Announcements like Selina Finance’s $53 million raise and another $64.7 million raise the next day for another banking startup are prompting corporate AI and fintech evangelists to join the debate about how stupid banks are and need help or competition.
The complaint is that banks are apparently too slow to embrace brilliant fintech ideas. They don’t seem to grasp where the industry is heading. Some technologists, tired of marketing their products to banks, have instead decided to go ahead and launch their own challenger banks.
But old school financiers are not stupid. Most know that the “buy versus build” choice in fintech is a false choice. The right question is almost never whether to buy software or build it in-house. Instead, banks have often worked to take the difficult but smarter path to the middle – and it’s accelerating.
Two reasons why banks are smarter
That’s not to say the banks haven’t made terrible mistakes. Critics complain that banks are spending billions trying to be software companies, building huge IT companies with huge cost and longevity redundancies, and investing in inefficient innovations and effort. “intrapreneurial”. But on the whole, the banks know their business better than the entrepreneurial markets that seek to influence them.
First, banks have something that most technologists don’t have enough of: banks have domain expertise. Technologists tend to overlook the exchange value of domain knowledge. And that’s a mistake. So much abstract technology, without critical discussion, deep alignment of product management, and clear cut business utility, renders too much technology abstract from the material value it seeks to create.
Second, banks aren’t hesitant to buy because they don’t value enterprise artificial intelligence and other financial technologies. They are reluctant because they attach too much importance to it. They know that enterprise AI gives a competitive edge, so why should they get it from the same platform everyone is tied to, tapping into the same data lake?
Competitiveness, differentiation, alpha, risk transparency and operational productivity will be defined by how highly productive and efficient cognitive tools are deployed at scale in the incredibly near future. The combination of NLP, ML, AI, and the cloud will accelerate competitive ideation by orders of magnitude. The question is, how do you hold the key elements of competitiveness? This is a difficult question for many companies to answer.
If successful, banks can get the true value of their domain expertise and develop a differentiated advantage where they don’t just float around with every other bank on someone’s platform. They can define the future of their industry and retain its value. AI is a force multiplier for business knowledge and creativity. If you don’t know your business well, you’re wasting your money. Ditto for the entrepreneur. If you can’t make your portfolio absolutely relevant to your business, you end up being a consulting firm pretending to be a product innovator.
Who is afraid of whom?
So are the banks at best cautious, at worst frightened? They don’t want to invest in the next big thing to make it fail. They can’t tell what’s real from the hype in the fintech space. And that’s understandable. After all, they spent a fortune on AI. Or have they?
It looks like they spent a fortune on things called AI – internal projects with no chance of scaling to the volume and concurrency requirements of the business. Or they have found themselves entangled in huge consultancy projects staggering toward a lofty goal that everyone knows deep down isn’t possible.
This perceived apprehension may or may not be good for the banking industry, but it has certainly helped foster the new industry of challenger banking.
Challenger banks are widely accepted because traditional banks are too stuck in the past to embrace their new ideas. Investors accept too easily. In recent weeks, US challenger banks Chime unveiled a credit card, US bank Point was launched, and German challenger bank Vivid was launched with the help of fintech company Solarisbank.
What’s going on behind the curtain
Traditional banks also expend resources to hire data scientists, sometimes in numbers that dwarf challenger bankers. Traditional bankers want to listen to their data scientists about questions and challenges rather than paying more to have an external fintech provider answer or solve them.
It’s probably the smart game. Traditional bankers wonder why should they pay for fintech services that they can’t own 100%, or how can they buy the right pieces and keep the pieces that are a competitive advantage? They don’t want that competitive advantage floating somewhere in a lake of data.
From the banks’ point of view, it is better to “fintech” internally, otherwise there is no competitive advantage; the business case is always compelling. The problem is that a bank is not designed to stimulate creativity in design. JPMC’s COIN project is a rare and fantastically successful project. However, this is an example of a super alignment between creative fintech and the bank’s ability to articulate a clear cut business case – a product requirements document for lack of a better term. Most in-house development plays games with open source, the sheen of chemistry fading as budgets come under scrutiny for ROI.
Many people will talk about setting new standards in the years to come as banks onboard these services and buy new businesses. Ultimately, fintech companies and banks will come together and proliferate the new normal as new banking options proliferate.
Don’t take on too much technical debt
So there is a danger of spending too much time learning to do it yourself and missing the boat when everyone else is moving forward.
Engineers will tell you that untrained management can fail to follow a consistent path. The result is an accumulation of technical debt as development demands keep zigzagging. Putting too much pressure on your data scientists and engineers can also lead to technical debt accumulating faster. A bug or inefficiency is left in place. New features are intended as workarounds.
This is one of the reasons why software developed in-house has a reputation for not scaling. The same problem appears in software developed by consultants. Old system problems hide under new ones and cracks start showing up in new apps built on shoddy code.
So how to solve this problem? What is the right model?
It’s a bit of a lackluster answer, but success comes from humility. It should be understood that big problems are solved with creative teams, each understanding what they bring, each being respected as an equal and managed in a crystal clear articulation of what needs to be solved and what success looks like.
Add to that Stalinist project management and your probability of success increases by an order of magnitude. So future successes will see banks having fewer, but much more trusted fintech partners who jointly value the intellectual property they create. They will have to respect the fact that neither can succeed without the other. It is a difficult code to decipher. But without it, the banks are in trouble, as are the entrepreneurs who seek to work with them.
