Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending

Garzón, Miller Janny Ariza; Arroyo, Javier; Caparrini, Antonio; Vargas, María Jesús Segovia

doi:10.1109/access.2020.2984412

Cited by 98 publications

(35 citation statements)

References 57 publications

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“…The application of a wide range of techniques (including statistical and soft computing) results in a more holistic analysis: i.e., such a combination of methods allows to generalize the usefulness of individual predictor variables more profoundly. The selection of these methods is based on their good performance for solving classification problems (e.g., [10], [40]). In fact, all these approaches have also been applied before in analyzing problems about exporting, for instance, [9] and [41] for logistic regression, [42] and [43] for rough set theory, [10] and [101] for decision trees (C4.5), [8] and [15] for artificial neural networks.…”

Section: Methods and Their Applicationmentioning

confidence: 99%

How Does Managerial Experience Predict the Internationalization Type of a Young Firm?

2021

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This study aims to find out how useful managers' past general and export experience is in predicting whether young manufacturing firms become fast internationalizers. Extant literature about the role of managerial experience in determining young firms' internationalization type is scant. This paper fills this gap by providing systematic evidence on which kinds of general and export experience can be used for accurate predictions of two firm types: born globals and general fast internationalizers. Our dataset encompasses information about managerial experience of the whole population of young Estonian manufacturing firms. Based on using four different prediction methods (logistic regression, rough sets, decision tree, neural networks) and a large variety of variables reflecting managers' past experience, the results indicate that in prediction models, export experience variables are more valuable than general experience variables. Born globals can be predicted with an accuracy of at least 90% in case of all applied machine learning methods, while the precision is lower in case of general fast internationalizers. The study leads to important implications for international business theory and practice.

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Section: Methods and Their Applicationmentioning

confidence: 99%

How Does Managerial Experience Predict the Internationalization Type of a Young Firm?

2021

Self Cite

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“…Regulated institutions are not willing to adapt models that cannot explain predictions and this is hampering the use of machine learning, specifically deep learning models. However, Ariza-Garzón et al [24] conducted a study in credit scoring where the focus was to make non-transparent machine learning models explainable. In the study, classical machine learning models were compared to a logistic regression model for Peer-to-Peer (P2P) lending and predictions were explained using SHAP values.…”

Section: Related Workmentioning

confidence: 99%

Making Deep Learning-Based Predictions for Credit Scoring Explainable

Dastile

Çelik

2021

IEEE Access

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Credit scoring has become an important risk management tool for money lending institutions. Over the years, statistical and classical machine learning models have been the most researched risk management tools in credit scoring literature, and recently the focus has turned to deep learning models. This transition is due to better performances that are shown by deep learning models in different domains. Despite deep learning models' superior performances, there is still a need for explaining how these models make their predictions. The non-transparency nature of deep learning models has created a bottleneck for their use in credit scoring. Explanations of decisions are important for lending institutions since it is a requirement for automated decisions that are generated by non-transparent models to be explained. The other issue in using deep learning models, specifically 2D Convolutional Neural Networks (CNNs), in credit scoring is the need to have the data in image format. We propose an explainable deep learning model for credit scoring which can harness the performance benefits offered by deep learning and yet comply with the legislation requirements for the automated decision-making processes. The proposed method converts tabular datasets into images and thus allowing the application of 2D CNNs in credit scoring. Each pixel of the image corresponds to a feature bin of the tabular dataset. The predictions from the 2D CNNs were explained using state-of-the-art explanation methods. Furthermore, explanations were evaluated using a sanity check methodology and also performances of the explanation methods were compared quantitatively. The proposed explainable deep learning model outperforms the other credit scoring methods on publicly available credit scoring datasets.

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“…Serrano-Cinca et al [14] select 18 factor variables classified into five groups: credit grade, credit assessment of the borrower, credit characteristics, loan applicant characteristics, credit history and indebtedness. Employment length at the current position, previous experience with the P2P lending platform, state of address, and FICO is also used [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending

Aleksandrova¹

2021

TEM Journal

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The purpose of this research is to evaluate several popular machine learning algorithms for credit scoring for peer to peer lending. The dataset to fit the models is extracted from the official site of Lending Club. Several models have been implemented, including single classifiers (logistic regression, decision tree, multilayer perceptron), homogeneous ensembles (XGBoost, GBM, Random Forest) and heterogeneous ensemble classifiers like Stacked Ensembles. Results show that ensemble classifiers outperform single ones with Stacked Ensemble and XGBoost being the leaders.

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Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending

Cited by 98 publications

References 57 publications

How Does Managerial Experience Predict the Internationalization Type of a Young Firm?

How Does Managerial Experience Predict the Internationalization Type of a Young Firm?

Making Deep Learning-Based Predictions for Credit Scoring Explainable

Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending

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