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From there , we developed the platform to fully integrate the partners using our open platform FusionFabric . cloud . With all building blocks in place , we are at the final stage ; partnering with critical microfinance , development banks , infrastructure and on-the-ground sponsors , which will be disclosed in the coming weeks .

Can you highlight any initial benefits you ’ ve seen from the initiative in Kenya ?

After identifying Kenya as our initial launchpad , we coached a local FinTech , Aqila , over a 12-month period , in support of our goal to boost the local FinTech economy . It has now integrated its proprietary coach-matching app and AI-based SME credit scoring into the Trust Machine and is ready to take it to market .

We ’ ve been working with our ecosystem to generate the missing market , credit and loan data to finely calibrate the loan optimisation model and create the e-learning programme specifically for Kenya . The final step is finalising a partnership with a consulting firm to roll out the programme and use its expertise in local specificities , regulations and governmental relations . Kenya has an over-reliance on expensive pay-day loans but a burgeoning FinTech scene and so we thought this would be a great opportunity to pilot the programme and reduce the US $ 19bn funding gap by 1 %, which could create over 50,000 jobs in the long term .

What are the next – and long-term – aims of the scheme ?

As we start rolling out the programme in Kenya , we ’ re using it as a blueprint for other countries including India , South Africa , Brazil , Philippines , Thailand , Pakistan and Indonesia – the programme is attracting interest from banks and partners alike . In each country , we will engage universities to support the programme and generate data for each market / sector to continually enhance the modelling capabilities .

Building out the e-learning / education aspect of the programme is also important , so that opportunity is extended to those with lower levels of literacy and just as important is building out the data underpinning the modelling to holistically encompass sector , country , fiscal , political , macro-economic and environmental factors .

‘ Data ’ plus ‘ education ’ are the building blocks to ‘ anticipation ’ – a state that enables both lenders and borrowers to better envisage the future and plan accordingly .

When we reach this state , we have put in motion a way to support both borrower and lender that will create opportunity in primary and digital economies , while reducing the cost of capital – and it is this self-sustainability that creates the conditions to reduce the funding gap by 1 % . . . and more .

How will it be scaled to more countries ?

Industrialising at scale requires two partners ; one FinTech and one consulting firm incountry . As Trust Machine is an ecosystem of cloud-based components , it is being developed for consumption via a uniform API , deployable in a few clicks . The FinTech will customise it for the local market and the partner will provide the local knowledge and platform to access entrepreneurs and SMEs .

Why is it so important for vendors to support SMEs , particularly in developing countries ?

SMEs are the lifeblood of the global economy but hit hardest by the pandemic . Keeping money flowing to the sector via initiatives like Trust Machine will bring more people into financial and economic ‘ visibility ’, and reduce the devastating impact that a ‘ K ’ shaped economy recovery would have , in which some sectors recover quickly while others decline , exacerbating inequality .

SMEs don ’ t just need affordable finance , they need the tools , support and opportunities to bring themselves into long-term economic growth . And as they grow , they contribute to the success of their country , attracting more investment and improving the lives of their citizens and nations . Supporting SMEs is supporting the next generation of opportunity and equality . �