Appropriate machine learning techniques for credit scoring and bankruptcy prediction in banking and finance: A comparative study

Boughaci, Dalila; Alkhawaldeh, Abdullah A. K.

doi:10.3233/rda-180051

Cited by 20 publications

(22 citation statements)

References 18 publications

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“…Investigation of Predictive Ability. The predictive ability of SVMs credit models is examined through two main approaches, i.e., comparative studies [8,11,12,18,26] and application on specific credit domain [19][20][21][22][23][24][25]27].…”

Section: Discussionmentioning

confidence: 99%

“…The most recent comparative study is contributed by Boughaci and Alkhawaldeh [18]. They investigated performances of 11 machine learning techniques (from the family of kNN, BN, NN, TREE, SVM, LOGIT, and ensembles) across eight different datasets.…”

Section: Discussionmentioning

confidence: 99%

“…However, for the holdout category, it can be noticed that the standard deviation is much higher than the other categories. Observing the holdout category, the high deviation is deduced to be contributed from an unusual lower accuracy from Boughaci and Alkhawaldeh [18]. This lower accuracy may be due to the usage of SVM without the hyperparameters tuning procedure.…”

Section: Simultaneous Hyperparameters Tuning and Features Selectionmentioning

confidence: 94%

“…Most of them compared the adopted methods with the standard LOGIT model and have shown the DM models are competitive. A Advances in Operations Research 3 few comparative studies and review papers [1,2,[8][9][10][11][12]18] reported good performance of DM models, and among the various methods, SVM and MA (especially EA) have been widely researched to be the alternative to the credit scoring models.…”

Section: Trendmentioning

confidence: 99%

“…However, they have contributed to another aspect, i.e., outperformance in timing [29,46], proper handling of imbalance [59], transparent rules [36,69], and presentation of competitive new methods [41,47,51,52,60,71,78,84]. Some comparative studies [8,11,12] identified certain methods to have best performance and recommended for future research but they did not penalize the use of other techniques since the reported performance is still very competitive, whereas comparative studies by [18,26] did not result in outperformance of any methods among the compared models. The rest of the research articles reviewed in this study have reported their proposed models having better results than the others.…”

Section: Simultaneous Hyperparameters Tuning and Features Selectionmentioning

confidence: 99%

See 4 more Smart Citations

Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches

Goh

Lee

2019

Advances in Operations Research

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Development of credit scoring models is important for financial institutions to identify defaulters and nondefaulters when making credit granting decisions. In recent years, artificial intelligence (AI) techniques have shown successful performance in credit scoring. Support Vector Machines and metaheuristic approaches have constantly received attention from researchers in establishing new credit models. In this paper, two AI techniques are reviewed with detailed discussions on credit scoring models built from both methods since 1997 to 2018. The main discussions are based on two main aspects which are model type with issues addressed and assessment procedures. Then, together with the compilation of past experiments results on common datasets, hybrid modelling is the state-of-the-art approach for both methods. Some possible research gaps for future research are identified.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Simultaneous Hyperparameters Tuning and Features Selectionmentioning

confidence: 94%

Section: Trendmentioning

confidence: 99%

Section: Simultaneous Hyperparameters Tuning and Features Selectionmentioning

confidence: 99%

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Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches

Goh

Lee

2019

Advances in Operations Research

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On the distributed software architecture of a data analysis workflow: A case study

Taşgetiren

Tigrak²,

Bozan³

et al. 2021

Concurrency and Computation

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Hybrid distributed computing software architectures gain great importance in data analysis workflows as the number of available underlying machine learning libraries and data storage systems increase. We argue that there is a need for novel approaches for software architecture designs that can enable machine learning data analysis workflows to run on top of different subsystem libraries. To address this need, we propose a hybrid distributed software architecture in this manuscript. The proposed architecture manages machine learning models for both supervised and unsupervised machine learning data analysis workflows. To show the usability of the proposed architecture, we implement a prototype for the banking sector as a case study. The prototype application includes two data analysis workflows: a workflow for predicting the loan usage tendency of customers, and a workflow for clustering the customers based on the usage patterns of banking loans. The prototype is tested on a large scale banking dataset. Performance tests were carried out to investigate the performance in terms of both responsiveness and scalability of the system. The results obtained reveal the usability of the proposed architecture.

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Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

Papíková

Papík

2022

Intell Sys Acc Fin Mgmt

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Small and medium-sized enterprises are the pillars of an economy, and their poor performance has a negative impact on living standards of population and country development. This study analyzes real-life data of 89,851 small and medium-sized enterprises, out of which 295 have declared bankruptcy. The analysis is performed via 27 financial ratios. The study framework combines seven classifications and three resampling and seven feature selection methods. Out of all classification methods applied, CatBoost has achieved the best results for all combinations of resampling and feature selection methods. CatBoost surpassed the results of other classification methods for the area under curve parameter, achieving a value of 99.95%. The application of resampling methods on different classification models has not identified a statistically significant level of improvement in any of the resampling methods. This finding has also been observed for feature selection methods. Based on these findings, we assume that individual resampling and feature selection methods do not improve model performance compared with the original imbalanced sample's results.Our results suggest that, even though the data sample may be significantly imbalanced with a minority of bankrupt companies, most classification algorithms can handle this imbalance and achieve interesting results. Moreover, our findings provide broad practical application for all stakeholders who could need to detect bankrupting companies.

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Appropriate machine learning techniques for credit scoring and bankruptcy prediction in banking and finance: A comparative study

Cited by 20 publications

References 18 publications

Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches

Credit Scoring: A Review on Support Vector Machines and Metaheuristic Approaches

On the distributed software architecture of a data analysis workflow: A case study

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

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