Insights and Recommendation Extraction using advances in Large Language Modeling

Businesses allocate a significant amount of financial resources and time to produce regular financial and managerial reports,
which involve analyzing accounting data. These reports play a vital role in evaluating and comprehending the overall
performance of the business, thereby aiding in making important decisions. Additionally, businesses often engage industry
experts or consultants to derive actionable recommendations and strategies based on the findings from the analysis. The
ultimate goal is to deliver these recommendations in a clear and easily understandable format, typically using a SaaS model
application. Automating this entire process comes with several benefits, such as enhancing operational efficiency, reducing
the likelihood of manual errors, and ultimately leading to cost savings. This project is specifically focused on extracting and
generating meaningful business recommendations using interpretable Large Language Models (LLMs). These LLMs are
sophisticated language models that can process and understand vast amounts of data. Ensuring the accuracy and
effectiveness of these generated recommendations is critical to maintaining the quality of the automatically generated
reports. The project aims to revolutionize the way businesses perform analysis and make decisions. It has the potential to
transform the current landscape of analysis and decision-making processes, making them more data-driven, efficient, and
precise.

Faculty Supervisor:

Rasha Kashef

Student:

Partner:

websiteTOON digital

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Toronto Metropolitan University

Program: