{"title":"AI-Driven Risk Management Strategies in Financial Technology","authors":"Harsh Daiya","doi":"10.60087/jaigs.v5i1.194","DOIUrl":null,"url":null,"abstract":"The integration of Artificial Intelligence (AI) into financial technology (FinTech) has revolutionized risk management strategies, offering innovative solutions to longstanding challenges. This paper explores the transformative potential of AI-driven risk management in the financial sector, focusing on predictive accuracy, fraud detection, and regulatory compliance. Employing a mixed-methods approach, the study combines quantitative data from surveys and questionnaires with qualitative insights from interviews and case studies. The findings highlight AI's ability to enhance risk assessment, improve fraud prevention, and optimize compliance processes, thereby creating a more secure and efficient financial environment. Despite the significant benefits, the study also identifies challenges, including regulatory adaptation and ethical considerations. The research concludes with recommendations for stakeholders to effectively implement AI-driven risk management strategies, ensuring a balance between innovation and security.","PeriodicalId":517201,"journal":{"name":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","volume":"88 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.60087/jaigs.v5i1.194","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The integration of Artificial Intelligence (AI) into financial technology (FinTech) has revolutionized risk management strategies, offering innovative solutions to longstanding challenges. This paper explores the transformative potential of AI-driven risk management in the financial sector, focusing on predictive accuracy, fraud detection, and regulatory compliance. Employing a mixed-methods approach, the study combines quantitative data from surveys and questionnaires with qualitative insights from interviews and case studies. The findings highlight AI's ability to enhance risk assessment, improve fraud prevention, and optimize compliance processes, thereby creating a more secure and efficient financial environment. Despite the significant benefits, the study also identifies challenges, including regulatory adaptation and ethical considerations. The research concludes with recommendations for stakeholders to effectively implement AI-driven risk management strategies, ensuring a balance between innovation and security.