Game Changers for SME Banking

What makes SME lending so hot these days?

There is a lot of rumour around the prospective growth of the global SME lending market. It represents 90% of businesses and 50% of employment worldwide. World Bank calculations furthermore indicate that 600 million jobs will be needed towards 2030 to absorb the growing global workforce, which makes SME development a high priority for many governments around the world. Businesses typically financed their activities through bank loans. Access to finance however has become a key constraint to SME growth these days. I see two major finance gaps for the upcoming decade. Firstly, according to strategy consultant Roland Berger, the coronavirus crisis caused 58 percent of SMEs to lose on average 50 percent of revenues year on year in the month of April 2020. This was even more critical as one in every two firms had just two months of cash reserves. Faced with this situation, many SMEs reinvented themselves: more than 40 percent of firms adapted their sales channels, products, services, or entire business model to be fit for future growth. After all the investments done, the projected future growth now needs a lot of capital.Secondly, another major finance gap can be found with micro and small businesses like self-employed people or tech start-ups whose cash flows make them ineligible for standard bank loans, or whose ventures are deemed too risky to be financed by traditional institutions. Their cost of financing is higher due to high interest risk premiums that reflect the estimated risk of these ventures. Moreover, many don’t have access to bank loans as bank loans are typically only available to companies with at least a two-year credit history. Fintech lenders and early-stage investors scan this segment. A substantial part of this types of small businesses can be found in developing countries where many people are self-employed or run micro-companies.

A huge finance gap of $5.2 trillion annually needs closing

The International Finance Corporation (IFC) now estimates that 65 million firms, or 40% of formal micro, small and medium enterprises (MSME’s) in developing countries, have an unmet financing need of $5.2 trillion every year, which is equivalent to 1.4 times the current level of the global SME lending. The gap volume varies considerably region to region. East Asia & Pacific accounts for the largest share (46%) of the total global finance gap and is followed by Latin America and the Caribbean (23%) and Europe and Central Asia (15%).

The question now is who will close this multi-trillion finance gap? Research from the Cambridge Centre For Alternative Finance shows that credit markets around the world are undergoing a deep transformation. Fintech and big tech firms are providing increasingly more lending to households and small businesses. A new Cambridge database estimates that fintech credit flows reached $223 billion in 2019, while big tech credit reached $572 billion. Fintech and big tech credit is thriving in countries with higher GDP per capita, higher banking sector mark-ups and less stringent banking regulation.

Fintech lending 2.0 is the name of the new game

FinTech lending today differentiates by leveraging data, technology, machine learning, and digital marketing to better target, underwrite, and serve customers at scale. A new wave however is coming: FinTech Lending 2.0. These Fintechs focus on finding innovative ways to de-risk borrowers to offer cheaper and more customized products with their AI and data competences. They offer value like instant invoice financing in stores and on websites and sell the loan with it. They ensure payments by taking their repayments from the SME’s cashflows and have flexible payback schemes that relate to the success of the SME borrower. They operate in niche markets actively building partnerships to select the cream of the crop rather than targeting groups and awaiting who pops up. In short, they leverage their AI and data competences for creating maximum value for their business borrowers and themselves. Take the Chinese Ant Financial. They started hooking 1.3 billion customers on their core payment service Alipay. Based on their AI-learning loop, which enabled them to learn faster than their competitors, they launched additional services and capabilities for their customers like cash management, a virtual credit card, consumer lending and wealth management. The intelligent platform behind all this leverages all the collected data in human intelligence that made them move into the business market with a credit assessment service for 2.0 lenders. Or take e-com giant Amazon. Amazon invites the fastest growing firms selling on their platform to take an Amazon credit for speeding up growth even more. The SME’s taking the loan must hold their inventory in Amazon warehouses as collateral. A stunning example of fintech lending 2.0 too!

“All data is credit data” closes the finance gap

So, what’s the secret behind this all? What’s the secret sauce of the 2.0 lenders from fintech & big tech? It’s developing super-fast use case driven learning loops on AI & big behavioral data! In my academic researches to “all data is credit data” over the past 7-years, I found several data-sources that are now already creating this kind of value for SME lending to close the finance gap. The “all data is credit data” approach combines conventional credit information, if available, with thousands of data points mined from customer offline and online activities. Intelligent platforms like the ones of Ant financial, Amazon and lots of fintechs like for example Kabbage, Biz2Credit and my company AdviceRobo are now already basing credit decisions on where people shop, the purchases they make, their online social media networks, their mobile phone behaviour, their lifestyles and attitudes and various other factors that are not intuitively related to creditworthiness. This goes for SME’s and consumers! Based on my research I would say the near future of SME “all data driven credit decisioning” is on a fusion of predictive behavioural data from transactions (open banking & accountancy packages), enriched with mobile & internet activities and psychometric & biometric data of entrepreneurs. Regarding social media data for SME credit decision making, researchers found some differentiating value in attributes too. Attributes like network status and positive self-image enrich the insights but are having too little predictive power as stand-alone factors. The additional beauty of building deeper customer profiles on big behavioral data is that these profiles have, next to credit decisioning, a big added value for other use cases like AML & Fraud fighting, customer financial health management, upsell & cross sell too!  Highly interesting for the upcoming decade will be to deeply understand the added value for these use cases of data coming from sensors too. Sensors in the entrepreneurial production processes, sensors in wearables for entrepreneurs like for example smart watches, Fitbits, Motiv rings, health tags on clothes and car computers even provide the opportunity to build deeper profiles for financial decision making on entrepreneurs and their businesses. Then “all data is credit data” play will revolutionize SME credit globally and help select, onboard and retain the cream of the crop for those who are able to play it well.

Be purposeful & prudent when it comes to your customer’s data

The other, crucial, side of this medal is the privacy and protection of the customer data in the digital world. Ant financial has for example recently been urged to share their customer data with the Chinese credit reporting database overseen by the Chinese central bank. Networks of hackers are searching the world for companies who did not protect their data and IT architectures well and regulators are becoming more and more strict when it comes to customer data protection. The “all data is credit data” play therefore is one to execute with prudency & purpose for the strongest protection of customers and their data. They need to trust you in how you act in their interest!

And the winner is…?

The new tech lenders are investing heavily in the development of human centered intelligence for hyper-personalization. They apply it in the selection of their prospects, their customer experiences and in their decision making in SME lending while many traditional banks are still at the beginning of digitalizing their SME-lending process. Moreover, the new tech lenders are tech companies that collect data of their customers in their value propositions and on top of that sell loans when appropriate, rather than lenders that are digitalizing to push their loans more. A huge, huge, huge difference in perception & trust for the cream of the crop of the millennial and gen z entrepreneurs. They distinguish very fine when value is created for their benefit or when something is being pushed towards them. But, as always, building successes is a combination of art and science. Also, in lending 2.0! Traditional lenders have the huge benefit of having large customer bases that they can leverage if they dare to be bold. Daring to quickly start implementing an AI and all data strategy in compelling use cases while, in parallel, digitizing their SME credit processes. Daring to step away from traditional set ups and busines models but to the fully invest in building adaptive eco-systems for closing the SME finance gap. The winners are smart and have balls too! And they are trusted as guardians of the customer’s financial interests. So, what an immense opportunity again this is for incumbents to grab! New mindsets are required though to “just do it” but “do it smartly”! Arm yourselves with big behavioral data: the lending 2.0 battle is on now!