Credit risk classification: an integrated predictive accuracy algorithm using artificial and deep neural networks

Mahbobi, Mohammad; Kimiagari, Salman; Vasudevan, Marriappan

doi:10.1007/s10479-021-04114-z

Cited by 25 publications

(7 citation statements)

References 32 publications

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“…The proposed fraud detection models should also be applied to solving related fraud detection problems, such as credit card and loan frauds, which also exhibit class imbalance characteristics and large real-world datasets are available for these problems (West & Bhattacharya, 2016). Other possible application fields of the proposed model include credit scoring (default prediction) (Mahbobi et al, 2021), direct marketing (Wong et al, 2020), and customer churn prediction (Wong et al, 2020). An issue that was not addressed in this study was the interpretability property of the fraud detection models.…”

Section: Discussionmentioning

confidence: 99%

Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

2022

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Mobile payment systems are becoming more popular due to the increase in the number of smartphones, which, in turn, attracts the interest of fraudsters. Extant research has therefore developed various fraud detection methods using supervised machine learning. However, sufficient labeled data are rarely available and their detection performance is negatively affected by the extreme class imbalance in financial fraud data. The purpose of this study is to propose an XGBoost-based fraud detection framework while considering the financial consequences of fraud detection systems. The framework was empirically validated on a large dataset of more than 6 million mobile transactions. To demonstrate the effectiveness of the proposed framework, we conducted a comparative evaluation of existing machine learning methods designed for modeling imbalanced data and outlier detection. The results suggest that in terms of standard classification measures, the proposed semi-supervised ensemble model integrating multiple unsupervised outlier detection algorithms and an XGBoost classifier achieves the best results, while the highest cost savings can be achieved by combining random under-sampling and XGBoost methods. This study has therefore financial implications for organizations to make appropriate decisions regarding the implementation of effective fraud detection systems.

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Section: Discussionmentioning

confidence: 99%

Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

2022

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“…This study chooses the KS statistic as the metric for three reasons. First, the credit data of SMEs is often imbalanced (Mahbobi et al, 2021 ). The KS statistic measures the default discrimination power of a model and can show the characteristics of imbalanced SME credit data.…”

Section: Methodsmentioning

confidence: 99%

A novel framework of credit risk feature selection for SMEs during industry 4.0

Yang

Shi

et al. 2022

Ann Oper Res

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With the development of industry 4.0, the credit data of SMEs are characterized by a large volume, high speed, diversity and low-value density. How to select the key features that affect the credit risk from the high-dimensional data has become the critical point to accurately measure the credit risk of SMEs and alleviate their financing constraints. In doing so, this paper proposes a credit risk feature selection approach that integrates the binary opposite whale optimization algorithm (BOWOA) and the Kolmogorov–Smirnov (KS) statistic. Furthermore, we use seven machine learning classifiers and three discriminant methods to verify the robustness of the proposed model by using three actual bank data from SMEs. The empirical results show that although no one artificial intelligence credit evaluation method is universal for different SMEs’ credit data, the performance of the BOWOA-KS model proposed in this paper is better than other methods if the number of indicators in the optimal subset of indicators and the prediction performance of the classifier are considered simultaneously. By providing a high-dimensional data feature selection method and improving the predictive performance of credit risk, it could help SMEs focus on the factors that will allow them to improve their creditworthiness and more easily access loans from financial institutions. Moreover, it will also help government agencies and policymakers develop policies to help SMEs reduce their credit risks.

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“…In addition to standard neural networks, some researchers focus on deep learning models in many financial fields. Compared with an artificial neural network (ANN), a deep neural network (DNN) is a model with more than one hidden layer between the input and output layers [45]. Deep learning models such as Convolutional Neural Networks (CNN) [46], Generative Adversarial Networks (GAN) [47], and Recurrent Neural Networks (RNN) [48] have been proven to significantly improve the accuracy of classification in various financial problems [49].…”

Section: Research On Default Risk Prediction Methodsmentioning

confidence: 99%

Default Risk Prediction of Enterprises Based on Convolutional Neural Network in the Age of Big Data: Analysis from the Viewpoint of Different Balance Ratios

Jiang

Pan

2022

Complexity

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In the age of big data, machine learning models are globally used to execute default risk prediction. Imbalanced datasets and redundant features are two main problems that can reduce the performance of machine learning models. To address these issues, this study conducts an analysis from the viewpoint of different balance ratios as well as the selection order of feature selection. Accordingly, we first use data rebalancing and feature selection to obtain 32 derived datasets with varying ratios of balance and feature combinations for each dataset. Second, we propose a comprehensive metric model based on multimachine learning algorithms (CMM-MLA) to select the best-derived dataset with the optimal balance ratio and feature combination. Finally, the convolutional neural network (CNN) is trained on the selected derived dataset to evaluate the performance of our approach in terms of type-II error, accuracy, G-mean, and AUC. There are two contributions in this study. First, the optimal balance ratio is found through the classification accuracy, which changes the deficiency of the existing research that samples are imbalanced or the balance ratio is 1 : 1 and ensures the accuracy of the classification model. Second, a comprehensive metric model based on the machine learning algorithm is proposed, which can simultaneously find the best balance ratio and the optimal feature selection. The experimental results show that our method can noticeably improve the performance of CNN, and CNN outperforms the other four commonly used machine learning models in the task of default risk prediction on four benchmark datasets.

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Credit risk classification: an integrated predictive accuracy algorithm using artificial and deep neural networks

Cited by 25 publications

References 32 publications

Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

A novel framework of credit risk feature selection for SMEs during industry 4.0

Default Risk Prediction of Enterprises Based on Convolutional Neural Network in the Age of Big Data: Analysis from the Viewpoint of Different Balance Ratios

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