The future of consumer financing depends on banks and how they can catch up with the fintechs

The vast majority of companies in the Buy Now, Pay Later (BNPL) space are direct lenders like Klarna and Affirm who are trying to compete with banks leveraging Artificial Intelligence (AI) and other tools at their fingertips, but they do not always have the balance sheets to back up their ambitions. 

Fintechs were created as technology companies designed to offer financial services, but banks have evolved over hundreds of years with financial regulation and now are adapting with the latest technology. Traditional lenders hold a particular advantage in the space that Silicon Valley BNPL fintechs have recently entered — consumer loans. Banks have always adopted new technology, so it will be easier to catch up on five years of tech than it will be for tech companies to adapt to compete against players with centuries of experience. 

New tech, better tech

During the Cold War, the Space Race between the US and Russia heated up when the Soviet Union put the first artificial satellite into space in 1957: Sputnik. Soon thereafter Laika the dog was sent into orbit, followed by the first person in space, Yuri Gagarin. Before the close of the 1960’s, however, the US declared strategic technological victory when Neil Armstrong set foot on the moon in 1969. What’s the connection? It’s not the first that paves the way that always wins. Much like the BNPL space. Fintechs may have figured out a novel technology and paved the way for an entirely new service line, but ultimately they’re competing against a richer competitor who is staffed with equally smart talent and can divert funds into building out the new revenue stream.  

In other words, banks have about five years to catch up on BNPL technology, whereas fintechs have 300 years of financial services to try to gain up on with their algorithms. This is because consumer lending is not just about feeding data into a neural network and trusting what it spits out on the other end. Lending is about marrying years of customer insights, human behaviour, financial models, aggregated data, and the latest in machine learning to generate the best positive outcome for consumers. 

A traditional lender is an aggregate of accumulated data, and a wealth of understanding — not a piece of software that accumulates data to forge an aggregate understanding. Banks will only be made stronger by incorporating AI, whereas a purely algorithmically driven decision engine is only as good as the publicly accessible and transactional generated data that is fed into it from its point of inception, to where it currently stands at whatever point in time you’re reading this. At the time of this writing: five years, on average.

When banks adopt a BNPL solution, their industry expertise and baked-in best practices for issuing unbiased loans based on the best possible data is translated into greater and quicker approvals from a deep ‘blue’ reservoir of pre-qualified lenders. 

Regulation on the horizon

Here’s the one hundred twenty-six billion dollar question: how will the multinational BNPL fintechs that built their business model on user acquisition survive a tentative GDPR-style regulation of AI currently ramping up in the EU, and eminent financial regulations simultaneously targeting their industry in the US, Europe, Australia, and the UK? [If you’re interested, The Paypers recently covered those spate of regulations in much greater depth than I feel I should be allowed to go into, here.]

Responsible lending goes hand-in-hand with regulation. Banks are regulated. It’s part of the fabric of their training programmes when they’re onboarding new loan officers. In the absolute worst-case scenario, if a bank were to have to take a step back to retool an algorithm so that it could provide the same level of functionality it was designed to do whilst shifting to comply with new regulatory statutes, they might not suffer any noticeable loss of service on the customer service end of things. After all, banks have centuries of experience, multiple rich revenue streams, and a cavalcade of high-level human capital bearing expertise across multiple industries to fall back on in the event that a single piece of technology fails or needs to be updated. 

For companies that rely heavily on one single piece of technology, however, the shift to comply with new regulations might be crippling. How might an algorithm company face up to the public if it is revealed they’ve been fostering widespread acceptance of a product crippled by bad data in order to sell untenable levels of debt to at-risk individuals with a bad credit history, for instance?

Is it ethical to apply BNPL to necessities?

A potentially ominous new development in the BNPL space that spun out of last year’s pandemic-driven spike in unemployment, and subsequent rent moratorium in big US cities like Los Angeles, is the idea of compartmentalising payments for necessities such as rent.

Traditional lenders would know better than to lend to someone who can’t pay their rent in one monthly instalment. If the person you’re renting to can’t pay up front each month to live in the place they’re renting from, why would splitting the payment into four easily-missable payments change their ability to pay?

Banks likely won’t approve these customers. Will fintechs? What does that mean with regard to how customers are allowed to accrue debt? As banks begin to catch up with BNPL fintechs they stand to not only make the space safer for their customers, they also will help reign in AI bias by taming the new technology with decades-old regulatory structures. This will mean fairer loans based on algorithms but guided by human input and decades of expertise. Disputes over alleged unfair lending practices that stem from algorithmic bias can be raised with human operators and challenged when using a bank’s BNPL service, because banks stand to benefit from safeguarding their clients.

When banks build BNPL products, they begin with a base of preexisting financial compliance and add in decades of complex datasets thereafter. In the event of an ‘AI transparency audit’ banks will have to prove that their in-house decision engines are compliant with the latest regulatory standards, and that their AI-driven BNPL services mirror that compliance in a way that’s transparent and accessible. A move from strength to strength and one that is surely likely to put banks in the best position to come out on top in BNPL.

About Yaacov Martin

Yaacov Martin is the Co-Founder and CEO at Jifiti. He holds an LLB in Law from the Hebrew University and is an active contributor to leading payments and fintech publications. Prior to Jifiti, Yaacov founded one of Israel’s leading import and distribution companies for consumer goods.

About Jifiti

Jifiti is a leading fintech company on a mission to bridge the gap between lenders, retailers, and consumers. Jifiti enables banks and lenders to provide point-of-sale financing by seamlessly deploying their competitive consumer loan programmes at any merchant's point-of-sale. Merchants and customers can now benefit from the best loan programmes offered by the world’s leading banks based on Jifiti’s cutting edge technology and user experience, both online and in-store. Jifiti works with major financial institutions including Mastercard, Citizens Bank, CaixaBank, Credit Agricole, and retailers including IKEA, Walmart, and others worldwide.