A multi-objective approach for profit-driven feature selection in credit scoring

Kozodoi, Nikita; Lessmann, Stefan; Παπακωνσταντίνου, Κωνσταντίνος; Gatsoulis, Yiannis; Baesens, Bart

doi:10.1016/j.dss.2019.03.011

Cited by 111 publications

(49 citation statements)

References 40 publications

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“…Logistic Regression is the most traditional technique used for modeling classification in credit risk [16,21]. Thus, we also study Logistic Regression results as benchmark.…”

Section: Resultsmentioning

confidence: 99%

“…Moula et al, Pławiak et al, Zhong et al [ 22 , 25 , 35 ] mention that the kernel function can be associated to linear functions, radial basis functions (RBF), polynomial functions or sigmoid functions. We use in our study, linear functions and RBF, since these models lead to interesting levels of performance in previous studies [ 14 , 16 , 22 , 25 ] and capture linear and/or nonlinearity patterns, in the case of RBF.…”

Section: Methodsmentioning

confidence: 99%

“…Lei et al, Shen et al, Yeh and Lien [ 18 , 26 , 34 ] investigated a Taiwanese credit card database, and Twala [ 30 ] analyzed credit operations in United States, Germany and Australia. These two last countries are also studied by Damrongsakmethee and Neagoe, Feng et al, Kamalloo and Saniee Abadeh, Kozodoi et al, Moula et al, Shi et al, Siami et al and Xiao et al [ 9 , 12 , 14 , 16 , 22 , 27 , 28 , 33 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine learning predictivity applied to consumer creditworthiness

2020

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Credit risk evaluation has a relevant role to financial institutions, since lending may result in real and immediate losses. In particular, default prediction is one of the most challenging activities for managing credit risk. This study analyzes the adequacy of borrower’s classification models using a Brazilian bank’s loan database, and exploring machine learning techniques. We develop Support Vector Machine, Decision Trees, Bagging, AdaBoost and Random Forest models, and compare their predictive accuracy with a benchmark based on a Logistic Regression model. Comparisons are analyzed based on usual classification performance metrics. Our results show that Random Forest and Adaboost perform better when compared to other models. Moreover, Support Vector Machine models show poor performance using both linear and nonlinear kernels. Our findings suggest that there are value creating opportunities for banks to improve default prediction models by exploring machine learning techniques.

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“…Logistic Regression is the most traditional technique used for modeling classification in credit risk [16,21]. Thus, we also study Logistic Regression results as benchmark.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning predictivity applied to consumer creditworthiness

2020

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“…At the same time, management actions can be performed at several levels by members of the management board, the director board, and the head. In the scientific literature, it is noted that there is a lack of practice here and caution when requesting large sums of money (Kozodoi, Lessmann, Papakonstantinou, Gatsoulis, & Baesensc, 2019). https://doi.org/10.15405/epsbs.2020.04.118 Corresponding Author: U. E. Monastyrsky Selection and peer-review under responsibility of the Organizing Committee of the conference eISSN: The remedy for violation of such duties should be the award of damages for claims against the parties to the contract under discussion.…”

Section: Resultsmentioning

confidence: 99%

Role Of Recovery Of Losses From Managers Or Controlling Persons At Enterprises

Monastyrsky¹

2020

European Proceedings of Social and Behavioural Sciences

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The corporate law reform has predetermined the need for a detailed study of managers' liability. It is liability, not only for the deliberate appropriation of the company property, but also for careless business operations, which resulted in negative economic indicators of the legal entity. Losses in corporate relations arise from violations and obstacles to actions that have a positive effect on the organizational position in the market, the value of its shares or its capitalization. In this case, the causers of damage to the organization may be people entrusted with its current financial and economic management, as well as subjects that have the ability to determine the company's behavior, with whom the owner of the share does not have a direct real or binding relationship. The author tries to find answers to the question of implementation by company representatives of their right to claim compensation for loss-making activities that also occur when performing actions that do not meet the usual conditions of the civil turnover or ordinary business risk. It is concluded that it is unacceptable to hold managers accountable for displaying bad faith or unreasonableness in the absence of their guilt. Guilt is a prerequisite for corporate liability of managers. In general, it should be noted that the civil law reform of the corporate sphere was unsuccessful in terms of the applied tools for recovery of losses. The legislation needs to be amended to specify that the managers' liability in organizations occurs only if there is their guilt.

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“…In this context, the development of the Z-Score was proposed in the year 1968 by Altman [32] that has been applied in many companies in the financial sector. For the application of credit scoring, in recent years, several new techniques have appeared, namely: Decision Trees [33], Artificial Neural Networks [12], Support Vector Machines [9], Rough Sets [19], Deep Learning [15], and Metaheuristic algorithms [34], among others.…”

Section: Computational Intelligence Models For Financial Applicationsmentioning

confidence: 99%

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

Rangel-Díaz-de-la-Vega

Villuendas-Rey

Yáñez-Márquez

et al. 2020

Applied Sciences

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In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring. To do this, six undersampling algorithms, six oversampling algorithms and four hybrid algorithms were evaluated in 13 imbalanced datasets referring to credit scoring. Then, the performance of four associative classifiers was analyzed. The experiments carried out allowed us to determine which sampling algorithms had the best results, as well as their impact on the associative classifiers evaluated. Accordingly, we determine that the Hybrid Associative Classifier with Translation, the Extended Gamma Associative Classifier and the Naïve Associative Classifier do not improve their performance by using sampling algorithms for credit data balancing. On the other hand, the Smallest Normalized Difference Associative Memory classifier was beneficiated by using oversampling and hybrid algorithms.

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A multi-objective approach for profit-driven feature selection in credit scoring

Cited by 111 publications

References 40 publications

Machine learning predictivity applied to consumer creditworthiness

Machine learning predictivity applied to consumer creditworthiness

Role Of Recovery Of Losses From Managers Or Controlling Persons At Enterprises

Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers

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