Statistical and machine learning models in credit scoring: A systematic literature survey

Dastile, Xolani; Çelik, Turgay; Potsane, Moshe Moses

doi:10.1016/j.asoc.2020.106263

Cited by 231 publications

(142 citation statements)

References 107 publications

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“…ANN is one of the first machine learning techniques to be used in credit risk assessment [10] and is still vastly used. For instance, Luo [20] examined the rating accuracy of five techniques, including ANN, in a single structure, combining with bagging.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Moreover, considering the diversity of machine learning methods, it is important to note that, according to Dastile et al and Galindo and Tamayo [10,13], algorithms for credit risk analysis vary substantially in their structure, approach and rationale, but can be classified into some groups, which we organized in the following subsections.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Within the context of credit risk research using machine learning techniques, there are several studies that seek to analyze the adequacy of the models in specific databases [1,25,35]. However, the literature has not yet identified techniques that consistently lead to higher credit prediction accuracy [10]. Vieira et al [31] examined the performance some of the most promising techniques, such as Support Vector Machines (SVM, which makes a line that seeks to maximize the distance between the instances from different groups), Decision Trees (DT, that classify instances by ordering them into sub-trees, from the root to some leaf ), Bagging (or Bootstrap aggregating, takes n bootstraps from the full sample and builds a classifier that gives a vote for each sample and uses a majority vote for classifying each instance), AdaBoost (adaptative boosting is similar to bagging, just include a weight in each vote based on its quality), and Random Forest (RF, that classifies by majority decision of votes given by a multitude of decision trees).…”

Section: Introductionmentioning

confidence: 99%

“…According to Khandani et al [15], machine learning procedures refer to a set of algorithms developed to recognize patterns using computational algorithms. Moreover, these tools have been widely employed in credit applications [12, 18, among others], as underpinned by Dastile et al [10].…”

Section: Introductionmentioning

confidence: 99%

“…These metrics are examined by other papers [e.g. 13,19,20,21,22,25] and are vastly used to assess performance of classification methods [10]. The findings are compared with previous results published in the literature.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning predictivity applied to consumer creditworthiness

2020

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Intelligent credit scoring using deep learning methods

Gicić

Ðonko

Subaşı

2023

Concurrency and Computation

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Summary Credit scoring is one the most important parts of credit risk management in reducing the risk of client defaults and bankruptcies. Deep learning has received much attention in recent years, but it has not been implemented so intensively in credit scoring compared to other financial domains. In this article, stacked unidirectional and bidirectional LSTM (long short‐term memory) networks as a complex area of deep learning are applied in solving credit scoring problems for the first time. The proposed robust model exploits the full potential of the three‐layer stacked LSTM and BDLSTM (bidirectional LSTM) architecture with the treatment and modeling of public datasets in a novel way since credit scoring is not a time sequence problem. Attributes of each loan instance were transformed into a sequence of the matrix with a fixed sliding window approach with a one‐time step. Our proposed models outperform existing and much more complex deep learning solutions thus we succeeded in preserving simplicity. In this article, measures of different types are employed to carry out consistent conclusions. The results by applying three hidden layers on the German Credit dataset showed an accuracy of 87.19%, for Kaggle dataset accuracy reached 93.69%, and for Microcredit dataset accuracy of 97.80%.

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Deep learning meets decision trees: An application of a heterogeneous deep forest approach in credit scoring for online consumer lending

Xia

Guo

et al. 2022

Journal of Forecasting

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Online consumer lending has recently been growing rapidly, but it faces high credit risk. For this problem, developing powerful credit scoring models has become an effective solution and can be achieved from three aspects: modeling approach, data source, and evaluation measure. This paper proposes a novel model that departs from those in previous studies in threefold. First, a heterogeneous deep forest model that combines deep learning architecture and tree‐based ensemble classifiers is proposed as the modeling approach. Second, a Bayesian‐based macroeconomic variable optimization method is developed to determine the macroeconomic variables and the corresponding lag term, and the selected macroeconomic variables are used as supplementary data source for modeling. Lastly, a series of capital charge error measures is proposed to evaluate credit scoring models from a regulatory perspective. The proposal is evaluated on multiple large datasets under performance measures on predictive accuracy, profitability, and capital charge errors. Frequentist and Bayesian nonparametric significance tests are used to examine the statistical significance of heterogeneous deep forest and benchmarks. Three main conclusions can be reached from the comparison. First, heterogeneous deep forest significantly outperforms the industry benchmarks over all the evaluation measures. Second, the predictive performance is enhanced after incorporating the selected macroeconomic variables and the corresponding lag, and the result remains robust under cross‐validation and forward‐chaining validation. Third, the capital charge errors reflect the model performance from a regulatory perspective and thus lead to different rankings from those when evaluating predictive accuracy and profitability.

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Statistical and machine learning models in credit scoring: A systematic literature survey

Cited by 231 publications

References 107 publications

Machine learning predictivity applied to consumer creditworthiness

Machine learning predictivity applied to consumer creditworthiness

Intelligent credit scoring using deep learning methods

Deep learning meets decision trees: An application of a heterogeneous deep forest approach in credit scoring for online consumer lending

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