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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]

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]

Background: The personal credit default discriminant measures the size of the credit default risk, which provides an essential decision-making basis for banks. Methods: This article constructs a three-stage default discriminant model based on the DF21. In the first stage, this article selects the feature combination. This article obtains the default prediction results by traversing the decision tree from 20 to 500 and the learning rate from 0.08 to 0.12 in XGBoost. Taking the lowest Type II error, the highest AUC and accuracy as the first, the second, and the third principles (TAA principle), respectively, this article infers the optimal parameter of decision tree and learning rate reversely and gets the feature importance. This article uses the forward selection method to determine the optimal feature combination according to the TAA principle. In the second stage, this article screens the base classifier for DF21. Considering the applicability of the classifier on different data sets, this article selects the classifier with the good classification performance as the base classifier on each data set. In the third stage, this article constructs the default discriminant model based on DF21. According to the idea that the combination of strong classifiers generates a stronger result, the four strong classifiers are used as the base classifier to improve the cascade structure of DF21. Results: Compared with the first stage, the Type II error (the proportion of the banks' principal loss) dropped by 4.41%, 5.98%, and 13.00% in the Japanese, Australian, and German, respectively, which proves the effectiveness of DF21. Conclusion: DF21 is significantly better than other classifiers and other scholars' models according to the TAA principle. [ABSTRACT FROM AUTHOR]

Credit scoring is an important topic in financial activities and bankruptcy prediction that has been extensively explored using deep neural network (DNN) methods. DNN-based credit scoring models rely heavily on a large amount of labeled data. The accuracy of DNN-based credit assessment models relies heavily on large amounts of labeled data. However, purely data-driven learning makes it difficult to encode human intent to guide the model to capture the desired patterns and leads to low transparency of the model. Therefore, the Probabilistic Soft Logic Posterior Regularization (PSLPR) framework is proposed for integrating prior knowledge of logic rule with neural network. First, the PSLPR framework calculates the rule satisfaction distance for each instance using a probabilistic soft logic formula. Second, the logic rules are integrated into the posterior distribution of the DNN output to form a logic output. Finally, a novel discrepancy loss which measures the difference between the real label and the logic output is used to incorporate logic rules into the parameters of the neural network. Extensive experiments were conducted on two datasets, the Australian credit dataset and the credit card customer default dataset. To evaluate the obtained systems, several performance metrics were used, including PCC, Recall, F1 and AUC. The results show that compared to the standard DNN model, the four evaluation metrics are increased by 7.14%, 14.29%, 8.15%, and 5.43% respectively on the Australian credit dataset. [ABSTRACT FROM AUTHOR]

Establishing precise credit scoring models to predict the potential default probability is vital for credit risk management. Machine learning models, especially ensemble learning approaches, have shown substantial progress in the performance improvement of credit scoring. The Bagging ensemble approach improves the credit scoring performance by optimizing the prediction variance while boosting ensemble algorithms reduce the prediction error by controlling the prediction bias. In this study, we propose a hybrid ensemble method that combines the advantages of the Bagging ensemble strategy and boosting ensemble optimization pattern, which can well balance the tradeoff of variance-bias optimization. The proposed method considers XGBoost as a base learner, which ensures the low-bias prediction. Moreover, the Bagging strategy is introduced to train the base learner to prevent over-fitting in the proposed method. Besides, the Bagging-boosting ensemble algorithm is further assembled in a cascading way, making the proposed new hybrid ensemble algorithm a good solution to balance the tradeoff of variance bias for credit scoring. Experimental results on the Australian, German, Japanese, and Taiwan datasets show the proposed Bagging-cascading boosted decision tree provides a more accurate credit scoring result. [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]

With the in-depth application of artificial intelligence technology in the financial field, credit scoring models constructed by machine learning algorithms have become mainstream. However, the high-dimensional and complex attribute features of the borrower pose challenges to the predictive competence of the model. This paper proposes a hybrid model with a novel feature selection method and an enhanced voting method for credit scoring. First, a novel feature selection combined method based on a genetic algorithm (FSCM-GA) is proposed, in which different classifiers are used to select features in combination with a genetic algorithm and combine them to generate an optimal feature subset. Furthermore, an enhanced voting method (EVM) is proposed to integrate classifiers, with the aim of improving the classification results in which the prediction probability values are close to the threshold. Finally, the predictive competence of the proposed model was validated on three public datasets and five evaluation metrics (accuracy, AUC, F-score, Log loss and Brier score). The comparative experiment and significance test results confirmed the good performance and robustness of the proposed model. [ABSTRACT FROM AUTHOR]

Credit scoring has become increasingly important for financial institutions. With the advancement of artificial intelligence, machine learning methods, especially ensemble learning methods, have become increasingly popular for credit scoring. However, the problems of imbalanced data distribution and underutilized feature information have not been well addressed sufficiently. To make the credit scoring model more adaptable to imbalanced datasets, the original model-based synthetic sampling method is extended herein to balance the datasets by generating appropriate minority samples to alleviate class overlap. To enable the credit scoring model to extract inherent correlations from features, a new bagging-based feature transformation method is proposed, which transforms features using a tree-based algorithm and selects features using the chi-square statistic. Furthermore, a two-layer ensemble method that combines the advantages of dynamic ensemble selection and stacking is proposed to improve the classification performance of the proposed multi-stage ensemble model. Finally, four standardized datasets are used to evaluate the performance of the proposed ensemble model using six evaluation metrics. The experimental results confirm that the proposed ensemble model is effective in improving classification performance and is superior to other benchmark models. [ABSTRACT FROM AUTHOR]

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models. [ABSTRACT FROM AUTHOR]

This study identifies credit risk sources, credit scoring index classification modes and modelling methods, and constructs a credit scoring system for small and micro businesses (SMBs) with soft information. Through the analysis and comparison of neural network models, this study demonstrates the superiority of the back-propagation neural network (BPNN) models for loan classification prediction. There are three contributions and innovations as follows. (1) Based on the actual demands and default characteristics of SMBs, this study adds the behavioural variables of loan managers to strengthen the role of soft information in credit scoring model. (2) It develops a hybrid analysis and comparison framework based on the BPNN model. It identifies that the BPNN model is more suitable for approving SMB loans, as it can precisely identify the second type of error. (3) Using the precious ledger data of SMB loans from a rural commercial bank in Jiangsu province, China, this study compares the prediction accuracy of the credit scoring model based on BPNN using two-level and five-level loan classifications. Further, it illustrates the applicability of the BPNN model. By connecting the practical operations of credit scoring and quantitative models, this paper supports commercial bank examination and approval work of SMB loans. [ABSTRACT FROM AUTHOR]

Emerging markets contain the vast majority of the world's population. Despite the enormous number of inhabitants, these markets still lack a proper finance infrastructure. One of the main difficulties felt by customers is the access to loans. This limitation arises from the fact that most customers usually lack a verifiable credit history. As such, traditional banks are unable to provide loans. This paper proposes credit scoring modeling based on non-traditional-data, acquired from smartphones, for loan classification processes. We use Logistic Regression (LR) and Support Vector Machine (SVM) models which are the top linear models in traditional banking. Then we compared the transformation of the training datasets creating boolean indicators against the categorization using Weight of Evidence (WoE). Our models surpassed the performance of the manual loan application selection process, improving the approval rate and decreasing the overdue rate. Compared to the baseline, the loans approved by meeting the criteria of the SVM model presented a decreased overdue rate. At the same time, using the score generated by a SVM model we were able to grant more loans. This paper shows that credit scoring can be useful in emerging markets. The non-traditional data can be used to build robust algorithms that can identify good borrowers as in traditional banking. [ABSTRACT FROM AUTHOR]

Machine learning techniques have been used successfully in several areas such as banking and finance. These techniques are used mainly for prediction, classification and partitioning data into different groups according to a certain common characteristic. In this work, we are interested in machine learning techniques for credit scoring and bankruptcy prediction in finance and banking. We evaluate and compare a range of machine learning techniques on several datasets issued from banks and financial institutions where the aim is to select the most appropriate methods suitable for each dataset. We use several metrics to evaluate the performance of the obtained models. The empirical studies are conducted on German, Australian, Japanese, Polish, Indian Qualitative Bankruptcy and Taiwan datasets. Also, we consider the huge "Give Me Some Credit dataset". The machine learning methods produce scores for applicants and companies and help a lot in the decision making. In other words, these methods permit us to distinguish between bad and good applicants or companies. The numerical study shows that there is no method able to consistently outperform the others on all the datasets. Also, there are significant differences between the studied methods on some datasets. For German and Give Me Some Credit datasets, the Bayes net method is able to produce good scores compared to the others studied methods. The LogitBoost method is competitive on both Polish and Australian datasets, while AdaBoost method is most appropriate for Japanese dataset. For Taiwan dataset, Random Forest method gives the best results compared to the other considered techniques. However, on Indian Qualitative Bankruptcy dataset, almost the methods are comparable due to the nature of this dataset. [ABSTRACT FROM AUTHOR]

Credit scoring is a procedure to estimate the risk related with credit products which is calculated using applicants' credentials and applicants' historical data. However, the data may have some redundant and irrelevant information and features, which lead to lower accuracy on the credit scoring model. So, by eliminating the redundant features can resolve the problem of credit scoring dataset. In this work, we have proposed a hybrid credit scoring model based on dimensionality reduction by Neighborhood Rough Set (NRS) algorithm and layered ensemble classification with weighted voting approach to improve the classification performance. For classifiers' raking, we have proposed a novel classifier ranking algorithm as an underlying model for representing ranks of the classifiers based on classifier accuracy. It is used on seven heterogeneous classifiers for finding the ranks of those classifiers. Further five best ranking classifiers are used as base classifier in layered ensemble framework. Results of the ensemble frameworks (Majority Voting (MV), Weighted Voting (WV), Layered Majority Voting (LMV), Layered Weighted Voting (LWV)) with all features and after feature reduction by various existing feature selection algorithms are compared in terms of accuracy, sensitivity, specificity and G-measure. Further, results of ensemble frameworks with NRS are also compared in terms of ROC curve analysis. The experimental outcomes reveal the success of proposed methods in two benchmarked credit scoring (Australian credit scoring and German loan approval) datasets obtained from UCI repository. [ABSTRACT FROM AUTHOR]

Credit scoring is an automated, objective and consistent tool which helps lenders to provide quick loan decisions. It can replace some of the more mechanical work done by experienced loan officers whose decisions are intuitive but potentially subject to bias. Prospective borrowers may have a strong motivation to fraudulently falsify one or more of the attributes they report on their application form. Applicants learn about the characteristics that are used to build credit scoring models, and may alter the answers on their application form to improve their chance of loan approval. Few automated credit scoring models have considered falsified information from borrowers. We will show that sometimes it is profitable for financial institutions to spend money and effort to identify dishonest customers. We will also find the optimal effort that banks should spend on identifying these liars. Furthermore, we will show that it is possible for liars to eventually adjust their lies to escape from credit checks. The proposed issue will be studied using simulated data and discriminant analysis. This research can help lending financial institutions to reduce risk and maximize profit, and it also shows that it is feasible for customers to lie intelligently so as to evade credit checks and get loans. [ABSTRACT FROM AUTHOR]

Modern data collections create vast opportunities for detecting useful hidden relationships. Also, increasingly, they fuel data privacy concerns. A trade-off between privacy protection and data usefulness is by now widely acknowledged. Real world data classification tasks, as for example credit scoring applications have to deal with such data security limitations by finding a way to effectively incorporate privacy preserving procedures. To this end we propose as a first stage to use a microaggregation procedure in order to anonymize data over personal credit client feature information. In a second stage we examine the performance of support vector machines (SVM) on such anonymized data. SVM are powerful and robust machine learning methods, having superior credit scoring classification performance when applied to original, non-anonymized data. We first partition the original credit scoring data set and construct anonymized data representatives, which are then used for credit client behavior forecasting models constructed by SVM and other comparable learning methods. The validation procedure for such models is adapted to the two-stage modeling approach. In order to assess the loss owing to data anonymization, the different classification models are evaluated against models that are trained on the original data. [ABSTRACT FROM AUTHOR]

Credit scoring or credit risk assessment is a domain of major importance for financial institutions. Accurate predictions can lead to significant savings for the institutions. In the current work we evaluate the use of an Ant Colony System (ACS) in the problem of credit scoring. ACS are nature inspired algorithms that search for the optimal solution, by mimicking the functions of ants. In our application artificial ants are applied for rule extraction. The performance of ant based rule extraction is compared against six widely used classification methods. All tests are complemented with feature selection approaches, for dimensionality reduction. Our evaluation is performed using three different datasets with credit scoring instances. The obtained results indicate that the examined ant based approach, offers high accuracy comparative to the accuracies obtained by the rest of the classifiers. Considering the fact that our approach has the ability to extract classification rules, thus offering interpretation of results, it appears as a promising alternative classification method for credit scoring. [ABSTRACT FROM AUTHOR]