financeФинансы: теория и практика/Finance: Theory and PracticeFinance: Theory and Practice2587-56712587-7089Financial University under The Government of Russian Federation10.26794/2587-5671-2024-28-3-174-182finance-2964Research ArticleМЕЖДИСЦИПЛИНАРНЫЕ ИССЛЕДОВАНИЯINTERDISCIPLINARY STUDIESИскусственный интеллект: стратегия управления финансовыми рискамиArtificial Intelligence: The Strategy of Financial Risk Managementhttps://orcid.org/0000-0002-4649-3117КумарА.KumarA.

Абхиджит Кумар — MBA, научный сотрудник факультета гуманитарных и социальных наук; специалист (S.O)

Дханбад;   Мумбаи, Махараштра, (зона Гоа)

Abhijeet Kumar — MBA, Research Scholar, Department of Humanities and Social Sciences; Specialist Officer (S.O)

Dhanbad; Mumbai, Maharashtra, (Goa Zone)

Abhijeet.Kumar7@bankofindia.co.inhttps://orcid.org/0000-0001-7115-7425КумарА.KumarA

Авинаш Кумар — научный сотрудник; SRF, факультет гуманитарных и социальных наук, Дханбад; доцент (G.F) / доцент (G.F.); научный сотрудник

Ахмадабад и Бангалор; Бангалор; Дханбад; Кум, Иран

Avinash Kumar — Fellow; SRF, Faculty of Humanities and Social Sciences; Asst. Prof. (G.F) / Asst. Prof. (G.F); Research Fellow

Ahmedabad, Bangalor; Dhanbad; Qom, Iran

1988avinashsingh@gmail.comhttps://orcid.org/0000-0002-1614-6237КумариС.KumariS.

Свати Кумари — SRF, факультет электротехники и вычислительной техники (EECS)

Бхилай

Swati Kumari— SRF, Department of Electrical Engineering and Computer Science (EECS)

Bhilai

swatisingh0437@gmail.comhttps://orcid.org/0000-0002-1923-294XКумариС.KumarS.

Снеха Кумари — магистр делового администрирования в области финансов, помощник менеджера

Мумбаи, Махараштра; (зона Джанбад)

Sneha Kumari — MBA in Finance, Assistant Manager

Mumbai, Maharashtra, (Dhanbad Zone)

snehasingh4571@gmail.comhttps://orcid.org/0000-0003-4525-367XКумариН.KumariN.

Неха Кумари — научный сотрудник факультета гуманитарных и социальных наук

Дханбад

Neha Kumari — Research Scholar, Department of Humanities and Social Sciences

Dhanbad

nehak.bhu@gmail.comhttps://orcid.org/0000-0002-7738-0588БехураА. К.BehuraA. K.

Аджит Кумар Бехура — PhD, профессор, доцент кафедры гуманитарных и социальных наук

Дханбад

Ajit K. Behura — PhD, Prof., Department of Humanities and Social Sciences

Dhanbad

ajitbehura@gmail.comИндийский технологический институт (Индийская школа горного дела); Банк Индии, Правительство Индии (U/T)ИндияIndian Institute of Technology (Indian School of Mines); Bank of India, Government of India (U/T)IndiaИндийский институт менеджмента; Индийский институт менеджмента; Мемориальный колледж П.К. Роя; Колледж Гуру Нанак; Университет Бинод Бихари Махто Кояланчал; Университет религий и конфессийИндияIndian Institute of Management; Indian Institute of Management; P K Roy Memorial College; Guru Nanak College; Binod Bihari Mahto Koyalanchal University; University of Religions and DenominationsIndiaИндийский технологический институтРоссияIndian Institute of TechnologyRussian FederationБанк Индии, Правительство Индии (U/T)ИндияBank of India, Government of India (U/T)IndiaИндийский технологический институт (Индийская школа горного дела)ИндияIndian Institute of Technology (Indian School of Mines)India202411072024283174182Copyright © Кумар А., Кумар А., Кумари С., Кумари С., Кумари Н., Бехура А.К., 20242024Кумар А., Кумар А., Кумари С., Кумари С., Кумари Н., Бехура А.К.Kumar A., Kumar A., Kumari S., Kumar S., Kumari N., Behura A.K.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://financetp.fa.ru/jour/article/view/2964

Данное исследование рассматривает использование искусственного интеллекта (ИИ) в качестве инструмента управления финансовыми рисками. Стимулом этого нововведения стало революционное влияние, которое оказывают финансовые технологии на бизнес-операции. Традиционные методы управления финансовыми рисками больше не приносят желаемых результатов и требуют пересмотра. Цель исследования — оценить роль искусственного интеллекта в управлении финансовыми рисками и предложить рекомендации по его дальнейшему использованию в финансовом секторе экономики. Методичный анализ соответствующей научной литературы показал, что ИИ, в частности машинное обучение, может помочь в управлении финансовыми рисками. Сделан вывод, что ИИ улучшает управление рыночными и кредитными рисками при проверке моделей, моделировании рисков, стресс-тестировании и подготовке данных. ИИ помогает контролировать качество полученных сведений, выявлять мошенничество и осуществлять поиск нужной информации в интернете. В будущем финансовые технологии будут продолжать оказывать влияние на финансовый сектор по мере того, как действующие компании модифицируют свою деятельность. Таким образом, инструменты управления финансовыми рисками будут включать в себя ИИ. В исследовании рассматриваются возможности использования ИИ в финансовом (рыночном и кредитном), риск-менеджменте и операционном секторах (непрерывность бизнеса и аварийное восстановление). Представлены наиболее перспективные технологии и методы ИИ, такие как RPA, управление данными, блокчейн, MRL, MRC, CRU, глубокое обучение, OML, моделирование и стресс-тестирование, машинное обучение и алгоритмы, нейронные сети, деревья решений, CPM, CRA, Black Box и т.д. для улучшения управления финансовыми рисками (FRM).

This research examines the use of artificial intelligence (AI) as a financial risk management tool. The concept is motivated by the revolutionary effects that financial technology has on business operations. Traditional methods of financial risk management are no longer effective and require revision. The purpose of the study is to assess the role of artificial intelligence in the management of financial risks and offer recommendations for its further use in the financial sector of the economy. Methodological analysis of relevant scientific literature showed that AI, in particular machine learning, can help in managing financial risks. It has been concluded that AI improves the management of market and credit risks in model verification, risk modelling, stress testing and data preparation. AI helps to monitor the quality of information received, detect fraud and search for the right information on the Internet. In the future, financial technology will continue to influence the financial sector as operating companies modify their operations. Thus, financial risk management tools will include AI. The study examines the possibilities of AI use in financial (market and credit), risk management and operational sectors (business continuity and emergency recovery). The paper presents the most promising AI technologies and techniques such as RPA, Data Management, Blockchain, MRL, MRC, CRU, Deep Learning, OML, Modelling and Stress Testing, Machine Learning and Algorithms, Neural Networks, Decision Trees, CPM, CRA, Black Box, etc. to improve “Financial Risk Management (FRM)”.

искусственный интеллект (AI)кредитный риск (CR)операционный риск (OR)рыночный риск (MR)машинное обучение (ML)artificial intelligence (AI)credit risk (CR)operational risk (OR)market risk (MR)machine learning (ML)ReferencesRegan S., Klein L., Jacobs M., Jr., Kazmi S. Model behavior. Nothing artificial: Emerging trends in the validation of machine learning and artificial intelligence models. Dublin: Accenture Consulting; 2017. 20 p. URL: https://www.michaeljacobsjr.com/wp-content/uploads/2022/04/Emerging_Trends_in_the_Validation_of_ML_and_AI_Models-USL_Print_Final_Dec19_1_pdfRegan S., Klein L., Jacobs M., Jr., Kazmi S. Model behavior. Nothing artificial: Emerging trends in the validation of machine learning and artificial intelligence models. Dublin: Accenture Consulting; 2017. 20 p. URL: https://www.michaeljacobsjr.com/wp-content/uploads/2022/04/Emerging_Trends_in_the_Validation_of_ML_and_AI_Models-USL_Print_Final_Dec19_1_pdfDay S. Quants turn to machine learning to model market impact. Risk. net. Apr. 05, 2017. URL: https://www.risk.net/asset-management/4644191/quants-turn-to-machine-learning-to-model-market-impactDay S. Quants turn to machine learning to model market impact. Risk.net. Apr. 05, 2017. URL: https://www.risk.net/asset-management/4644191/quants-turn-to-machine-learning-to-model-market-impactHeaton J.B., Polson N., Witte J.H. Rejoinder to ‘Deep learning for finance: Deep portfolios’. Applied Stochastic Models in Business and Industry. 2017;33(1):19–21. DOI: 10.1002/asmb.2230Heaton J.B., Polson N., Witte J.H. Rejoinder to ‘Deep learning for finance: Deep portfolios’. Applied Stochastic Models in Business and Industry. 2017;33(1):19–21. DOI: 10.1002/asmb.2230Ghosh I., Sanyal M.K.Introspecting predictability of market fear in Indian context during COVID 19 pandemic: An integrated approach of applied predictive modelling and explainable AI. International Journal of Information Management Data Insights. 2021;1(2):100039. DOI: 10.1016/j.jjimei.2021.100039Ghosh I., Sanyal M.K.Introspecting predictability of market fear in Indian context during COVID 19 pandemic: An integrated approach of applied predictive modelling and explainable AI. International Journal of Information Management Data Insights. 2021;1(2):100039. DOI: 10.1016/j.jjimei.2021.100039Bogojević Arsić V.Upravljanje finansijskim rizikom. Beograd: SZR Kragulj; 2009. 593 p.Bogojević Arsić V.Upravljanje finansijskim rizikom. Beograd: SZR Kragulj; 2009. 593 p.Altman E.I., Marco G., Varetto F.Corporate distress diagnosis: Comparison using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance. 1994;18(3):505–529. DOI: 10.1016/0378–4266(94)90007–8Altman E.I., Marco G., Varetto F.Corporate distress diagnosis: Comparison using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking & Finance. 1994;18(3):505–529. DOI: 10.1016/0378–4266(94)90007–8Bogojević Arsić V.Challenges of financial risk management: AI applications. Management: Journal of Sustainable Business and Management Solutions in Emerging Economies. 2021;26(3):27–33. DOI: 10.7595/management.fon.2021.0015Bogojević Arsić V.Challenges of financial risk management: AI applications. Management: Journal of Sustainable Business and Management Solutions in Emerging Economies. 2021;26(3):27–33. DOI: 10.7595/management.fon.2021.0015Khandani A.E., Kim A.J., Lo A.W.Consumer credit risk models via machine learning algorithms. Journal of Banking & Finance. 2010;34(11):2767–2787. DOI: 10.1016/j.jbankfin.2010.06.001Khandani A.E., Kim A.J., Lo A.W.Consumer credit risk models via machine learning algorithms. Journal of Banking & Finance. 2010;34(11):2767–2787. DOI: 10.1016/j.jbankfin.2010.06.001Yao X., Crook J., Andreeva G. Support vector regression for loss given default modelling. European Journal of Operational Research. 2015;240(2):528–538. DOI: 10.1016/j.ejor.2014.06.043Yao X., Crook J., Andreeva G. Support vector regression for loss given default modelling. European Journal of Operational Research. 2015;240(2):528–538. DOI: 10.1016/j.ejor.2014.06.043Figini S., Bonelli F., Giovannini E. Solvency prediction for small and medium enterprises in banking. Decision Support Systems. 2017;102:91–97. DOI: 10.1016/j.dss.2017.08.001Figini S., Bonelli F., Giovannini E. Solvency prediction for small and medium enterprises in banking. Decision Support Systems. 2017;102:91–97. DOI: 10.1016/j.dss.2017.08.001Ha V.-S., Nguyen H.-N. Credit scoring with a feature selection approach based deep learning. MATEC Web of Conference. 2016;54:05004. DOI: 10.1051/matecconf/20165405004Ha V.-S., Nguyen H.-N. Credit scoring with a feature selection approach based deep learning. MATEC Web of Conference. 2016;54:05004. DOI: 10.1051/matecconf/20165405004Ha V.-S., Lu D.-N., Choi G.S., Nguyen H.-N., Yoon B.Improving credit risk prediction in online peer-to-peer (P2P) lending using feature selection with deep learning. In: 21st Int. conf. on advanced communication technology (ICACT). (PyeongChang, February 17–20, 2019). Piscataway, NJ: IEEE; 2019. DOI: 10.23919/ICACT.2019.8701943Ha V.-S., Lu D.-N., Choi G.S., Nguyen H.-N., Yoon B.Improving credit risk prediction in online peer-to-peer (P2P) lending using feature selection with deep learning. In: 21st Int. conf. on advanced communication technology (ICACT). (PyeongChang, February 17–20, 2019). Piscataway, NJ: IEEE; 2019. DOI: 10.23919/ICACT.2019.8701943Byanjankar A., Heikkilä M., Mezei J. Predicting credit risk in peer-to-peer lending: A neural network approach. In: IEEE symposium series on computational intelligence (Cape Town, December 07–10, 2015). Piscataway, NJ: IEEE; 2015. DOI: 10.1109/SSCI.2015.109Byanjankar A., Heikkilä M., Mezei J. Predicting credit risk in peer-to-peer lending: A neural network approach. In: IEEE symposium series on computational intelligence (Cape Town, December 07–10, 2015). Piscataway, NJ: IEEE; 2015. DOI: 10.1109/SSCI.2015.109Hou X. P2P borrower default identification and prediction based on RFE-multiple classification models. Open Journal of Business and Management. 2020;8(2):866–880. DOI: 10.4236/ojbm.2020.82053Hou X. P2P borrower default identification and prediction based on RFE-multiple classification models. Open Journal of Business and Management. 2020;8(2):866–880. DOI: 10.4236/ojbm.2020.82053Son Y., Byun H., Lee J. Nonparametric machine learning models for predicting the credit default swaps: An empirical study. Expert Systems with Applications. 2016;58:210–220. DOI: 10.1016/j.eswa.2016.03.049Son Y., Byun H., Lee J. Nonparametric machine learning models for predicting the credit default swaps: An empirical study. Expert Systems with Applications. 2016;58:210–220. DOI: 10.1016/j.eswa.2016.03.049Wang S., Li B., Yang M., Yan Z. Missing data imputation for machine learning. In: Li B., Yang M., Yuan H., Yan Z. eds. IoT as a service. Cham: Springer-Verlag; 2019:67–72. (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Vol. 271). DOI: 10.1007/978–3–030–14657–3_7Wang S., Li B., Yang M., Yan Z. Missing data imputation for machine learning. In: Li B., Yang M., Yuan H., Yan Z. eds. IoT as a service. Cham: Springer-Verlag; 2019:67–72. (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Vol. 271). DOI: 10.1007/978–3–030–14657–3_7Ghorbani A., Zou J.Y. Embedding for informative missingness: Deep learning with incomplete data. In: 56th Annu. Allerton conf. on communication, control, and computing (Monticello, IL, October 02–05, 2018). Piscataway, NJ: IEEE; 2018. DOI: 10.1109/ALLERTON.2018.8636008Ghorbani A., Zou J.Y. Embedding for informative missingness: Deep learning with incomplete data. In: 56th Annu. Allerton conf. on communication, control, and computing (Monticello, IL, October 02–05, 2018). Piscataway, NJ: IEEE; 2018. DOI: 10.1109/ALLERTON.2018.8636008Ding Y., Simonoff J.S.An investigation of missing data methods for classification trees applied to binary response data. Journal of Machine Learning Research. 2010;11(6):131–170. URL: http://www.jmlr.org/papers/volume11/ding10a/ding10a.pdfDing Y., Simonoff J.S.An investigation of missing data methods for classification trees applied to binary response data. Journal of Machine Learning Research. 2010;11(6):131–170. URL: http://www.jmlr.org/papers/volume11/ding10a/ding10a.pdfTwala B. An empirical comparison of techniques for handling incomplete data using decision trees. Applied Artificial Intelligence. 2009;23(5):373–405. DOI: 10.1080/08839510902872223Twala B. An empirical comparison of techniques for handling incomplete data using decision trees. Applied Artificial Intelligence. 2009;23(5):373–405. DOI: 10.1080/08839510902872223García-Laencina P.J., Sancho-Gómez J.L., Figueiras-Vidal A.R. Machine learning techniques for solving classification problems with missing input data. In: Proc. 12th World multi-conf. on systemics, cybernetics and informatics (WMSCI 2008). Winter Garden, FL: International Institute of Informatics and Systemics; 2008. URL: https://www.iiis.org/cds2008/cd2008sci/SCI2008/PapersPdf/S507DT.pdfGarcía-Laencina P.J., Sancho-Gómez J.L., Figueiras-Vidal A.R. Machine learning techniques for solving classification problems with missing input data. In: Proc. 12th World multi-conf. on systemics, cybernetics and informatics (WMSCI 2008). Winter Garden, FL: International Institute of Informatics and Systemics; 2008. URL: https://www.iiis.org/cds2008/cd2008sci/SCI2008/PapersPdf/S507DT.pdfGarcía-Laencina P.J., Serrano J., Figueiras-Vidal A.R., Sancho-Gómez J.L. Multi-task neural networks for dealing with missing inputs. In: Mira J., Álvarez J.R., eds. Bio-inspired modeling of cognitive tasks (IWINAC 2007). Berlin, Heidelberg: Springer-Verlag; 2007:282–291. (Lecture Notes in Computer Science. Vol. 4527). DOI: 10.1007/978–3–540–73053–8_28García-Laencina P.J., Serrano J., Figueiras-Vidal A.R., Sancho-Gómez J.L. Multi-task neural networks for dealing with missing inputs. In: Mira J., Álvarez J.R., eds. Bio-inspired modeling of cognitive tasks (IWINAC 2007). Berlin, Heidelberg: Springer-Verlag; 2007:282–291. (Lecture Notes in Computer Science. Vol. 4527). DOI: 10.1007/978–3–540–73053–8_28Wilkens S. Machine learning in risk measurement: Gaussian process regression for value-at-risk and expected shortfall. Journal of Risk Management in Financial Institutions. 2019;12(4):374–383. DOI: 10.2139/ssrn.3246131Wilkens S. Machine learning in risk measurement: Gaussian process regression for value-at-risk and expected shortfall. Journal of Risk Management in Financial Institutions. 2019;12(4):374–383. DOI: 10.2139/ssrn.3246131Klein L., Jacobs M., Jr., Merchant A. Emerging trends in model risk management: High performance. Delivered. Dublin: Accenture; 2015. 16 p. URL: https://www.michaeljacobsjr.com/wp-content/uploads/2022/04/AccentureEmerging-Trends-Model-Risk-Management.pdfKlein L., Jacobs M., Jr., Merchant A. Emerging trends in model risk management: High performance. Delivered. Dublin: Accenture; 2015. 16 p. URL: https://www.michaeljacobsjr.com/wp-content/uploads/2022/04/AccentureEmerging-Trends-Model-Risk-Management.pdfAbramovV., Lowdermilk M., Zhou X. A practical guide to market risk model validations (Chapter I — Introduction). SSRN Electronic Journal. 2017. DOI: 10.2139/ssrn.2916853AbramovV., Lowdermilk M., Zhou X. A practical guide to market risk model validations (Chapter I — Introduction). SSRN Electronic Journal. 2017. DOI: 10.2139/ssrn.2916853Sanford A., Moosa I. Operational risk modelling and organizational learning in structured finance operations: A Bayesian network approach. Journal of the Operational Research Society. 2015;66(1):86–115. DOI: 10.1057/jors.2013.49Sanford A., Moosa I. Operational risk modelling and organizational learning in structured finance operations: A Bayesian network approach. Journal of the Operational Research Society. 2015;66(1):86–115. DOI: 10.1057/jors.2013.49Carrivick L., Westphal A. Machine learning in operational risk: Making a business case for its practical implementation. White paper. Genève: Operational Riskdata eXchange Association (ORX); 2019. 12 p. URL: https://5992322.fs1.hubspotusercontent-na1.net/hubfs/5992322/2023-themeassets/Resources/ORX%20The%20case%20for%20machine%20learning%20in%20operational%20risk%20white%20paper.pdf?__hstc=167785086.dd3bc162691b835290b1a88709a71fdc.1714982037069.1714982037069.1714982037069.1&__hssc=167785086.4.1714982037069&__hsfp=2305378398&hsutk=dd3bc162691b835290b1a88709a71fdc&contentType=standard-pageCarrivick L., Westphal A. Machine learning in operational risk: Making a business case for its practical implementation. White paper. Genève: Operational Riskdata eXchange Association (ORX); 2019. 12 p. URL: https://5992322.fs1.hubspotusercontent-na1.net/hubfs/5992322/2023-themeassets/Resources/ORX%20The%20case%20for%20machine%20learning%20in%20operational%20risk%20white%20paper.pdf?__hstc=167785086.dd3bc162691b835290b1a88709a71fdc.1714982037069.1714982037069.1714982037069.1&__hssc=167785086.4.1714982037069&__hsfp=2305378398&hsutk=dd3bc162691b835290b1a88709a71fdc&contentType=standard-pageVan Liebergen B. Machine learning: A revolution in risk management and compliance? The Capco Institute Journal of Financial Transformation. 2017;45:60–67. URL: https://www.iif.com/portals/0/Files/private/32370132_van_liebergen_-_machine_learning_in_compliance_risk_management.pdfVan Liebergen B. Machine learning: A revolution in risk management and compliance? The Capco Institute Journal of Financial Transformation. 2017;45:60–67. URL: https://www.iif.com/portals/0/Files/private/32370132_van_liebergen_-_machine_learning_in_compliance_risk_management.pdfWang J., Wang J. Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing. 2015;156:68–78. DOI: 10.1016/j.neucom.2014.12.084Wang J., Wang J. Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks. Neurocomputing. 2015;156:68–78. DOI: 10.1016/j.neucom.2014.12.084Hamdy A., Hussein W.B. Credit risk assessment model based using principal component analysis and artificial neural network. MATEC Web of Conferences. 2016;76:02039. DOI: 10.1051/matecconf/20167602039Hamdy A., Hussein W.B. Credit risk assessment model based using principal component analysis and artificial neural network. MATEC Web of Conferences. 2016;76:02039. DOI: 10.1051/matecconf/20167602039Nazemi A., Heidenreich K., Fabozzi F.J. Improving corporate bond recovery rate prediction using multi-factor support vector regressions. European Journal of Operational Research. 2018;271(2):664–675. DOI: 10.1016/j.ejor.2018.05.024Nazemi A., Heidenreich K., Fabozzi F.J. Improving corporate bond recovery rate prediction using multi-factor support vector regressions. European Journal of Operational Research. 2018;271(2):664–675. DOI: 10.1016/j.ejor.2018.05.024Abedin M.Z., Guotai C., Colombage S., Moula F.-E. Credit default prediction using a support vector machine and a probabilistic neural network. The Journal of Credit Risk. 2018;14(2):1–29. DOI: 10.21314/JCR.2017.233Abedin M.Z., Guotai C., Colombage S., Moula F.-E. Credit default prediction using a support vector machine and a probabilistic neural network. The Journal of Credit Risk. 2018;14(2):1–29. DOI: 10.21314/JCR.2017.233Harris T. Credit scoring using the clustered support vector machine. Expert Systems with Applications. 2015;42(2):741–750. DOI: 10.1016/j.eswa.2014.08.029Harris T. Credit scoring using the clustered support vector machine. Expert Systems with Applications. 2015;42(2):741–750. DOI: 10.1016/j.eswa.2014.08.029Radović O., Stanković J., Stanković J. Tail risk assessment using support vector machine. Journal of Engineering Science and Technology Review. 2015;8(1):61–64. DOI: 10.25103/jestr.081.11Radović O., Stanković J., Stanković J. Tail risk assessment using support vector machine. Journal of Engineering Science and Technology Review. 2015;8(1):61–64. DOI: 10.25103/jestr.081.11Yu L. Credit risk evaluation with a least squares fuzzy support vector machines classifier. Discrete Dynamics in Nature and Society. 2014;2014: 564213. DOI: 10.1155/2014/564213Yu L. Credit risk evaluation with a least squares fuzzy support vector machines classifier. Discrete Dynamics in Nature and Society. 2014;2014: 564213. DOI: 10.1155/2014/564213Pacelli V., Azzollini M. An artificial neural network approach for credit risk management. Journal of Intelligent Learning Systems and Applications. 2011;3(2):103–112. DOI: 10.4236/jilsa.2011.32012Pacelli V., Azzollini M. An artificial neural network approach for credit risk management. Journal of Intelligent Learning Systems and Applications. 2011;3(2):103–112. DOI: 10.4236/jilsa.2011.32012Chen L., Pelger M., Zhu J. Deep learning in asset pricing. Management Science. 2023;70(2):714–750. DOI: 10.1287/mnsc.2023.4695Chen L., Pelger M., Zhu J. Deep learning in asset pricing. Management Science. 2023;70(2):714–750. DOI: 10.1287/mnsc.2023.4695Kim A., Yang Y., Lessmann S., Ma T., Sung M.-C., Johnson J.E.V. Can deep learning predict risky retail investors? A case study in financial risk behaviour forecasting. European Journal of Operational Research. 2020;283(1):217–234. DOI: 10.1016/j.ejor.2019.11.007Kim A., Yang Y., Lessmann S., Ma T., Sung M.-C., Johnson J.E.V. Can deep learning predict risky retail investors? A case study in financial risk behaviour forecasting. European Journal of Operational Research. 2020;283(1):217–234. DOI: 10.1016/j.ejor.2019.11.007

The authors declare that there are no conflicts of interest present.