We aim to improve the work efficiency and work processes of legal departments after hearing their plight from our legal friend.

What it does

Our team’s solution is aimed at improving the efficiency of lawyers’ work processes when drafting the Management’s Discussion and Analysis (“MD&A”) section in the prospectus in Capital Market US-related deals (SMU LIT Hackathon Problem Statement, 2022).

This is done by automating the process of extracting relevant economic data from financial statements and drafting excerpts of the MD&A section for the lawyer. By doing so, lawyers can spend minimal time on formulaic data entry work and instead, devote their time to considering the legal issues that their clients may face. Further, the risk of human error from copy-pasting and manual calculations would also be mitigated, if not fully removed.

Lawyers can use our solution by simply uploading a PDF copy of the relevant financial statements on our designated website, together with keywords provided by the user. Statements for the MD&A section will be generated automatically. Thereafter, the lawyer can make any necessary amendments or additions, such as to the possible reasons for the period-on-period changes, in finalising the MD&A into the prospectus that they were working on. Additionally, given that company financial statements are likely to contain sensitive data, our solution provides for data security by providing an option for lawyers to set a password when uploading financial statements, which would later be required to access the generated excerpts of the MD&A.

In addition, we have retrieved sample financial statements online for sample documents to test out our codes. May refer to the samples attached to our GitHub repository.

Alternatively, the data can also be accessed via a RESTFUL API for this solution to be easily integrated into the existing workflows of legal entities working with other third-party service providers.

How we built it

We developed this solution using Next.js for the website while using Flask to serve as our backend to process and analyze submitted API files.

Challenges we ran into

A significant challenge we faced was coordinating meeting times for our team to sync up and keep track of each others' progress.

Accomplishments that we're proud of

Working together as a team to develop a full-stack application from scratch, with a team consisting of various skill levels.

What we learned

We learned how to manage our own expectations and balance the load of developing our project. We also gained more experience from working on a coding project as a team.

What's next for Hotel Trivia Go

We aim to potentially expand this project to utilize Artificial Intelligence (AI) and Machine Learning (ML) to produce more accurate results and the ability to process more complex (unstructured) data types.

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