Rebalancing David vs. Goliath — How AI can help SMEs get Better Business Terms
Tags: Fintech, Commodities, Artificial Intelligence, Technology, Insurtech

Rebalancing David vs. Goliath — How AI can help SMEs get Better Business Terms ChAI Jun 17, 2019·4 min read

Historically, SMEs have tended to hold back in the face of new technologies. First-mover advantages related to technology have usually been captured by bold new entrants or those with enough scale and resources to fully take advantage of emerging opportunities — leaving the middle ground behind. With Artificial Intelligence (AI) it’s a different story — AI has never been less expensive or more accessible, so there is no longer any excuse for not adopting it. For once, the winners in the AI race will be the fastest, nimblest and hungriest, not the behemoths. In this blog, we discuss the benefits of AI for SMEs — as providers of services and products to others, but equally as clients in their own right.

Inside-out: Making SMEs more competitive

Given that SMEs tend to be nimbler with greater organisational flexibility, changes can (at least in theory) be implemented more easily. This means that SMEs should be able to realise the benefits of AI faster than larger companies and compete with them head-on at a whole new level. We see three main ways that AI levels the competitive playing field for SMEs in their own businesses:

Automation : Large companies have more hands on deck to get everything done, making them seem more competitive than SMEs. But SMEs can use AI services to automate repetitive and routine tasks. AI-powered services and systems allow smaller businesses to operate like a large organisation with much more speed and greater efficiency.

: Large companies have more hands on deck to get everything done, making them seem more competitive than SMEs. But SMEs can use AI services to automate repetitive and routine tasks. AI-powered services and systems allow smaller businesses to operate like a large organisation with much more speed and greater efficiency. Data analytics : Large companies have the resources to turn to expensive data scientists, which is out of reach to most SMEs. AI can close this gap, giving SMEs the same ability to spot new revenue streams and opportunities for growth by analysing their data — both historical and forward-looking, no matter how small the company. E g, in agriculture, machine-learning techniques in farming management apps such as FarmLogs help individual farmers to profile soil fertility and then predict, based on weather, climate and prices, when and where to fertilise a crop. Welcome to the AI-powered world of “precision agriculture”.

: Large companies have the resources to turn to expensive data scientists, which is out of reach to most SMEs. AI can close this gap, giving SMEs the same ability to spot new revenue streams and opportunities for growth by analysing their data — both historical and forward-looking, no matter how small the company. E g, in agriculture, machine-learning techniques in farming management apps such as FarmLogs help individual farmers to profile soil fertility and then predict, based on weather, climate and prices, when and where to fertilise a crop. Welcome to the AI-powered world of “precision agriculture”. Customer service: As a general rule,large companies can afford more staff for customer service. But AI can make this a moot point. Virtual assistants and chatbots are making it easier than ever for SMEs to engage with customers in a personalised way and provide instant support — regardless of time or place, looking like a large company.

Outside-in: Enjoying the same terms as larger peers

There is no doubt that AI can help level the playing field for SMEs in terms of how they run their own businesses. But can AI provide other benefits for SMEs as well? Indeed, an equally important rebalancing of David vs. Goliath is the fact that AI can give SMEs access to solutions and business terms that were previously reserved for their larger peers.

In pre-AI times, there was a perfectly good excuse for most companies to focus efforts on their largest prospects and clients. These clients had the highest revenue potential, whereas the cost of understanding their needs and wants was on par with that for smaller clients. AI techniques and big data bring fundamental change to this equation, making the quest for understanding SME business needs a lot cheaper than before. For already data-heavy industries like banking and insurance, tailor-made SME solutions can now easily and cheaply be developed and serviced. Similarly, AI allows for new start-ups to arrive on the stage, offering innovative solutions where large incumbents haven’t been able to.

As one of these start-ups, an area particularly close to our hearts is that of commodity price risk management. Price volatility is a major risk to all sellers and buyers of commodities, yet the current financial tools available to deal with price risk are designed for the very largest, most sophisticated entities or financial institutions (such as hedge funds), rather than small-scale players who are mainly price-takers.

To illustrate, the margin requirement to access a futures contract for aluminium on the London Metals Exchange is $3,000 per lot (1 lot = 25 tonnes). Let’s assume that a small manufacturer, who uses aluminium as a key input for its production, spends 40% of annual turnover on buying the metal. With a turnover of $3 million, $1.2 million is spent on aluminium each year. At $1,900 per tonne, the company buys 632 tonnes of aluminium — translating into a margin requirement of almost $76,000. A lot of money for a small company for one year’s price risk transfer.

At ChAI, our first mission will be to help such small and mid-sized manufacturers de-risk their supply chain of industrial metals like aluminium and copper by making price risk transfer accessible, affordable and simple for them. We proudly sit at the intersection of manufacturing, finance and technology to bring stability and predictability where price volatility otherwise can run havoc with SME P&Ls.

ChAI is joining a growing group of FinTech startups such as US-based WorldCover who are disrupting the commodity price risk management industry. Whereas WorldCover have grown from a point of understanding agriculture and farming, ChAI’s starting point is the world of manufacturing, global supply chains and commodity trading. However, our approaches are similar in that they:

Look to level the playing field for small and mid-sized clients who have long had to get by without the hedging tools available to their larger peers

for small and mid-sized clients who have long had to get by without the hedging tools available to their larger peers Are made possible by recent advances in data science , an explosion in available data sets — particularly alternative data like satellite imagery — and the significantly reduced cost of computational power , meaning that large numbers of small clients can now be serviced cost-effectively

, an explosion in — particularly alternative data like satellite imagery — and the significantly , meaning that large numbers of small clients can now be serviced cost-effectively Are based on third-party data, which brings objectivity — thus avoiding disputes and fraudulent claims — and speed as no manual assessments are required

Author; Silvi Wompa, CUO