From personal experience working with grassroots NGOs in developing countries, one of the main problems identified is the difficulty to obtain funding to ensure sustainability and scalability of their programs. We realised that a key issue at hand is that international donors have problems trusting these NGOs' use of donated monies as financial statements are unclear and usually not compliant with International Financial Reporting Standards (IFRS). This creates a vicious cycle as unclear financial statements reduce donors' willingness to provide funds, and the lack of funds causes NGOs not to invest in their finance functions.
What it does
A platform where "unclean" financial statements can be fed into the system along with some qualitative data, and outputs a simple, "clean" income statement for the donor to easily read and comprehend. This will allow the donor to have a true and fair representation of the NGO's financials, with minimal effort from both parties.
How we built it
Using Angular, we created a web interface where users can input the NGO's balance sheet and income statement (source of financial information). We then used Python to extract, process and rearrange input financial data into categories compliant with IFRS. We then used Flask to push the categories back into the web interface and used Angular to present the financial information in a manner that is actually useful to the user.
Challenges we ran into
We spent six hours working on a different idea with Amazon Lex, but realised that it lacked a key functionality that was essential to our project, so we had to start over with a completely new project.
Many of us were new to Python, so it was a bit of a learning curve to learn the language. Financial statements provided by NGOs in developing countries tend to lack large amounts of past and present data, so we had to use assumptions to decide how asset and liability classes can be appropriately classified. We had to learn Flask from scratch.
Accomplishments that we're proud of
With 3/4 of the group not financially trained, we still managed to understand and manipulate financial data in accordance with international accounting standards.
What we learned
We learned that we need to fully assess the tools we are planning to work with to ensure feasibility before starting work on a project.
What's next for FinanceAoun
Scale the project to produce more useful financial statements. We can use NLP to make the categorization of categories more accurate.