Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM27483
Volume:
7 |
12 |
Dec 2023
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The financial technology (FinTech) sector has experienced rapid growth, and with it, the increasing complexity and volume of financial transactions. Fraud detection and risk management are critical challenges for financial institutions, as cyber threats continue to evolve. The integration of Artificial Intelligence (AI) into financial systems offers promising solutions for automating these processes, improving their accuracy and efficiency. This paper explores the potential of AI-driven automation in transforming fraud detection and risk management practices within FinTech. The research examines existing literature, highlights key developments in AI technology, and evaluates the effectiveness of AI models in detecting fraudulent activities and managing financial risk. The study presents a comparative analysis of traditional versus AI-based fraud detection methods, providing evidence of the potential benefits and challenges of AI integration. The findings suggest that AI can significantly enhance fraud detection accuracy, reduce response times, and help institutions manage financial risks proactively. However, issues related to data privacy, algorithmic transparency, and regulatory compliance present challenges that require further exploration. The paper concludes by recommending future research directions and emphasizing the importance of a collaborative approach between AI developers, financial institutions, and regulatory bodies to address these challenges.
KEYWORDS
Publication Date
12/27/2023
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
14
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...