Artificial intelligence applications in finance: a survey

IF 3.6 2区管理学 Q2 BUSINESS

Journal of Management Analytics Pub Date : 2023-08-10 DOI:10.1080/23270012.2023.2244503

Xuemei Li, Alexander Sigov, Leonid Ratkin, Leonid A. Ivanov, Ling Li

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引用次数: 0

Abstract

AbstractFinance is in our daily life. We invest, borrow, lend, budget, and save money. Finance also provides guidelines for corporation and government spending and revenue collection. Traditional statistical solutions such as regression, PCA, and CFA have been widely used in financial forecasting and analysis. With the increasing interest in artificial intelligence in recent years, this paper reviews the Artificial Intelligence (AI) techniques in the finance domain systematically and attempts to identify the current AI technologies used, major applications, challenges, and trends in Finance. It explores AI-related articles in Finance in IEEE Xplore and EI compendex databases. Findings suggest AI has been engaged in Finance in financial forecasting, financial protection, and financial analysis and decision-making areas. Financial forecasting is one of the main sub-fields of Finance affected by AI technology. Major AI technology used is the supervised learning. Deep learning has gained popular in recent years. AI could be used to address some emerging topics.Keywords: machine learning; artificial intelligencefinance Disclosure statementNo potential conflict of interest was reported by the author(s).

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人工智能在金融中的应用:一项调查

摘要金融在我们的日常生活中无处不在。我们投资、借贷、做预算、存钱。财务还为公司和政府的支出和收入提供指导方针。传统的统计方法如回归、主成分分析和CFA等在财务预测和分析中得到了广泛的应用。随着近年来人们对人工智能的兴趣日益浓厚，本文系统地回顾了人工智能(AI)技术在金融领域的应用，并试图识别当前人工智能技术在金融领域的使用、主要应用、挑战和趋势。它在IEEE explore和EI compendex数据库中探索金融领域与人工智能相关的文章。研究结果表明，人工智能在金融预测、金融保护、金融分析和决策等领域已经涉足金融领域。金融预测是受人工智能技术影响的主要金融子领域之一。使用的主要人工智能技术是监督学习。近年来，深度学习越来越受欢迎。人工智能可以用来解决一些新兴的话题。关键词:机器学习;披露声明作者未报告潜在的利益冲突。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Management Analytics SOCIAL SCIENCES, MATHEMATICAL METHODS-

CiteScore

13.30

自引率

3.40%

发文量

期刊介绍： The Journal of Management Analytics (JMA) is dedicated to advancing the theory and application of data analytics in traditional business fields. It focuses on the intersection of data analytics with key disciplines such as accounting, finance, management, marketing, production/operations management, and supply chain management. JMA is particularly interested in research that explores the interface between data analytics and these business areas. The journal welcomes studies employing a range of research methods, including empirical research, big data analytics, data science, operations research, management science, decision science, and simulation modeling.