Research on corporate finance credit risk method based on LSTM

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dc.contributor.authorWu Y.
dc.date.accessioned2025-04-01T06:20:41Z
dc.date.available2025-04-01T06:20:41Z
dc.date.issued2024
dc.description.abstract© 2024 Copyright held by the owner/author(s).As the financial and technology fields are continuously developed in the modern days, FinTech is also quickly developed as an emerging field. Under this background, more and more issues have occurred, in particular, the increased complexity of corporate credit risk management for credit institutions. The traditional methods of risk management cannot satisfy the need of the modern financial industry to control risks efficiently and accurately, for instance, through the use of static blacklists and whitelists. Therefore, the utilization of Large-Scale Artificial Intelligence in credit risk management has become a new trend. The goal of our research is to discover a new appropriate predict approach, which firstly reviews the limitations of the available risk management methods, which are unable to cope with the quickly growing market demand for online financial services and are inadequate in assessing the comprehensive risk of enterprises. To solve these problems, the new Long Short-Term Memory Network is proposed to adopt for credit risk management. The method helps to conduct a comprehensive and accurate analysis on multi-dimensional corporate data through deep learning to assess enterprise risk from business and development aspects. Besides, this research is empirical that could be successfully simulated into data in loaning scenes, proves the new method could handle the risk events and improve the accurate of risk control, raises the facts that bankers are highly efficient in credit approval process. The data of the experiment reveals that the application of the LSTM model in predicting and managing asset financial credit risks still has some apparent advantages, and the practicability is very high. The related research outcomes provide a new high-load risk management tool for the FinTech field, which is of great significance for promoting the innovation and development of the financial industry.
dc.description.firstpage204
dc.description.lastpage210
dc.identifier.doi10.1145/3700058.3700091
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/40398
dc.relation.ispartofProceedings of International Conference on Digital Economy, Blockchain and Artificial Intelligence, DEBAI 2024
dc.rightsAcesso Restrito
dc.subject.otherlanguageAI
dc.subject.otherlanguagecredit risk management
dc.subject.otherlanguageFintech
dc.subject.otherlanguageLSTM
dc.subject.otherlanguageMachine learning
dc.titleResearch on corporate finance credit risk method based on LSTM
dc.typeArtigo de evento
local.scopus.citations0
local.scopus.eid2-s2.0-85216082195
local.scopus.subjectCorporate finance
local.scopus.subjectCorporates
local.scopus.subjectCredit risk management
local.scopus.subjectCredit risks
local.scopus.subjectFinancial industry
local.scopus.subjectLarge-scales
local.scopus.subjectLSTM
local.scopus.subjectMachine-learning
local.scopus.subjectRisks management
local.scopus.subjectTechnology fields
local.scopus.updated2025-04-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85216082195&origin=inward
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