Announcements such as Selina Finance’s $ 53 million raised and another $ 64.7 million the next day for a different banking startup will spark the company’s artificial intelligence and fintech evangelists to return to the debate about how banks are stupid and need help or competition.
The complaint is that banks are seemingly too slow to embrace fintech’s bright ideas. They don’t seem to understand where the industry is going. Some technicians, tired of marketing their products to banks, have instead decided to continue and launch their own challenge bank.
But the financiers of the old school are not stupid. Most know that fintech’s “buy vs. build” choice is the wrong choice. The real question is almost never whether to buy the tool or build it internally. Instead, banks have often worked to walk the difficult but smarter paths right in the middle – and it’s accelerating.
Two reasons why banks are smarter
This is not to say that banks have not made terrible mistakes. Critics are complaining about banks using billions of companies to be software companies, creating huge IT companies with huge layoffs to cost and longevity challenges, and investing in inefficient innovations and “intrapreneurial” measures. But overall, banks are more familiar with their business practices than the entrepreneurial markets that seek to influence them.
First, banks have something that most technologies don’t have enough of: banks have domain expertise. Technologies are working to lower the exchange value of domain information. And that’s a mistake. So many abstract technologies, without critical discussion, deep product management alignment, and sharp, clear, and business benefits, make too much technology abstract from the material value it creates.
Second, banks are not reluctant to buy because they do not value the company’s artificial intelligence and other fintech. They are reluctant because they overestimate it. They know that a company’s artificial intelligence gives it a competitive advantage, so why should they get it from the same platform that everyone else is attached to, from the same data lake?
Competitiveness, differentiation, alpha, risk transparency, and operational productivity will be determined by how large-scale, effective cognitive tools will be widely used in the incredibly near future. The combination of NLP, ML, artificial intelligence, and cloud accelerates competitive thinking on an order of magnitude. The question is, how do you own the key factors of competitiveness? That is a difficult question for many companies.
If they get it right, banks can get real value from their domain expertise and develop a differentiated advantage where they don’t just float with every other bank on someone’s platform. They can define the future of their industry and keep value. Artificial intelligence is a power factor for business knowledge and creativity. If you don’t know your business well, money is wasted. The same goes for the entrepreneur. If you can’t make your portfolio absolutely business-relevant, you’ll end up as a consulting firm pretending to be a product innovator.
Who is afraid of anyone?
So are banks cautious at best and afraid at worst? They don’t want to invest in the next big thing just to get it on the flop. They can’t tell the real hyps in fintech space. And that is understandable. After all, they have used the property for artificial intelligence. Or do they have?
It looks like they’ve spent the property on things called AI – internal projects that don’t have the ability to snowball in hell to scale according to the number and concurrency requirements of the company. Or they have infiltrated huge consulting projects that are cheating towards some lofty goal that everyone knows is not possible within them.
This perceived intimidation may be good for banking, but it has certainly helped promote the new industry of the challenge bank.
Challenge banks are widely accepted because traditional banks are too stuck in the past to embrace their new ideas. Investors too easily agree. In recent weeks, American challenge banks Chime unveiled a credit card, U.S. point of interest and German challenge bank Vivid founded fintech company Solarisbank.
What happens behind the curtain
Traditional banks also use resources to hire data scientists – sometimes so much that those dwarves challenge bankers. Old bankers want to listen to their data scientists on questions and challenges rather than pay more from an external fintech supplier for an answer or solution.
This is undeniably a smart game. Traditional bankers ask themselves why should they pay for fintech services that they can’t fully own, or how can they buy the right bits and keep the competitive edge? They don’t want a competitive advantage floating in the data lake somewhere.
From the banks’ point of view, it is better to “fintech” internally, otherwise, there is no competitive advantage; a business case is always convincing. The problem is that the bank is not designed to encourage creativity in design. JPMC’s COIN project is a rare and incredibly successful project. While this is an example of a creative fintech and a bank that is able to express a clear, sharp business problem – a product requirements document for a better term. Most of the internal development is playing with open source, and the splendor of alchemy ends when budgets are treated hard in terms of return on investment.
Many people talk about setting new standards in the coming years as banks use these services and buy new businesses. Ultimately, fintech companies and banks will merge together and set a new standard as new options in banking increase.
Do not incur too much technical debt
So you run the risk of spending too much time learning how to do it yourself, and tackling the boat as everyone else moves forward.
Engineers report that untrained management may fail to lead a consistent course. The result is an accumulation of technical debt as development-level requirements continue in the zigzag. Applying too much pressure to scientists and engineers can also lead to faster accumulation of technical debt. An error or inefficiency is left in place. The new features are built as workarounds.
This is one reason why internally built software has a reputation for scalability. The same problem occurs in software developed by consultants. Old system problems are hidden under new ones, and cracks begin to appear in new applications built on low-quality code.
So how do you fix this? What is the right model?
It’s a bit of a boring answer, but success comes from humility. It needs an understanding that big problems are solved with creative teams, each of whom understands what they bring, each is respected as an equal, and has clear control over what needs to be solved and what success looks like.
Throw in Stalinist project management, and the probability of success rises to the order of magnitude. So for future success, banks will have fewer but much more reliable fintech partners who will value the intellectual property they create together. They must respect that neither can succeed without the other. It’s hard to crack the code. But without it, banks are in trouble, as are entrepreneurs who want to work with them.
