{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:16:27Z","timestamp":1761174987526,"version":"build-2065373602"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00539-8","type":"journal-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:33:54Z","timestamp":1761118434000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Use of machine learning in the financial sector: an analysis of trends and the research agenda"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9434-6923","authenticated-orcid":false,"given":"Alejandro","family":"Valencia-Arias","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4525-1423","authenticated-orcid":false,"given":"Diana Yanet","family":"Gaviria Rodr\u00edguez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0496-853X","authenticated-orcid":false,"given":"Lilian","family":"Verde Flores","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6192-2928","authenticated-orcid":false,"given":"Sebasti\u00e1n","family":"Cardona-Acevedo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5818-949X","authenticated-orcid":false,"given":"Juan Manuel","family":"Raunelli-Sander","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9136-0260","authenticated-orcid":false,"given":"Erica Janet","family":"Agudelo Ceballos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8689-4399","authenticated-orcid":false,"given":"Daniel","family":"Cardona Valencia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,22]]},"reference":[{"key":"539_CR1","doi-asserted-by":"publisher","first-page":"107237","DOI":"10.1016\/j.engfailanal.2023.107237","volume":"149","author":"A Cardellicchio","year":"2023","unstructured":"Cardellicchio A, Ruggieri S, Nettis A, Ren\u00f2 V, Uva G. Physical Interpre-tation of machine Learning-Based recognition of defects for the risk Man-agement of existing Bridge heritage. Eng Fail Anal. 2023;149:107237. https:\/\/doi.org\/10.1016\/j.engfailanal.2023.107237.","journal-title":"Eng Fail Anal"},{"key":"539_CR2","doi-asserted-by":"publisher","DOI":"10.1057\/s41278-020-00183-2","author":"M Clintworth","year":"2023","unstructured":"Clintworth M, Lyridis D, Boulougouris E. Financial risk assessment in shipping: a holistic machine learning based methodology. Maritime Econ Logistics. 2023. https:\/\/doi.org\/10.1057\/s41278-020-00183-2.","journal-title":"Maritime Econ Logistics"},{"key":"539_CR3","doi-asserted-by":"publisher","first-page":"37848","DOI":"10.1109\/ACCESS.2022.3162858","volume":"10","author":"Z Shahbazi","year":"2022","unstructured":"Shahbazi Z, Byun YC. Machine Learning-Based analysis of Crypto-currency market financial risk management. Ieee Access. 2022;10:37848\u201356. https:\/\/doi.org\/10.1109\/ACCESS.2022.3162858.","journal-title":"Ieee Access"},{"issue":"1","key":"539_CR4","doi-asserted-by":"publisher","first-page":"769","DOI":"10.2991\/ijcis.d.210203.007","volume":"14","author":"S Hamal","year":"2021","unstructured":"Hamal S, Senvar \u00d6. Comparing performances and effectiveness of machine learning classifiers in detecting financial accounting fraud for Turkish SMEs. Int J Comput Intell Syst. 2021;14(1):769\u201382. https:\/\/doi.org\/10.2991\/ijcis.d.210203.007.","journal-title":"Int J Comput Intell Syst"},{"key":"539_CR5","doi-asserted-by":"publisher","first-page":"112810","DOI":"10.1016\/j.jenvman.2021.112810","volume":"293","author":"J Chen","year":"2021","unstructured":"Chen J, Huang G, Chen W. Towards better flood risk management: assessing flood risk and investigating the potential mechanism based on machine learning models. J Environ Manage. 2021;293:112810. https:\/\/doi.org\/10.1016\/j.jenvman.2021.112810.","journal-title":"J Environ Manage"},{"key":"539_CR6","doi-asserted-by":"publisher","first-page":"39193","DOI":"10.1109\/ACCESS.2021.3060457","volume":"9","author":"F Jamil","year":"2021","unstructured":"Jamil F, Iqbal N, Ahmad S, Kim D. Peer-to-Peer energy trading Mecha-nism based on blockchain and machine learning for sustainable electrical power supply in smart grid. Ieee Access. 2021;9:39193\u2013217. https:\/\/doi.org\/10.1109\/ACCESS.2021.3060457.","journal-title":"Ieee Access"},{"key":"539_CR7","doi-asserted-by":"publisher","first-page":"119386","DOI":"10.1016\/j.jclepro.2019.119386","volume":"249","author":"H Lu","year":"2020","unstructured":"Lu H, Ma X, Huang K, Azimi M. Carbon trading volume and price forecasting in China using multiple machine learning models. J Clean Prod. 2020;249:119386. https:\/\/doi.org\/10.1016\/j.jclepro.2019.119386.","journal-title":"J Clean Prod"},{"key":"539_CR8","doi-asserted-by":"publisher","first-page":"101507","DOI":"10.1016\/j.irfa.2020.101507","volume":"71","author":"A Samitas","year":"2020","unstructured":"Samitas A, Kampouris E, Kenourgios D. Machine learning as an early warning system to predict financial crisis. Int Rev Financial Anal. 2020;71:101507. https:\/\/doi.org\/10.1016\/j.irfa.2020.101507.","journal-title":"Int Rev Financial Anal"},{"issue":"3","key":"539_CR9","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1111\/eufm.12326","volume":"28","author":"S Aziz","year":"2022","unstructured":"Aziz S, Dowling M, Hammami H, Piepenbrink A. Machine learning in finance: A topic modeling approach. Eur Financ Manag. 2022;28(3):744\u201370. https:\/\/doi.org\/10.1111\/eufm.12326.","journal-title":"Eur Financ Manag"},{"issue":"1","key":"539_CR10","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1080\/03085147.2022.2050088","volume":"52","author":"C Borch","year":"2023","unstructured":"Borch C, Min BH. Machine learning and social action in markets: from First-to Second-Generation automated trading. Econ Soc. 2023;52(1):37\u201361. https:\/\/doi.org\/10.1080\/03085147.2022.2050088.","journal-title":"Econ Soc"},{"key":"539_CR11","doi-asserted-by":"publisher","first-page":"101646","DOI":"10.1016\/j.ribaf.2022.101646","volume":"61","author":"S Ahmed","year":"2022","unstructured":"Ahmed S, Alshater MM, Ammari AE, Hammami H. Artificial intelligence and machine learning in finance: A bibliometric review. Res Int Bus Finance. 2022;61:101646. https:\/\/doi.org\/10.1016\/j.ribaf.2022.101646.","journal-title":"Res Int Bus Finance"},{"issue":"2","key":"539_CR12","doi-asserted-by":"publisher","first-page":"967","DOI":"10.3390\/su15020967","volume":"15","author":"MR Maria","year":"2023","unstructured":"Maria MR, Ballini R, Souza RF. Evolution of green finance: A Biblio-metric analysis through complex networks and machine learning. Sustainability. 2023;15(2):967. https:\/\/doi.org\/10.3390\/su15020967.","journal-title":"Sustainability"},{"key":"539_CR13","doi-asserted-by":"publisher","first-page":"125363","DOI":"10.1016\/j.physa.2020.125363","volume":"562","author":"BM Tabak","year":"2021","unstructured":"Tabak BM, Silva TC, Fiche ME, Braz T. Citation likelihood analysis of the interbank financial networks literature: A machine learning and Biblio-metric approach. Physica A. 2021;562:125363. https:\/\/doi.org\/10.1016\/j.physa.2020.125363.","journal-title":"Physica A"},{"key":"539_CR14","doi-asserted-by":"publisher","first-page":"105906","DOI":"10.1016\/j.ijsu.2021.105906","volume":"88","author":"MJ Page","year":"2021","unstructured":"Page MJ, et al. The PRISMA 2020 statement: an updated guideline for Report-ing systematic reviews. Int J Surg. 2021;88:105906. https:\/\/doi.org\/10.1016\/j.ijsu.2021.105906.","journal-title":"Int J Surg"},{"key":"539_CR15","doi-asserted-by":"publisher","unstructured":"Valencia-Arias A, Gonz\u00e1lez-Ruiz JD, Verde Flores L, Vega-Mori L, Rodr\u00ed-guez-Correa P, S\u00e1nchez G, Santos. Machine learning and Block-chain: A bibliometric study on security and privacy. Information. 2024;15(1). https:\/\/doi.org\/10.3390\/info15010065.","DOI":"10.3390\/info15010065"},{"key":"539_CR16","doi-asserted-by":"publisher","first-page":"5113","DOI":"10.1007\/s11192-021-03948-5","volume":"126","author":"VK Singh","year":"2021","unstructured":"Singh VK, Singh P, Karmakar M, Leta J, Mayr P. The journal coverage of web of Science, scopus and dimensions: A comparative analysis. Scientometrics. 2021;126:5113\u201342. https:\/\/doi.org\/10.1007\/s11192-021-03948-5.","journal-title":"Scientometrics"},{"issue":"8","key":"539_CR17","doi-asserted-by":"publisher","first-page":"4741","DOI":"10.3390\/ijerph19084741","volume":"19","author":"J Li","year":"2022","unstructured":"Li J, Mao Y, Ouyang J, Zheng S. A review of urban microclimate Re-search based on CiteSpace and VOSviewer analysis. Int J Environ Res Public Health. 2022;19(8):4741. https:\/\/doi.org\/10.3390\/ijerph19084741.","journal-title":"Int J Environ Res Public Health"},{"issue":"2","key":"539_CR18","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1148\/radiol.09090626","volume":"255","author":"V Durieux","year":"2010","unstructured":"Durieux V, Gevenois PA. Bibliometric indicators: quality measurements of scientific publication. Radiology. 2010;255(2):342\u201351. https:\/\/doi.org\/10.1148\/radiol.09090626.","journal-title":"Radiology"},{"key":"539_CR19","doi-asserted-by":"publisher","unstructured":"Kumar S, Sharma D, Rao S, Lim WM, Mangla SK. Past, Present, and future of sustainable finance: insights from big data analytics through Ma-chine learning of scholarly research. Ann Oper Res. 2022;1\u201344. https:\/\/doi.org\/10.1007\/s10479-021-04410-8.","DOI":"10.1007\/s10479-021-04410-8"},{"issue":"14","key":"539_CR20","doi-asserted-by":"publisher","first-page":"8374","DOI":"10.3390\/su14148374","volume":"14","author":"U Islam","year":"2022","unstructured":"Islam U, et al. Detection of distributed denial of service (DDoS) attacks in IOT based monitoring system of banking sector using machine learning models. Sustainability. 2022;14(14):8374. https:\/\/doi.org\/10.3390\/su14148374.","journal-title":"Sustainability"},{"key":"539_CR21","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s10614-020-10042-0","volume":"57","author":"N Bussmann","year":"2021","unstructured":"Bussmann N, Giudici P, Marinelli D, Papenbrock J. Explainable machine learning in credit risk management. Comput Econ. 2021;57:203\u201316. https:\/\/doi.org\/10.1007\/s10614-020-10042-0.","journal-title":"Comput Econ"},{"key":"539_CR22","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.ijpe.2019.01.032","volume":"211","author":"Y Zhu","year":"2019","unstructured":"Zhu Y, Zhou L, Xie C, Wang GJ, Nguyen TV. Forecasting smes\u2019 credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. Int J Prod Econ. 2019;211:22\u201333. https:\/\/doi.org\/10.1016\/j.ijpe.2019.01.032.","journal-title":"Int J Prod Econ"},{"key":"539_CR23","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s00521-016-2304-x","volume":"28","author":"Y Zhu","year":"2017","unstructured":"Zhu Y, Xie C, Wang GJ, Yan XG. Comparison of Individual, ensemble and integrated ensemble machine learning methods to predict china\u2019s SME credit risk in supply chain finance. Neural Comput Appl. 2017;28:41\u201350. https:\/\/doi.org\/10.1007\/s00521-016-2304-x.","journal-title":"Neural Comput Appl"},{"key":"539_CR24","doi-asserted-by":"publisher","unstructured":"Zhu Y, Xie C, Sun B, Wang GJ, Yan XG. Predicting china\u2019s SME credit risk in supply chain financing by logistic Regression, artificial neural Net-work and hybrid models. Sustainability. 2016;8(5). https:\/\/doi.org\/10.3390\/e18050195.","DOI":"10.3390\/e18050195"},{"issue":"32","key":"539_CR25","doi-asserted-by":"publisher","first-page":"15849","DOI":"10.1073\/pnas.1903070116","volume":"116","author":"M Belkin","year":"2019","unstructured":"Belkin M, Hsu D, Ma S, Mandal S. Reconciling modern Ma-chine-Learning practice and the classical Bias\u2013Variance Trade-Off. Proceed-ings Natl Acad Sci. 2019;116(32):15849\u201354. https:\/\/doi.org\/10.1073\/pnas.1903070116.","journal-title":"Proceed-ings Natl Acad Sci"},{"issue":"2","key":"539_CR26","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1080\/10807030902761056","volume":"15","author":"N Li","year":"2009","unstructured":"Li N, Liang X, Li X, Wang C, Wu DD. Network environment and financial risk using machine learning and sentiment analysis. Hum Ecol Risk Assess. 2009;15(2):227\u201352. https:\/\/doi.org\/10.1080\/10807030902761056.","journal-title":"Hum Ecol Risk Assess"},{"key":"539_CR27","doi-asserted-by":"publisher","unstructured":"Borch C, Hansen KB. Alternative Data and Sentiment Analysis: Pro-specting Non-Standard Data in Machine Learning-Driven Finance, 2022. https:\/\/doi.org\/10.1177\/20539517211070701","DOI":"10.1177\/20539517211070701"},{"key":"539_CR28","doi-asserted-by":"publisher","first-page":"107948","DOI":"10.1016\/j.asoc.2021.10794","volume":"113","author":"L Min","year":"2021","unstructured":"Min L, Dong J, Liu J, Gong X. Robust Mean-Risk portfolio optimization using machine Learning-Based Trade-off parameter. Appl Soft Comput. 2021;113:107948. https:\/\/doi.org\/10.1016\/j.asoc.2021.10794.","journal-title":"Appl Soft Comput"},{"key":"539_CR29","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.eswa.2019.01.012","volume":"124","author":"BM Henrique","year":"2019","unstructured":"Henrique BM, Sobreiro VA, Kimura H. Literature review: machine learning techniques applied to financial market prediction. Expert Syst Appl. 2019;124:226\u201351. https:\/\/doi.org\/10.1016\/j.eswa.2019.01.012.","journal-title":"Expert Syst Appl"},{"key":"539_CR30","doi-asserted-by":"publisher","first-page":"102331","DOI":"10.1109\/ACCESS.2019.2928325","volume":"7","author":"W Xiong","year":"2019","unstructured":"Xiong W, Xiong L. Smart contract based data trading mode using blockchain and machine learning. IEEE Access. 2019;7:102331\u201344. https:\/\/doi.org\/10.1109\/ACCESS.2019.2928325.","journal-title":"IEEE Access"},{"issue":"13","key":"539_CR31","doi-asserted-by":"publisher","first-page":"5317","DOI":"10.3390\/su12135317","volume":"12","author":"CD Lucia","year":"2020","unstructured":"Lucia CD, Pazienza P, Bartlett M. Does good ESG lead to better Fi-nancial performances by firms? Machine learning and logistic regression models of public enterprises in Europe. Sustainability. 2020;12(13):5317. https:\/\/doi.org\/10.3390\/su12135317.","journal-title":"Sustainability"},{"issue":"2","key":"539_CR32","doi-asserted-by":"publisher","first-page":"19","DOI":"10.2308\/ajpt-50009","volume":"30","author":"J Perols","year":"2011","unstructured":"Perols J. Financial statement fraud detection: an analysis of statistical and machine learning algorithms. Auditing: J Pract Theory. 2011;30(2):19\u201350. https:\/\/doi.org\/10.2308\/ajpt-50009.","journal-title":"Auditing: J Pract Theory"},{"key":"539_CR33","doi-asserted-by":"publisher","first-page":"8359","DOI":"10.1007\/s00521-018-3963-6","volume":"31","author":"X Ma","year":"2019","unstructured":"Ma X, Lv S. Financial credit risk prediction in internet finance driven by machine learning. Neural Comput Appl. 2019;31:8359\u201367. https:\/\/doi.org\/10.1007\/s00521-018-3963-6.","journal-title":"Neural Comput Appl"},{"issue":"1","key":"539_CR34","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.jfds.2016.03.002","volume":"2","author":"R Dash","year":"2016","unstructured":"Dash R, Dash PK. A hybrid stock trading framework integrating technical analysis with machine learning techniques. J Finance Data Sci. 2016;2(1):42\u201357. https:\/\/doi.org\/10.1016\/j.jfds.2016.03.002.","journal-title":"J Finance Data Sci"},{"key":"539_CR35","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1007\/s10257-018-0388-9","volume":"18","author":"J Uthayakumar","year":"2020","unstructured":"Uthayakumar J, Metawa N, Shankar K, Lakshmanaprabu SK. Intelligent hybrid model for financial crisis prediction using machine learning techniques. IseB. 2020;18:617\u201345. https:\/\/doi.org\/10.1007\/s10257-018-0388-9.","journal-title":"IseB"},{"key":"539_CR36","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1016\/j.future.2019.07.059","volume":"101","author":"G Baryannis","year":"2019","unstructured":"Baryannis G, Dani S, Antoniou G. Predicting supply chain risks using machine learning: the Trade-off between performance and interpretability. Future Generation Comput Syst. 2019;101:993\u20131004. https:\/\/doi.org\/10.1016\/j.future.2019.07.059.","journal-title":"Future Generation Comput Syst"},{"key":"539_CR37","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.jbusres.2020.10.012","volume":"131","author":"AI Canhoto","year":"2021","unstructured":"Canhoto AI. Leveraging machine learning in the global fight against money laundering and terrorism financing: an affordances perspective. J Bus Res. 2021;131:441\u201352. https:\/\/doi.org\/10.1016\/j.jbusres.2020.10.012.","journal-title":"J Bus Res"},{"key":"539_CR38","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.eswa.2018.08.003","volume":"115","author":"FD Paiva","year":"2019","unstructured":"Paiva FD, Cardoso RTN, Hanaoka GP, Duarte WM. Decision-Making for financial trading: A fusion approach of machine learning and portfolio selection. Expert Syst Appl. 2019;115:635\u201355. https:\/\/doi.org\/10.1016\/j.eswa.2018.08.003.","journal-title":"Expert Syst Appl"},{"key":"539_CR39","doi-asserted-by":"publisher","unstructured":"Rundo F, Trenta F, Stallo AL, Battiato S. Advanced Markov-Based Machine Learning Framework for Making Adaptive Trading System, Computa-tion. 2019;7(1):4, https:\/\/doi.org\/10.3390\/computation7010004","DOI":"10.3390\/computation7010004"},{"key":"539_CR40","doi-asserted-by":"publisher","first-page":"100693","DOI":"10.1016\/j.jfs.2019.100693","volume":"45","author":"J Beutel","year":"2019","unstructured":"Beutel J, List S, Schweinitz G. Does machine learning help Us predict banking crises? J Financial Stab. 2019;45:100693. https:\/\/doi.org\/10.1016\/j.jfs.2019.100693.","journal-title":"J Financial Stab"},{"issue":"8","key":"539_CR41","first-page":"781","volume":"56","author":"P Buryan","year":"2008","unstructured":"Buryan P, Tauser J. Machine learning and exchange rate modelling in international financial management. Ekon Cas. 2008;56(8):781\u201399.","journal-title":"Ekon Cas"},{"key":"539_CR42","doi-asserted-by":"publisher","first-page":"109876","DOI":"10.1016\/j.asoc.2022.109876","volume":"132","author":"TB \u00c7elik","year":"2023","unstructured":"\u00c7elik TB, \u0130can \u00d6, Bulut E. Extending machine learning prediction Ca-pabilities by explainable AI in financial time series prediction. Appl Soft Comput. 2023;132:109876. https:\/\/doi.org\/10.1016\/j.asoc.2022.109876.","journal-title":"Appl Soft Comput"},{"issue":"4","key":"539_CR43","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1177\/1042258720945206","volume":"46","author":"I Blohm","year":"2022","unstructured":"Blohm I, Antretter T, Sir\u00e9n C, Grichnik D, Wincent J. It\u2019sa peoples Game, isn\u2019t It?! A comparison between the investment returns of business angels and machine learning algorithms. Entrepreneurship Theory Pract. 2022;46(4):1054\u201391. https:\/\/doi.org\/10.1177\/1042258720945206.","journal-title":"Entrepreneurship Theory Pract"},{"key":"539_CR44","doi-asserted-by":"publisher","first-page":"109641","DOI":"10.1016\/j.chaos.2020.109641","volume":"133","author":"S Lahmiri","year":"2020","unstructured":"Lahmiri S, Bekiros S. Intelligent forecasting with machine learning trading systems in chaotic intraday bitcoin market. Chaos Solitons Fractals. 2020;133:109641. https:\/\/doi.org\/10.1016\/j.chaos.2020.109641.","journal-title":"Chaos Solitons Fractals"},{"issue":"3","key":"539_CR45","doi-asserted-by":"publisher","first-page":"171","DOI":"10.2174\/2213275911666181012121059","volume":"12","author":"S Gupta","year":"2019","unstructured":"Gupta S, Saxena A. Classification of operational and financial variables affecting the bullwhip effect in Indian sectors: A machine learning approach. Recent Pat Comput Sci. 2019;12(3):171\u20139. https:\/\/doi.org\/10.2174\/2213275911666181012121059.","journal-title":"Recent Pat Comput Sci"},{"key":"539_CR46","doi-asserted-by":"crossref","unstructured":"Ziora L. Machine-learning solutions in the management of a contemporary business organisation: a case study approach in a banking sector. in Rational decisions in organisations. Auerbach; 2022. pp. 189\u2013201.","DOI":"10.1201\/9781003030966-14"},{"key":"539_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-3787-8","author":"P Mathur","year":"2018","unstructured":"Mathur P. Machine learning applications using python: cases studies from healthcare, retail, and finance. Apress. 2018. https:\/\/doi.org\/10.1007\/978-1-4842-3787-8.","journal-title":"Apress"},{"issue":"1","key":"539_CR48","first-page":"1","volume":"7","author":"SI Abir","year":"2025","unstructured":"Abir SI, et al. Deep learning for financial markets: A Case-Based analysis of BRICS nations in the era of intelligent forecasting. J Econ Finance Acc Stud. 2025;7(1):1\u201315.","journal-title":"J Econ Finance Acc Stud"},{"key":"539_CR49","doi-asserted-by":"publisher","unstructured":"Zhu X, et al. RMER-DT: robust multimodal emotion recognition in conversational contexts based on diffusion and Transformers. Inform Fusion. 2025;103268. https:\/\/doi.org\/10.1016\/j.inffus.2025.103268.","DOI":"10.1016\/j.inffus.2025.103268"},{"key":"539_CR50","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2025.3572495","author":"R Wang","year":"2025","unstructured":"Wang R, Guo C, Shabaz M, Rida I, Cambria E, Zhu X. CIME: contextual Interaction-based multimodal emotion analysis with enhanced semantic information. IEEE Trans Comput Soc Syst. 2025. https:\/\/doi.org\/10.1109\/TCSS.2025.3572495.","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"539_CR51","doi-asserted-by":"publisher","unstructured":"Zheng J et al. Apr., Dynamic Spectral Graph Anomaly Detection, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 12, pp. 13410\u201313418, 2025, https:\/\/doi.org\/10.1609\/aaai.v39i12.33464","DOI":"10.1609\/aaai.v39i12.33464"},{"issue":"4","key":"539_CR52","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1007\/s10614-021-10135-4","volume":"60","author":"Y Song","year":"2022","unstructured":"Song Y, Wu R. The impact of financial enterprises\u2019 excessive financial-ization risk assessment for risk control based on data mining and machine learning. Comput Econ. 2022;60(4):1245\u201367. https:\/\/doi.org\/10.1007\/s10614-021-10135-4.","journal-title":"Comput Econ"},{"issue":"21","key":"539_CR53","doi-asserted-by":"publisher","first-page":"13875","DOI":"10.3390\/su142113875","volume":"14","author":"S Kumar","year":"2022","unstructured":"Kumar S, et al. Exploitation of machine learning algorithms for detecting financial crimes based on customers. Behav Sustain. 2022;14(21):13875. https:\/\/doi.org\/10.3390\/su142113875.","journal-title":"Behav Sustain"},{"key":"539_CR54","doi-asserted-by":"crossref","unstructured":"Mankad S, Michailidis G, Kirilenko A. Discovering the ecosystem of an electronic financial market with a dynamic Machine-Learning method. Algo-rithmic Finance, 2, 2, pp. 151\u201365 10 3233 \u2013\u200913023, 2013.","DOI":"10.3233\/AF-13023"},{"issue":"1","key":"539_CR55","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1080\/2573234X.2021.1955022","volume":"5","author":"M Palmer","year":"2022","unstructured":"Palmer M, Roeder J, Muntermann J. Induction of a sentiment dictionary for financial analyst communication: A Data-Driven approach balancing Ma-chine learning and human intuition. J Bus Analytics. 2022;5(1):8\u201328. https:\/\/doi.org\/10.1080\/2573234X.2021.1955022.","journal-title":"J Bus Analytics"},{"key":"539_CR56","doi-asserted-by":"publisher","first-page":"106187","DOI":"10.1016\/j.asoc.2020.106187","volume":"90","author":"TA Borges","year":"2020","unstructured":"Borges TA, Neves RF. Ensemble of machine learning algorithms for cryptocurrency investment with different data resampling methods. Appl Soft Comput. 2020;90:106187. https:\/\/doi.org\/10.1016\/j.asoc.2020.106187.","journal-title":"Appl Soft Comput"},{"key":"539_CR57","doi-asserted-by":"publisher","unstructured":"Piovezan RPB, Junior PPA, \u00c1vila SL. Machine learning method for return direction forecast of exchange traded funds (ETFs) using classification and regression models. Comput Econ. 2023;1\u201326. https:\/\/doi.org\/10.1007\/s10614-023-10385-4.","DOI":"10.1007\/s10614-023-10385-4"},{"issue":"7","key":"539_CR58","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.3390\/en12071249","volume":"12","author":"KY Bae","year":"2019","unstructured":"Bae KY, Jang HS, Jung BC, Sung DK. Effect of prediction error of machine learning schemes on photovoltaic power trading based on energy storage systems. Energies (Basel). 2019;12(7):1249. https:\/\/doi.org\/10.3390\/en12071249.","journal-title":"Energies (Basel)"},{"issue":"4","key":"539_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3488378","volume":"16","author":"C Liu","year":"2022","unstructured":"Liu C, Yan J, Guo F, Guo M. Forecasting the market with machine learning algorithms: an application of NMC-BERT-LSTM-DQN-X algorithm in quantitative trading. ACM Trans. 2022;16(4):1\u201322. https:\/\/doi.org\/10.1145\/3488378.","journal-title":"ACM Trans"},{"issue":"3","key":"539_CR60","doi-asserted-by":"publisher","first-page":"251","DOI":"10.3844\/jcssp.2021.251.264","volume":"17","author":"F Kamalov","year":"2021","unstructured":"Kamalov F, Gurrib I, Rajab K, Kamalov F, Gurrib I, Rajab K. Financial forecasting with machine learning: price vs return. J Comput Sci. 2021;17(3):251\u201364. https:\/\/doi.org\/10.3844\/jcssp.2021.251.264.","journal-title":"J Comput Sci"},{"issue":"1","key":"539_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40854-020-00217-x","volume":"7","author":"H Sebasti\u00e3o","year":"2021","unstructured":"Sebasti\u00e3o H, Godinho P. Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financial Innov. 2021;7(1):1\u201330. https:\/\/doi.org\/10.1186\/s40854-020-00217-x.","journal-title":"Financial Innov"},{"key":"539_CR62","doi-asserted-by":"publisher","first-page":"5574","DOI":"10.3390\/app9245574","volume":"9","author":"F Rundo","year":"2019","unstructured":"Rundo F. Machine learning for quantitative finance applications: A survey. Appl Sci. 2019;9:5574. https:\/\/doi.org\/10.3390\/app9245574.","journal-title":"Appl Sci"},{"key":"539_CR63","doi-asserted-by":"publisher","first-page":"105779","DOI":"10.1016\/j.asoc.2019.105779","volume":"86","author":"TH Chen","year":"2020","unstructured":"Chen TH. Do you know your customer? Bank risk assessment based on machine learning. Appl Soft Comput. 2020;86:105779. https:\/\/doi.org\/10.1016\/j.asoc.2019.105779.","journal-title":"Appl Soft Comput"},{"issue":"10","key":"539_CR64","doi-asserted-by":"publisher","first-page":"6594","DOI":"10.1109\/TII.2020.3045011","volume":"17","author":"D Said","year":"2020","unstructured":"Said D. A decentralized electricity trading framework (DETF) for connected evs: A blockchain and machine learning for profit margin optimization. IEEE Trans Industr Inf. 2020;17(10):6594\u2013602. https:\/\/doi.org\/10.1109\/TII.2020.3045011.","journal-title":"IEEE Trans Industr Inf"},{"key":"539_CR65","unstructured":"Sakhare NN, Imambi SS. Technical Analysis Based Prediction of Stock Market Trading Strategies Using Deep Learning and Machine Learning Algo-rithms, International Journal of Intelligent Systems and Applications in Engi-neering. 2022;10(3):411421.https:\/\/orcid.org\/0000-0002-1748-799X."},{"key":"539_CR66","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.ins.2019.02.030","volume":"485","author":"I Gonz\u00e1lez-Carrasco","year":"2019","unstructured":"Gonz\u00e1lez-Carrasco I, Jim\u00e9nez-M\u00e1rquez JL, L\u00f3pez-Cuadrado JL, Ruiz-Mezcua B. Automatic detection of relationships between banking operations using machine learning. Inf Sci (N Y). 2019;485:319\u201346. https:\/\/doi.org\/10.1016\/j.ins.2019.02.030.","journal-title":"Inf Sci (N Y)"},{"key":"539_CR67","doi-asserted-by":"publisher","first-page":"3034","DOI":"10.1109\/ACCESS.2022.3232287","volume":"11","author":"SK Hashemi","year":"2022","unstructured":"Hashemi SK, Mirtaheri SL, Greco S. Fraud detection in banking data by machine learning techniques. IEEE Access. 2022;11:3034\u201343. https:\/\/doi.org\/10.1109\/ACCESS.2022.3232287.","journal-title":"IEEE Access"},{"issue":"11","key":"539_CR68","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.5373\/JARDCS\/V11SP11\/20193134","volume":"11","author":"N Hamdoun","year":"2019","unstructured":"Hamdoun N, Rguibi K. Impact of Ai and machine learning on financial industry: application on Moroccan credit risk scoring. J Adv Res Dyn Control Syst. 2019;11(11):1041\u20138. https:\/\/doi.org\/10.5373\/JARDCS\/V11SP11\/20193134.","journal-title":"J Adv Res Dyn Control Syst"},{"key":"539_CR69","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/j.eneco.2019.05.006","volume":"81","author":"H Ghoddusi","year":"2019","unstructured":"Ghoddusi H, Creamer GG, Rafizadeh N. Machine learning in energy economics and finance: A review. Energy Econ. 2019;81:709\u201327. https:\/\/doi.org\/10.1016\/j.eneco.2019.05.006.","journal-title":"Energy Econ"},{"issue":"3","key":"539_CR70","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1016\/j.ijforecast.2019.11.005","volume":"36","author":"A Petropoulos","year":"2020","unstructured":"Petropoulos A, Siakoulis V, Stavroulakis E, Vlachogiannakis NE. Pre-dicting bank insolvencies using machine learning techniques. Int J Forecast. 2020;36(3):1092\u2013113. https:\/\/doi.org\/10.1016\/j.ijforecast.2019.11.005.","journal-title":"Int J Forecast"},{"key":"539_CR71","doi-asserted-by":"crossref","unstructured":"Pattnaik D, Ray S, Raman R. Applications of artificial intelligence and machine learning in the financial services industry: A bibliometric review. Heliyon, 10, 1, 2024.","DOI":"10.1016\/j.heliyon.2023.e23492"},{"key":"539_CR72","doi-asserted-by":"publisher","unstructured":"Yu TR, Song X. Big data and artificial intelligence in the banking industry, In: Handbook of financial econometrics, statistics, technology, and risk management: (In, vol. 4, 2025, pp. 3841\u20133857. https:\/\/doi.org\/10.1142\/9789819809950_0117","DOI":"10.1142\/9789819809950_0117"},{"key":"539_CR73","doi-asserted-by":"publisher","unstructured":"Kanaparthi V. Transformational application of artificial intelligence and machine learning in financial technologies and financial services: A bibliometric review, 2024. https:\/\/doi.org\/10.48550\/arXiv.2401.15710","DOI":"10.48550\/arXiv.2401.15710"},{"issue":"2","key":"539_CR74","doi-asserted-by":"publisher","first-page":"51","DOI":"10.5281\/zenodo.12826933","volume":"1","author":"M Paramesha","year":"2024","unstructured":"Paramesha M, Rane NL, Rane J. Artificial intelligence, machine learning, deep learning, and blockchain in financial and banking services: A comprehensive review. Partners Univers Multidisciplinary Res J. 2024;1(2):51\u201367. https:\/\/doi.org\/10.5281\/zenodo.12826933.","journal-title":"Partners Univers Multidisciplinary Res J"},{"key":"539_CR75","doi-asserted-by":"publisher","first-page":"100772","DOI":"10.1016\/j.entcom.2024.100772","volume":"52","author":"C Wenjing","year":"2025","unstructured":"Wenjing C. Simulation application of virtual robots and artificial intelligence based on deep learning in enterprise financial systems. Entertain Comput. 2025;52:100772. https:\/\/doi.org\/10.1016\/j.entcom.2024.100772.","journal-title":"Entertain Comput"},{"issue":"2","key":"539_CR76","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1108\/JFRA-09-2023-0544","volume":"23","author":"SE Mohsen","year":"2025","unstructured":"Mohsen SE, Hamdan A, Shoaib HM. Digital transformation and integration of artificial intelligence in financial institutions. J Financial Report Acc. 2025;23(2):680\u201399. https:\/\/doi.org\/10.1108\/JFRA-09-2023-0544.","journal-title":"J Financial Report Acc"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00539-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00539-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00539-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T07:34:00Z","timestamp":1761118440000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00539-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,22]]},"references-count":76,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["539"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00539-8","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,22]]},"assertion":[{"value":"21 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"280"}}