Feature Selection in a Credit Scoring Model

Laborda, Juan; Ryoo, Seyong

doi:10.3390/math9070746

Cited by 27 publications

(16 citation statements)

References 61 publications

(66 reference statements)

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“…Feature score and approximation of the relevance score. The feature score assessment is crucial in identifying feature engineering 36,37 . In the considered image img, where the texture of the images is designated as the dimension C of the feature vector.…”

Section: Local Binary Pattern a Local Binary Pattern (Lbpmentioning

confidence: 99%

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Praveen

Srinivasu

Shafi

et al. 2022

Sci Rep

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Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting for most deaths. Its early discovery and diagnosis may significantly increase the odds of survival. Breast cancer imaging is critical for early identification, clinical staging, management choices, and treatment planning. In the current study, the FastAI technology is used with the ResNet-32 model to precisely identify ductal carcinoma. ResNet-32 is having few layers comparted to majority of its counterparts with almost identical performance. FastAI offers a rapid approximation toward the outcome for deep learning models via GPU acceleration and a faster callback mechanism, which would result in faster execution of the model with lesser code and yield better precision in classifying the tissue slides. Residual Network (ResNet) is proven to handle the vanishing gradient and effective feature learning better. Integration of two computationally efficient technologies has yielded a precision accuracy with reasonable computational efforts. The proposed model has shown considerable efficiency in the evaluating parameters like sensitivity, specificity, accuracy, and F1 Score against the other dominantly used deep learning models. These insights have shown that the proposed approach might assist practitioners in analyzing Breast Cancer (BC) cases appropriately, perhaps saving future complications and death. Clinical and pathological analysis and predictive accuracy have been improved with digital image processing.

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Section: Local Binary Pattern a Local Binary Pattern (Lbpmentioning

confidence: 99%

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Praveen

Srinivasu

Shafi

et al. 2022

Sci Rep

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“…In quantitative models, each data instance is described by various characteristics representing the level of risk of a loan or borrower (Laborda and Ryoo, 2021; Xia et al. , 2021).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Furthermore, credit granting is usually regarded as a dynamic scenario (Xia et al. , 2021; Laborda and Ryoo, 2021). This makes it a complex decision-making issue, which may compromise the survival of an organisation.…”

Section: Introductionmentioning

confidence: 99%

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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“…Such a trend is evident in various disciplines in credit risk management and the improvement of credit scoring applications as well. 11 The most comprehensive analysis of the application of statistical and machine learning models in credit scoring was performed by Lessman, 12 with the conclusion that there is no consensus on the best performing algorithm. However, the majority of the research proved that machine learning techniques are significantly better than any statistical model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A large number of papers have been published in the financial sector, and much research on the application of machine learning has been ongoing in recent years. Such a trend is evident in various disciplines in credit risk management and the improvement of credit scoring applications as well 11 . The most comprehensive analysis of the application of statistical and machine learning models in credit scoring was performed by Lessman, 12 with the conclusion that there is no consensus on the best performing algorithm.…”

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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Feature Selection in a Credit Scoring Model

Cited by 27 publications

References 61 publications

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

A recent review on optimisation methods applied to credit scoring models

Intelligent credit scoring using deep learning methods

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