Making Deep Learning-Based Predictions for Credit Scoring Explainable

Dastile, Xolani; Çelik, Turgay

doi:10.1109/access.2021.3068854

Cited by 44 publications

(28 citation statements)

References 33 publications

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“…, 2019; Kozodoi et al. , 2019; Ashofteh and Bravo, 2021; Dastile and Celik, 2021; Djeundje et al. , 2021; Kang et al.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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A recent review on optimisation methods applied to credit scoring models

Kamimura,

Pinto,

Nagano

2023

JEFAS

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PurposeThis paper aims to present a literature review of the most recent optimisation methods applied to Credit Scoring Models (CSMs).Design/methodology/approachThe research methodology employed technical procedures based on bibliographic and exploratory analyses. A traditional investigation was carried out using the Scopus, ScienceDirect and Web of Science databases. The papers selection and classification took place in three steps considering only studies in English language and published in electronic journals (from 2008 to 2022). The investigation led up to the selection of 46 publications (10 presenting literature reviews and 36 proposing CSMs).FindingsThe findings showed that CSMs are usually formulated using Financial Analysis, Machine Learning, Statistical Techniques, Operational Research and Data Mining Algorithms. The main databases used by the researchers were banks and the University of California, Irvine. The analyses identified 48 methods used by CSMs, the main ones being: Logistic Regression (13%), Naive Bayes (10%) and Artificial Neural Networks (7%). The authors conclude that advances in credit score studies will require new hybrid approaches capable of integrating Big Data and Deep Learning algorithms into CSMs. These algorithms should have practical issues considered consider practical issues for improving the level of adaptation and performance demanded for the CSMs.Practical implicationsThe results of this study might provide considerable practical implications for the application of CSMs. As it was aimed to demonstrate the application of optimisation methods, it is highly considerable that legal and ethical issues should be better adapted to CSMs. It is also suggested improvement of studies focused on micro and small companies for sales in instalment plans and commercial credit through the improvement or new CSMs.Originality/valueThe economic reality surrounding credit granting has made risk management a complex decision-making issue increasingly supported by CSMs. Therefore, this paper satisfies an important gap in the literature to present an analysis of recent advances in optimisation methods applied to CSMs. The main contribution of this paper consists of presenting the evolution of the state of the art and future trends in studies aimed at proposing better CSMs.

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“…, 2019; Kozodoi et al. , 2019; Ashofteh and Bravo, 2021; Dastile and Celik, 2021; Djeundje et al. , 2021; Kang et al.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…, 2022). Important advances have been obtained, and practically all credit management areas (receipt, response, recovery, collection or risk measurement) use CSMs (Řezáč, 2015; Dastile and Celik, 2021). However, the literature has not yet presented a broader analysis of the CSM modelling process.…”

Section: Introductionmentioning

confidence: 99%

A recent review on optimisation methods applied to credit scoring models

Kamimura,

Pinto,

Nagano

2023

JEFAS

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“…The source of the dataset was a Chinese consumer finance company, not the classical German or Australian Credit dataset 26 . The proposed model was however applied only to numerical features and this gap is closed by a new study 27 that used both categorical and continuous features. Another study on CNN application in credit default prediction using the Lending Club dataset demonstrated superior performance regarding the accuracy and AUC (area under the curve) 28 .…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…TreeSHAP method for Stochastic Gradient Boosting [58], a real-time binary classification model [59], CNN [60], MLP [61], etc. The authors in [62] compared the traditional and machine learning models in the credit score evaluation area.…”

Section: Consumer Credit Riskmentioning

confidence: 99%

Machine learning-driven credit risk: a systemic review

Shi

Tse

Luo

et al. 2022

Neural Comput & Applic

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Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models.

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Making Deep Learning-Based Predictions for Credit Scoring Explainable

Cited by 44 publications

References 33 publications

A recent review on optimisation methods applied to credit scoring models

A recent review on optimisation methods applied to credit scoring models

Intelligent credit scoring using deep learning methods

Machine learning-driven credit risk: a systemic review

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