Enhancing Syndicate Lending Through AI-Powered Fine-Tuning: Leveraging LLAMA2 for Dynamic Decision Support

Abstract

This electronic document describes the involvement of finance agencies and various types of corporate industries through “syndicated loans” that simplify financial transactions for both agencies and corporations. This will be a revolutionary step towards the ‘fintech’ sector as it is all about decentralization and how it changes the whole game during the lending process. This paper describes a proposed process of lending, monitoring, evaluation, and development in the lending process between borrowers (enterprises) and lenders using Large Language Model. After highlighting the difficulties in implementing this proposal, the paper also discusses its impact on the transition process and outlines what has already been done. The proposal increases the transparency of the entire process among stakeholders, simplifies manual work, reduces operational costs, and provides data to support the analysis and summarization of benefits derived from the lending process. A secure system offers the ease of use and dexterity of the technology that makes the agenda highly reliable in all aspects of test cases. The study includes the experimentation of proposed system that how it reduces the failure rate of startups by 70% by providing financial aids to startups and suggesting a better business model if needed.