Katapult Accelerator is a global accelerator that aims to fast-track solutions to global problems within both environmental and societal domains. 50 technology changemakers built an MVP that helps investors to identify the best startups to invest in to maximize job creation, generate more equal societies, and provide opportunities for people left behind. 

The team used data collection, data analysis, and predictive modeling in this challenge.

This project is open-sourced, code is shared on Omdena GitHub.

The problem

There are two main problem angles that the challenge addressed:

• Research: while there is research on the purely financial factors in startup success, the research on early-stage startups is sparse (especially in the first two years after founding). This is because startups generally won’t have much financial information available or won’t have generated large enough sums of money for financial data to be of much value in terms of evaluation success. While there is some qualitative research data available, this data is not particularly suitable for investors looking to make wise decisions. 
• Startup Success Prediction & Investor Confidence: The market for environmentally-friendly investments is rapidly growing. Unfortunately, many of the wealthiest investors are often reluctant to invest in startups – especially impact startups – due to the lack of relevant research available, the higher risks involved, and the lack of business history on which future successes can be predicted. Investing in startups is globally understood to be risky and not an optimal asset management strategy. However, there are plenty of startups that have tremendous potential, but they may lose out on funding and success due to this bias and lack of insight, trust, and understanding of startup investing. If a project doesn’t already have funding, it becomes difficult to raise new funding.

Having a tool that is tailored to startups and that can predict their future successes on more than just financial variables would be invaluable to attract more investor capital to impact startups with great potential. And here came the project’s outcomes.

Solutions

In this two-month challenge, Omdena collaborators built models that predict the economic impact of startup investment, based on past historic investments. The challenge looked at historic investments and analyzed what factors made a good investment and how big the impact was. It also looked at the other socio-economic factors that may affect an investment.

Outcomes

• A list of non-financial factors divided into segments that can be publicly curated using data collection bots, 
• And a supplemental list of specific questions that can be manually responded to by a startup.
• An MVP tool that is able to predict a startup’s success based on inputs 1 and 2

Either one of the above may be utilized towards success prediction, while using all, would help to boost the AI model’s confidence score.  

The project’s final presentation

Potential impact

The opportunities that arise from the MVP and future version of the product are manifold:

• A fairer, more objective, and faster funding allocation system based on quantifiable factors and data, thus eliminating possible investor bias factors and more equality.
• The chances of startup success greatly improve, which benefits investors, entrepreneurs, startup employees, customers, and governments.
• Startups have more time to invest in R&D and other foundation-laying activities without having to immediately worry about generating profit, seeing as they can trust the AI success prediction instead of having to prove their worth early on in a financial way.
• The startup scene is very diverse and often includes employees from all over the world. Funding more startups and more successful startups results in an international atmosphere, the exchange of knowledge and skills, and more entrepreneurial freedom (No author, 2020).
• By enabling startups to attract enough funding, startups can avoid being acquired by larger, more established companies which then reduce the impact of the startups (Corl, 2014).