Our results show that one can formulate a comprehensive validation process by integrating traditional and non-traditional frameworks. Contribution: This study contributes to existing model risk literature by proposing a new model validation framework that utilises Shapley values to explain ML model predictions in credit scoring.

The basic concept of credit scoring is to assess an individual`s payment ability as well as the specific individual`s credit default risk, hence determining an individual`s creditworthiness. Based on the credit score, financial institutions, insurance companies, telecommunication companies and other businesses decide whether consumers are eligible for a mortgage, credit card, auto loan, and other credit products. However, in many countries, potential tenants and insurance applicants also use credit scores extensively for screening. Accordingly, Credit Bureaus (CB) or Consumer Reporting Agencies (CRA) exert an essential gatekeeper function for important economic areas of consumers’ everyday life. However, when examining CBs globally, there are considerable differences in the use of data to calculate credit scores. Interestingly, the influence of CBs on credit rating receives little to no attention in academic research. This is particularly evident in the absence of a framework for classifying Credit Bureaus. Therefore, 24 traditional and non-traditional Credit Bureaus operating in 17 different countries are analyzed. First, the study identifies the different data types underlying credit reports and credit scores. Second, CBs are classified and clustered according to the type of information used for credit scoring. Furthermore, promising areas of research, in particular the ethical conflict between data protection and economic participation are highlighted.

Credit scoring is a useful tool for assessing the capability of customers repayments. The purpose of this paper is to compare the predictive abilities of six credit scoring models: Linear Discriminant Analysis (LDA), Random Forests (RF), Logistic Regression (LR), Decision Trees (DT), Support Vector Machines (SVM) and Deep Neural Network (DNN). To compare these models, an empirical study was conducted using a sample of 688 observations and twelve variables. The performance of this model was analyzed using three measures: Accuracy rate, F1 score, and Area Under Curve (AUC). In summary, machine learning techniques exhibited greater accuracy in predicting loan defaults compared to other traditional statistical models.

Accurate and condition-based validation will increase the value of banks, and if this is not done well, banks will face the risk of bankruptcy. The purpose of this research is to evaluate the usefulness of financial reporting information in modeling the credit rating of bank customers in listed companies. The methods used to validate the model in listed companies were grounded theory, DEMATEL, and regression. In the first stage, four main axes including (1) financial criteria, (2) non-financial criteria, (3) corporate governance criteria, and (4) market criteria for credit rating of customers were clarified by asking the opinion of 10 experts, were explained for the credit rating of customers. Three hypotheses were identified in the DEMATEL phase: the first hypothesis: There is a significant positive relationship between financial factors and bank facilities received by companies admitted to the Tehran Stock Exchange. Second hypothesis: There is a significant positive relationship between the company's market information and the bank facilities received by the companies admitted to the Tehran Stock Exchange. Third hypothesis: There is a significant positive relationship between non-financial factors and bank facilities received by companies listed on the Tehran Stock Exchange. The findings of the regression analysis did not reject the above hypotheses in 64 companies during the 5 years from 1396 to 1400. Banks should pay attention to the characteristics of companies in the existing conditions when providing facilities

The greatest source of failure in portfolio analytics is not individual models that perform poorly, but rather an inability to integrate models quantitatively across management functions. The separable components of age–period–cohort models provide a framework for integrated credit risk modeling across an organization. Using a panel data structure, credit risk scores can be integrated with an APC framework using either logistic regression or machine learning. Such APC scores for default, payoff, and other key rates fit naturally into forward-looking cash flow estimates. Given an economic scenario, every applicant at the time of origination can be assigned profit and profit volatility estimates so that underwriting can truly be account-level. This process optimizes the most fallible part of underwriting, which is setting cutoff scores and assigning loan pricing and terms. This article provides a summary of applications of APC models across portfolio management roles, with a description of how to create the models to be directly integrated. As a consequence, cash flow calculations are available for each account, and cutoff scores can be set directly from portfolio financial targets. [ABSTRACT FROM AUTHOR]

The k-nearest neighbors (k-NN) algorithm, a fundamental machine learning technique, typically employs the Euclidean distance metric for proximity-based data classification. This research focuses on the feature importance infused k-NN model, an advanced form of k-NN. Diverging from traditional algorithm uniform weighted Euclidean distance, feature importance infused k-NN introduces a specialized distance weighting system. This system emphasizes critical features while reducing the impact of lesser ones, thereby enhancing classification accuracy. Empirical studies indicate a 1.7% average accuracy improvement with proposed model over conventional model, attributed to its effective handling of feature importance in distance calculations. Notably, a significant positive correlation was observed between the disparity in feature importance levels and the model's accuracy, highlighting proposed model's proficiency in handling variables with limited explanatory power. These findings suggest proposed model's potential and open avenues for future research, particularly in refining its feature importance weighting mechanism, broadening dataset applicability, and examining its compatibility with different distance metrics. [ABSTRACT FROM AUTHOR]

What variables indicate whether a small or medium enterprise (SME) applying for financing from a development bank is a good loan candidate? Structural equation modeling applied to a set of variables can provide the necessary insights. This paper analyzes 407 SMEs that applied for development loans in Pichincha Province, Ecuador. Rather than specific economic/financial quantitative variables often used in credit scoring models, we employed qualitative variables to study creditworthiness. The structural equation methodology can verify whether a client is creditworthy based on company management, product characteristics, and contextual market aspects. Another key contribution is the finding that an entrepreneur's personal/professional traits are the primary determinant for granting this type of loan. The results have theoretical and practical implications that could enhance the limited empirical research in this field to date. [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]

Using data between 2009 and 2020, we provide a detailed description of the borrowers within the Enterprise Finance Guarantee (EFG) loan portfolio, analyse time to default and how it differs across lender types. For limited companies, we match additional financial and non-financial data from public and proprietary databases and profile the characteristics of EFG companies within the population of limited companies. Employing hazard models we find loans granted to unincorporated businesses by the medium-sized financial institutions are associated with a much lower hazard than those provided by smaller local lending institutions and not-for-profit agencies. Moreover, we find some evidence that loans to limited companies, issued by the big UK banking groups, have a significantly lower default than those from medium-sized financial institutions. Large banks screen out high-risk firms. We argue that smaller lenders are able to price the risks rejected by the larger banks, using a wider range of credit information. JEL codes: G01, G21, L52, D25 [ABSTRACT FROM AUTHOR]

Most credit scoring datasets suffer from imbalanced class distribution, with most borrowers repaying their loans and only a few defaulting. This can affect the accuracy of traditional machine learning algorithms. To address the imbalance issue, we propose a new approach that combines borderline conditional generative adversarial networks, ensemble learning, and multi-objective optimization techniques. Our method involves employing an autoencoder to extract relevant features from the dataset, followed by generative adversarial networks (GAN)-based oversampling method to balance the dataset. We utilize a multi-objective evolutionary algorithm to optimize the selection of balanced data subsets, which are then used to train multiple classifiers. The predictions from these classifiers are combined using a stacking method, enhancing overall accuracy. Our experiments indicate that our proposed model MOCGAN, effectively handles class imbalance, achieving a mean AUC of 90.12% and a mean F1-score of 88.25%, thereby significantly enhancing the reliability of credit scoring models.

EXplainable Artificial Intelligence (XAI) aims to reveal the reasons behind predictions from non-transparent classifiers. Explanations of automated decisions are important in critical domains such as finance, legal, and health. As a result, researchers and practitioners in recent years have actively worked on developing techniques that explain decisions from machine learning algorithms. For instance, an explanation technique called counterfactual explanation has recently been gaining traction in XAI. The interest in counterfactual explanations stems from the ability of the explanations to reveal what could have been different to achieve a desired outcome, as opposed to only highlighting important features. For instance, if a customer’s loan application is denied by the bank, a counterfactual will indicate the changes required for the customer to qualify for the loan in the future. For a counterfactual to be considered effective, several counterfactual properties must hold. This paper proposes a novel optimization formulation designed to generate counterfactual explanations that possess multiple properties concurrently. The efficacy of the proposed method is assessed on a publicly available credit dataset. The results showed a trade-off between validity and sparsity, which are both parts of a suite of counterfactual properties. Furthermore, the results showed that our proposed approach compromises validity to some degree but strikes a good balance between validity and sparsity.

Practical Byzantine Fault Tolerance (PBFT), a classic consensus algorithm in blockchain technology, is extensively used in consortium blockchain networks. However, it is challenged by issues such as low consensus efficiency, poor scalability, inability to guarantee throughput with large-scale node access, and complex communication processes. To solve these problems, this paper proposes an improved PBFT consensus mechanism based on credit scoring and aggregated signatures, i.e., the CA-PBFT algorithm. First, the algorithm designs the node credit scoring mechanism, adds the coordination node in the original algorithm model, stipulates the node state and functional limitations, and realizes the dynamic joining and exiting of the nodes, to solve the low efficiency of the PBFT algorithm during the consensus process and the problem of not supporting the dynamic joining and exiting of the nodes; at the same time, the signature scheme based on the BLS aggregated signature is designed, which reduces the length of the signature and simplifies the signing process, to solve the problem of the node’s signature taking up too much space during the consensus process, which affects the efficiency of the signature validation as well as the efficiency of the signature construction. Experimental results show that this consensus mechanism enables an efficient, secure, and scalable consensus process with low resource and computational costs.

This article addresses the challenges associated with determining the borrower's creditworthiness. The authors define the borrower's creditworthiness as the ability to timely and fully meet debt obligations to the lender. This determination is based on an evaluation of the borrower's financial condition, forecast of business development, and other factors influencing the ability to repay the loan. The authors identify internal and external factors affecting the creditworthiness of borrowers in Ukraine. Internal factors include the economic situation, regulatory policies, credit history, credit rating, and a lack of financial literacy. External factors encompass the global economic situation and the political climate. The article also discusses challenges in determining the borrower's creditworthiness during times of war. The authors note that creditworthiness can sharply decrease during such periods due to factors such as reduced income, increased costs, decreased demand for goods and services, and an unstable economic situation. To address these challenges during wartime, the authors propose the following recommendations: • Widespread use of artificial intelligence: AI enables the consideration of more factors affecting the borrower's creditworthiness and the adaptation of credit scoring models to changes in the economic situation. • Expansion of data on the borrower's financial condition: Financial institutions should utilize more data sources, including unstructured data such as social media and behavioral data, to assess the borrower's financial condition. • Development of new methods for assessing creditworthiness: Financial institutions should create new methods that account for the specific conditions of wartime. • Establishment of cooperation between financial institutions and government agencies: This collaboration will provide additional data on the financial condition of borrowers, offering financial institutions a more comprehensive view. • Promotion of financial literacy among the population: Improving financial literacy will empower borrowers to better understand their financial obligations and make more informed lending decisions. Implementing these recommendations will enable financial institutions to enhance the accuracy of creditworthiness assessments and, consequently, reduce credit risks during times of war.

Digital footprints provide crucial insights into individuals’ behaviors and preferences. Their role in credit scoring is becoming increasingly significant. Therefore, it is crucial to combine digital footprint data with traditional data for personal credit scoring. This paper proposes a novel credit-scoring model. First, lasso-logistic regression is used to select key variables that significantly impact the prediction results. Then, digital footprint variables are categorized based on business understanding, and candidate models are constructed from various combinations of these groups. Finally, the optimal weight is selected by minimizing the Kullback–Leibler loss. Subsequently, the final prediction model is constructed. Empirical analysis validates the advantages and feasibility of the proposed method in variable selection, coefficient estimation, and predictive accuracy. Furthermore, the model-averaging method provides the weights for each candidate model, providing managerial implications to identify beneficial variable combinations for credit scoring.

The decision made by machine learning is mostly based on historical data that is used to train them. It raises the awareness that discrimination in machine learning should be eliminated because it may contain societal bias. The financial industry uses credit scoring as a reference to reflect the customer risk profile. To achieve fairness in the model, this paper tries to: (1) assess bias and (2) improve fairness in machine learning models with three bias mitigation methodologies. This study depicts that there is a trade-off between improving fairness and preserving performance. Implementing post-processing methods, for example, Grid Search performs best. [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]

Mobile e-commerce has grown rapidly in the last decade because of the development of mobile network services, computing capabilities and big data's applications. Financial institutions have been undergoing fundamental transformation in credit risk areas, specifically to traditional credit policy, that is now inadequate for accurately evaluating an individual's credit risk profile in a timely manner. A big-scale dataset representing deep mobile usage of 450,722 anonymous mobile users with a 28-month loan history and mobile behavior of both iOS and Android is designed, can add value for credit scoring in terms of better accuracy and lower feature acquisition cost by introducing a cost-based quantum-inspired evolutionary algorithm (QIEA) feature selection method. The QIEA adopts quantum-based individual representation and quantum rotation gate operator to improve feature exploration capability of conventional genetic algorithm (GA). The expected feature yield fitness function introduced in QIEA able to identify cost-effective feature subsets. Experimental results show that quantum-based method achieves good predictive performances even with only 70–80% number of features selected by GAs, and hence achieve lower feature acquisition costs with budget constraints. Additionally, computational time can be reduced by 30–60% compared with GAs depending on different feature set sizes. [ABSTRACT FROM AUTHOR]

Credit scoring or predicting bankruptcy is among the most crucial techniques for identifying high-risk and low-risk credit situations. Accordingly, enhancing the accuracy of bankruptcy prediction methods decreases the risk of inappropriate financial decisions. Also, increasing the accuracy of credit scoring models brings significant benefits such as improved turnover, credit market growth, proper and efficient allocation of financial resources, and sustained improvement of the profits of banks, investors, funds, and governments. Various statistical classification methods have been developed in the literature with different features and characteristics for more accurate bankruptcy prediction. However, despite all appearance differences in statistical classification approaches, they all adhere to a common idea and concept in their training procedures. The basic operation logic in whole-developed statistical classification methods focuses on maximizing a continuous distance-based cost function to yield the highest performance. Despite it being a common and frequently used procedure for classification purposes, it is an unreasonable and inefficient manner to achieve maximum accuracy in a discrete classification field. In this paper, a new discrete direction-based Logistic Regression that is a common statistical classifier method for bankruptcy forecasting is proposed. In the proposed Logistic Regression, in contrast to all traditionally developed statistical classifiers, the compatibility of the cost function and the training procedure is considered. While it can be shown overall that the performance of the presented discrete direction-based classifier will not be inferior to its continuous counterpart, an evaluation of the suggested classifier is conducted to ascertain its superiority. For this purpose, three credit scoring datasets are considered to assess the classification rate of the presented classifier. Empirical outcomes demonstrate that, as pre-expected, in all cases, the model put forward can attain a superior performance compared to conventional alternatives. These findings clearly demonstrated the significant influence of the consistency between the cost function and the training process on the classification capability, a consideration absent in any of the traditional statistical classification procedures. Consequently, the presented Logistic Regression can be considered an efficient alternative for credit scoring purposes to achieve more accurate results. [ABSTRACT FROM AUTHOR]

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