Machine Learning: The Backbone of Intelligent Trade Credit-Based Systems. (8th March 2022)

• Record Type: Journal Article
• Authors: Shah, Faiza; Liu, Yumin; Anwar, Aamir; Shah, Yasir; Alroobaea, Roobaea; Hussain, Saddam; Ullah, Syed Sajid
• Other Names: Chen Chin-Ling Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-03-08
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/7149902
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Technology has turned into a significant differentiator in the money and traditional recordkeeping systems for the financial industry. To depict two customers as potential investors, it is mandatory to give the complex innovation that they anticipate and urge to purchase. In any case, it is difficult to keep on top of and be a specialist in each of the new advancements that are accessible. By reappropriating IT administrations, monetary administrations firms can acquire prompt admittance to the most recent ability and direction. Financial systems, along with machine learning (ML) algorithms, are vital for critical concerns like secure financial transactions and automated trading. These are the key to the provision of financial decisions for investors and stakeholders for the firms which are working with the trade credit (TC) approach, in Small and Medium Industries (SMEs). Huge and very sensitive data is processed in a limited time. The trade credit is a reason for more financial gains. The impact of TC with predictive machine learning algorithms is the reason why intelligent and safe revenue generation is the main target of the proposed study. That is, the combination of financial data and technology (FinTech) domains is a potential reason for sales growth and ultimately more profit.