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Lending and Credit Risk Management
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Lending and Credit Risk Management

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Risk".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 19085

Special Issue Editors

Prof. Dr. Peter Verhoeven

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Guest Editor
QUT Business School, Queensland University of Technology, Brisbane, QLD 4000, Australia
Interests: risk analysis; banking; financial modelling; project finance; lending
Dr. Doureige Jurdi

E-Mail Website
Guest Editor
Department of Economics, Finance and Marketing, La Trobe Business School, La Trobe University, Melbourne, Bundoora VIC 3086, Australia
Interests: risk modelling; time series analysis; Basel regulations; lending
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Across the globe, the COVID-19 pandemic has highlighted financial fragility, with many businesses and individuals unable to meet their current and ongoing financial obligations. The pandemic has served as a wake-up call to businesses and lenders alike regarding the need to better manage negative shocks to the revenue stream and building a more resilient balance sheet as part of the path to financial resilience. At the same time, credit risk modelling has continued to gain momentum with the advance of big data and artificial intelligence (AI). For this Special Issue, we welcome all types of credit-related papers covering pandemic and normal times.

Prof. Dr. Peter Verhoeven
Dr. Doureige Jurdi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • lending
  • counterparty credit risk
  • COVID-19 and lending
  • credit risk management
  • credit risk modelling
  • debt maturity and cost of lending
  • financial fragility
  • regulatory capital
  • non-performing loans

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Published Papers (3 papers)

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Research

19 pages, 3621 KiB  
Article
Explaining Deep Learning Models for Credit Scoring with SHAP: A Case Study Using Open Banking Data
by Lars Ole Hjelkrem and Petter Eilif de Lange
J. Risk Financial Manag. 2023, 16(4), 221; https://doi.org/10.3390/jrfm16040221 - 2 Apr 2023
Cited by 3 | Viewed by 5864
Abstract
Predicting creditworthiness is an important task in the banking industry, as it allows banks to make informed lending decisions and manage risk. In this paper, we investigate the performance of two different deep learning credit scoring models developed on the textual descriptions of [...] Read more.
Predicting creditworthiness is an important task in the banking industry, as it allows banks to make informed lending decisions and manage risk. In this paper, we investigate the performance of two different deep learning credit scoring models developed on the textual descriptions of customer transactions available from open banking APIs. The first model is a deep learning model trained from scratch, while the second model uses transfer learning with a multilingual BERT model. We evaluate the predictive performance of these models using the area under the receiver operating characteristic curve (AUC) and Brier score. We find that a deep learning model trained from scratch outperforms a BERT transformer model finetuned on the same data. Furthermore, we find that SHAP can be used to explain such models both on a global level and for explaining rejections of actual applications. Full article
(This article belongs to the Special Issue Lending and Credit Risk Management)
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15 pages, 1111 KiB  
Article
The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank
by Lars Ole Hjelkrem, Petter Eilif de Lange and Erik Nesset
J. Risk Financial Manag. 2022, 15(12), 597; https://doi.org/10.3390/jrfm15120597 - 12 Dec 2022
Cited by 3 | Viewed by 3866
Abstract
Banks generally use credit scoring models to assess the creditworthiness of customers when they apply for loans or credit. These models perform significantly worse when used on potential new customers than existing customers, due to the lack of financial behavioral data for new [...] Read more.
Banks generally use credit scoring models to assess the creditworthiness of customers when they apply for loans or credit. These models perform significantly worse when used on potential new customers than existing customers, due to the lack of financial behavioral data for new bank customers. Access to such data could therefore increase banks’ profitability when recruiting new customers. If allowed by the customer, Open Banking APIs can provide access to balances and transactions from the past 90 days before the score date. In this study, we compare the performance of conventional application credit scoring models currently in use by a Norwegian bank with a deep learning model trained solely on transaction data available through Open Banking APIs. We evaluate the performance in terms of the AUC and Brier score and find that the models based on Open Banking data alone are surprisingly effective in predicting default compared to the conventional credit scoring models. Furthermore, an ensemble model trained on both traditional credit scoring data and features extracted from the deep learning model further outperforms the conventional application credit scoring model for new customers and narrows the performance gap between application credit scoring models for existing and new customers. Therefore, we argue that banks can increase their profitability by utilizing data available through Open Banking APIs when recruiting new customers. Full article
(This article belongs to the Special Issue Lending and Credit Risk Management)
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23 pages, 6205 KiB  
Article
Explainable AI for Credit Assessment in Banks
by Petter Eilif de Lange, Borger Melsom, Christian Bakke Vennerød and Sjur Westgaard
J. Risk Financial Manag. 2022, 15(12), 556; https://doi.org/10.3390/jrfm15120556 - 28 Nov 2022
Cited by 17 | Viewed by 7440
Abstract
Banks’ credit scoring models are required by financial authorities to be explainable. This paper proposes an explainable artificial intelligence (XAI) model for predicting credit default on a unique dataset of unsecured consumer loans provided by a Norwegian bank. We combined a LightGBM model [...] Read more.
Banks’ credit scoring models are required by financial authorities to be explainable. This paper proposes an explainable artificial intelligence (XAI) model for predicting credit default on a unique dataset of unsecured consumer loans provided by a Norwegian bank. We combined a LightGBM model with SHAP, which enables the interpretation of explanatory variables affecting the predictions. The LightGBM model clearly outperforms the bank’s actual credit scoring model (Logistic Regression). We found that the most important explanatory variables for predicting default in the LightGBM model are the volatility of utilized credit balance, remaining credit in percentage of total credit and the duration of the customer relationship. Our main contribution is the implementation of XAI methods in banking, exploring how these methods can be applied to improve the interpretability and reliability of state-of-the-art AI models. We also suggest a method for analyzing the potential economic value of an improved credit scoring model. Full article
(This article belongs to the Special Issue Lending and Credit Risk Management)
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