An Expert Interview: Liberalizing Data and Increasing Liquidity in the Social Capital Market through Technology
The following interview with Barnaby Nelson (The Value Exchange) & Franziska Reh (Uncap – Unconventional Capital) occurred as a follow-up to the ‘Innovative Financing for Social Entrepreneurs‘ round table discussions in Cairo.
SIEMENS STIFTUNG: One of the big issues that we identified during the round table session is the funding gap when it comes to financing social enterprises. From your point of view, what is the role that technology can play and in which areas do you see high potential?
If you are an impact investor sitting somewhere in Germany, how would you find an entrepreneur somewhere for example in South Africa, for example, who is not large enough to be on the radar of anyone else in the market, of big accelerators, foundations etc. Again technology, in the form of platforms, for instance, can to some extent bring those parties together automatically.
BARNABY: I agree completely. If you look at those two basic problems – the big ticket sizes and the difficulties in finding each other – for me the big question is, how do you create a platform that addresses these issues without creating more complexity? How do you do something that slots in very seamlessly to the way people actually work or want to work? Solutions need to fit in with the workflow of the investor, but equally you have to fit in the workflow of the social entrepreneur, which actually is most likely a little harder. If done well, technology can facilitate exchange, bring the deal together, reduce the lack of transparency, and increase connectivity.
SIEMENS STIFTUNG: There was a strong consensus on the argument that technology should be seen as a means to an end. When you mentioned the aspect of reducing the cost for due diligence, what could a concrete solution look like?
FRANZISKA: When we talk about early-stage investing, the most important thing an investor wants to know is: is this a good entrepreneur? I think that notion accounts for 80% of the due diligence. The other 20% relates to questions like: does this idea in general make sense in that market? But as we all know, the idea that you start with in year one is probably not the idea that will generate your revenues in year five. So the question is: how can we bring down the costs of assessing whether someone is a good entrepreneur or not? Usually investors would meet an entrepreneur, learn about their journey, and make a decision based on their gut instincts, their experience, and some key indicators. That might work well, but it definitely does not scale. Technologies like psychometric assessments powered by artificial intelligence can actually help to automate this due diligence process. We are currently testing assessment tools that screen for entrepreneurial potential. And the good thing is that it also reduces the human bias. Investors have their own understanding of what makes a good entrepreneur. But we can see that entrepreneurship works differently in Europe as it does in Africa or Asia, for instance. There are dynamics that we can ́t even understand from the outside. But, in order to use those AI-powered tests, we need better, less biased data, based on local datasets. For example, most of the currently existing data sets will probably tell you that 80% of successful entrepreneurs are white, male, and went to university.
BARNABY: Coming from a banking background, one additional thing that I see is the point of knowing your social entrepreneur. There is a transformation going on in the world of banking where investors are moving away from balance sheet reporting and credit analysis to much more of a transactional analysis. I think this is something aspirational we may use in this space as well. As Franziska has said, it comes down to getting to know individuals in a much more statistical quantitative way that removes personal bias. But also when you are loo-king at an organization that is asking for a decent amount of funding, that means they have been running for a certain amount of time. If you can take the daily behavior they have built up over the last 2 years, including their cash flows and the way they properly manage their business, and transform it into statistical information, that could form parts of the due diligence process.
Someone signals to those investors that there may be a good entrepreneur, but then even they would go back and do their own due diligence. I think, opening up data (publishing due diligence and sharing the costs on certain parts through an intelligent system) would already make trusting third-party due diligence more likely.
BARNABY: Right, but this is where we need to be careful in not creating more work for everyone. The risk here is that basically being “found” comes at a cost premium for the social entrepreneur. While this might be a necessary cost, at the end of the day, it is a necessary evil to go out to find funding. It is just time you could be spending saving two hundred billion tons of grain from getting ruined by moisture, for example. So there is an opportunity cost, and in the commercial sector this is a question of more or less profit, but in social development it means more or less impact; as a sector we have to make sure we are not self-defeating.
SIEMENS STIFTUNG: Barnaby, previous to this talk you mentioned the idea of having open source platforms for due diligence. How could this look like?
BARNABY: I have put a lot of thought into that and I think you could look at the workflows of everyone today. Basically, you need to find a way of sourcing most data from within the existing workflow by hanging that on the software systems the two sides use. For instance, social entrepreneurs need to keep their accounts. Whenever they use this data, part of it can be transferred to impact investors in a neutralized way to fit their due diligence requirements.
FRANZISKA: Right, and it could go even further. We are currently looking at accounting tools that do two things at the same time. First of all, they nudge entrepreneurs to do proper accounting and show them how to do it, but at the same time, the tools could also provide direct access to their data. At some point you could create a benchmark powered through artificial intelligence and communicate directly with the entrepreneur: “compared to your peers that are also doing irrigation systems in Ethiopia, your costs for material are way too high,” for instance. It could be like a learning tool.
SIEMENS STIFTUNG: In the case that you have multiple stakeholders willing to share data and software systems, who could be the one taking the lead?
BARNABY: At the end of the day, there is a total cost of doing this. You have got to go where the money is. Social entrepreneurs are not going to be able to pay for such a system until they have reasonable substantial turnover. As long as they are looking for money they are not going to volunteer to spend it, so you have to go where the investors are. But if we are talking about reducing the costs for investors through simple due diligence, the magic has to be an equation where the investor has a lower cost through plugging in to what we were previously talking about and the social entrepreneur has no cost until they have revenues.
SIEMENS STIFTUNG: Thank you, both, very much!