klarna

AI will hurt banking without a ground-up approach

Injured Piggy Bank WIth Crutches

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The last twelve months have seen AI fever reach new peaks. Every technology giant we could name have got a huge AI story to tell. Starting with IBM Watson, Google Allo, Facebook AI Research, Amazon Echo have all had major traction. This is also reflected in investments being made into the AI industry. Industry leaders magazine predict 2017 would be the year of AI investments. It is predicted that about $37 Billion would be invested into AI by 2025. There have been similar “This time it is different” stories on AI for almost 50 year. Well, I believe it is definitely different this time, mainly because AI was preceded by the Big Data revolution this time. I believe it is different for firms and industries with access to quality data to leverage for AI. I believe that the technology firms mentioned above have got their data right. And I believe banks are a joke when it comes to quality data. AI will hurt banks without quality data.

 

Internal legacy tech challenges and data quality problems aside, new regulation in the form of PSD2 is going to have massive impact on a bank’s strategy to customer facing AI use cases. With PSD2, customers will be more in control of their transaction data. Third party providers (TPP) will now have access to customers’ transaction data, in the process becoming an abstraction layer between banks and customers. So, unless banks completely rethink their customer interfacing model, nimble players who can create clever AI applications on customer’s financial information, will make banks just a utility provider, hence hurting their margins.

Banks have two challenges to resolve at the same time, internal data quality issues to make AI work for operational intelligence, and external data ownership issues to make AI work for dealing with customers.race_for_ai_new_1-featured.png

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Data is the 21st Century Oil

There have been some success stories with Fintech firms doing AI and there are new ones emerging every day. The world is moving to a place where there will be an AI app (and an app store) for most activities. When we were evaluating IBM Watson at PwC, one phrase the IBM team used was “Watson going to school”. This effectively meant training IBM Watson on a particular topic. Let’s assume IBM Watson had to be trained in MIFID 2 regulations, it involved loading the regulation text into Watson and then an SME spending a few weeks with Watson asking questions on MIFID 2. The SME would then provide feedback on Watson’s answers, and Watson would use this data to provide better answers next time. This needed to be done until Watson became an expert in MIFID 2 before the capability could be launched commercially. We also learnt from the exercise, how understanding legal/regulatory language was different from natural language. That exercise showed how critical data, data structures and the taxonomy of data was for AI applications to work. On that note, news is that IBM just acquired a Promontory Financial Group to help improve Watson capabilities for banks’ back office functions.

'And then Dmitri noticed something that would have a profound effect on the human/robot wars.'

Regulations for data have failed?

Most successful AI platforms have access to high quality data, and in huge volumes. They also get to see regular transactions across different streams of data that they can then learn from. This is the case with Fintech firms that use AI at the heart of their proposition. What about our dear banks? Banks do have data, but the quality, integrity and accuracy of data stored digitally is generally appalling. A few years ago BCBS 239 emerged as a regulation focused on fixing data in banks, however compliance to that is mostly being treated as a check box exercise costing the banks millions. The point being, banks are years away from processes and infrastructure that provides quality data. If AI is introduced into this landscape, it would be more detrimental to existing processes, as there would be more hands involved in confirming results suggested by AI, and the costs of using AI would outweigh its benefits. Is there hope?

Banks need to get back to the drawing board to make the most of AI. Here is a simple approach I can think of,

  1. Banks need to have a function to deliver Intelligence capability.
  2. This function needs its own budgets and operating model.
  3. Very similar to data governance models, the Intelligence organisation needs to be federated across the firm.
  4. This federated model needs to cover parts of the bank that benefits most from AI, but also where data and processes are more matured
  5. Implementation of AI in these parts of the bank could spark viral uptakes across the firm
  6. There needs to be standardised ways for AI applications within banks to interface with each other. Without this, there will be AI applications developed across the bank for every single process and there wouldn’t be any integration possible. This is where governance will help.

Well, in order for the above to work, data still needs to be of good quality. Top down models are not always lean in their approach. However, it is possible to achieve a top down model that can be lean, if the priorities are based on benefit realization rather than empire building to get to an MD promotion.

Now, what about external challenges?

PSD2 could be a great opportunity for banks, and of course a huge challenge too. Let’s focus on the opportunity first. Say a customer banks with Barclays for his salaried account, has a mortgage from Halifax, and a credit card from HSBC. If Barclays was willing to be an Account Information Service Provider (AISP), it can effectively source the customer’s transaction (with their permission) from Halifax and HSBC and could offer Personal Finance Management (PFM) services. Imagine having access to transaction data across all products that your customers have. Barclays with access to mortgage transactions from Halifax could create a credit product for the customer for home renovation. While this is just one example, PSD2 could create new revenues streams for banks if they were willing to target other points in the payments value chain. That is a whole topic for discussion by itself though.

AISP in action

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While this sounds good from the banks’ perspective, the more likely outcome would be Fintech firms, and possibly tech giants (Google, IBM, Microsoft, Amazon) making clever use of customer transaction data as they are light years ahead of banks in their AI capabilities. While we have already discussed Tink in DailyFintech, Qapital and more recently Klarna are also jumping on the PSD2 bandwagon.

How can AI-Fintech firms chip into the story above? Well, they don’t have much to lose (unlike banks). AI firms focused on banking use cases, should focus on small problems and solve them really well. And the AI industry focused on Banking will need to identify the importance of standardisation of interfaces for data interactions across banking applications. While this exercise will be made easier for customer facing use cases (thanks to the PSD2 wave), operational AI internal to banks will be a much harder nut to crack. If achieved, it will allow for multiple processes and activities within banks to be replaced by AI in one go. I wouldn’t be surprised if banks, warmed up by Blockchain revolution, form consortiums to drive AI revolution. And if this happens, I believe AI penetration within banks could be faster and deeper than Blockchain managed.

Arunkumar Krishnakumar is a Fintech thought-leader and an investor. 

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What comes after MarketPlace Lending?

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Now that Lending Club is past its crisis mode and is just another mature company that has to impress investors with predictable growth in financials on a quarterly basis, we look at where the puck is headed in Lending.

What innovation will change the Lending game and create companies as big as Lending Club ten years from now?

Disclosure: I sold shares in Lending Club just before their recent quarterly earnings (Q4), having been fortunate enough to buy in at 3.51 after writing this post. Although I expect Lending Club to do well, the shares no longer have the great margin of safety that they did right after the crisis in May.

This post looks at four innovations that focus on two big imperatives facing any Lending entrepreneur – reducing Customer Acquisition Cost (where Customer = Borrower) and reducing Cost Of Capital (reducing the intermediary cost):

  • The next generation Deposit Account
  • Just in time Borrowing by consumers
  • Big Data for the lending to Micro Entrepreneurs
  • Automated working capital financing for SME

The next generation Deposit Account.

One big reason Banks have a low cost of capital is that consumers are willing to put up with lousy interest rates in order to get safety i.e. to know that their money is not at risk. The next generation Deposit Account could change that and our thesis is that the next generation Deposit Account will be based on the Lending Account.

Market Place Lending has created the first real banking innovation in hundreds of years which is the Lending Account. Until the likes of Prosper, Lending Club, Funding Circle and Lufax came along, consumers could have a Deposit Account (lend money to a bank) and a Loan Account (borrow money from a Bank) but could not directly lend in any simple scalable way.

This post shows how one Consumer has used Lending Accounts to make good money, much better than Lending to a Bank via a Deposit Account.

Most people don’t want to a) work that hard b) take that much risk. This is where the next generation Deposit Account is awaiting a great entrepreneur. The next generation Deposit Account will be a Lending Account that is ultra low risk and short tenor. Let’s start with tenor. If you are willing to lock up your capital for a few years, you can use existing Market Place Lending accounts. Compare the risk-adjusted return over 3 years compared to locking up your money in a 3 year Deposit account or a AAA Sovereign Bond and the Market Place Lending account looks pretty good.

However, most people want to have cash available at short notice for emergencies. They might want the notice/tenor to be weeks or at most months. That is hard to do using Market Place Lending accounts because the Borrower needs longer to repay. Unless you work hard to resell on a Secondary Market such as Foliofn, this is not an option.

This requires some financial engineering – the sort of thing that Wall Street has always done well. Through a mix of securitization, secondary markets and a cap & floor based guaranty, this is feasible. The arbitrage between lending to bank (Deposit account) and lending directly is big enough to give any entrepreneur enough to play with.

A note on securitization. Although securitization is seen as the villain of the Great Financial Crisis of 2008, there is nothing wrong with securitization per se. The issue is transparency. If you can hide a bunch of dodgy BBB loans in a shiny AAA package that is bad. If BBB loans are sold to those who know how to manage the risk/reward trade off, then markets are working as they should.

In addition to financial engineering, some UX magic is needed to make this as  simple for consumers as opening a Deposit Account.

If anybody is working on this, please let us know in comments.

Just in time Borrowing by consumers

The way a Market Place Lender like Lending Club or Prosper finds borrowers is remarkably old-fashioned. There is a lot of direct mail and search engine marketing to find consumers who want to refinance expensive credit card debt.

What if you eliminated the credit card phase and the Market Place Lender could acquire customers at the point of sale? That is what Klarna is doing for example; the proposition is bill me and I promise to pay. That can work in small, homogenous and relatively wealthy countries (eg Nordics, Switzerland) where the default rates will be relatively low.

This can also work at the opposite end of the spectrum in huge and relatively poor countries (such as China, India, Africa) where Credit Card penetration is low ie new models can appear at the point of sale to deliver lower cost borrowing to consumers at the point of purchase.

What is unclear is whether these new borrower acquisition models at the point of purchase will be part of a Lending Marketplace or part of an ecosystem that delivers customers to the Lending Marketplaces.

Big Data for the lending to Micro Entrepreneurs

You can lend to business or consumer or to the grey area in between of the self-employed “micro entrepreneur” where companies like Iwoca operate. The key here is that the revenue sources for these self-employed micro entrepreneurs are data rich services such as Uber, AirBnB, Amazon, Alibaba, eBay etc and data is the key to assessing lending risk.

Automated working capital financing for SME

Approved Payables Finance when the SME sells to Global 2000 type Corporates is working well. The APR is far lower than the SME would get from traditional finance or AltFi and Lenders get short tenor, self-liquidating high grade debt at far better interest rates than Sovereign Bond lending.

To date this has remained a niche play, despite working so well. This will scale when two things happen:

– An open standard drives e-invoicing to 90% plus adoption (the remaining 10% can be forced, enabing huge cuts in AR and AP processes). Once AR and AP is entirely digital, inserting just in time working capital financing options is easy.

– A credit rating for SME; today this only works when buyer is “investment grade”i.e. a corporate with a credit rating from an agency such as Moody’s, S&P or Fitch . If a butcher selling to a baker or candlestick maker could evaluate the credit rating of the  baker or candlestick maker, pricing credit would be simple and thus the APR would come down a lot. This is not rocket science and as always it is a data problem. All you need to know is does the baker or candlestick maker pay their bills on time.

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If anybody is working on solutions for the kind of innovation profiled here that we have not already mentioned, please tell us in comments.

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Klarna is Primary Fintech Innovation from Nordics

Primary innovation is doing something that has never been done before.

Most primary innovation is ridiculous and fails without anybody noticing.

Very, very rarely, primary innovation changes the world and the company that creates the innovation becomes a Unicorn.

Secondary innovation wins by giving a twist to that primary innovation. The two simple twists are:

  • Apply the primary innovation to a different country aka “concept arbitrage”. A lot of European and Asian Fintech falls into this category. This is more viable in Fintech because “bits don’t stop at borders, but money has to show its passport” (meaning that regulation is a barrier to entry).
  • Apply the primary innovation to a different domain aka the xx of yy (e.g. the “Uber or AiBnB of..). A lot of “subprime VC” goes to fund this type of secondary innovation.

Secondary innovation is a perfectly sensible way to make money. It is just not as interesting as primary innovation. It is hard to imagine creating a Unicorn with secondary innovation (but very easy to get headline valuations over $1 billion for secondary innovation).

The number of primary innovations is really limited. Maybe there are 10 in Fintech. Historically, almost all primary innovation has come from Silicon Valley.

Occasionally what appears to be secondary innovation – the small twist – is actually the game-changing innovation. For example, Angel List Syndicates could look like a small twist to the equity crowdfunding concept. I think Angel List Syndicates is actually the innovation that changes the whole asset management business.

I think Klarna is one of those few primary innovations. The fact that this primary innovation comes from Europe is significant.

The Klarna story has some “straws in the wind” that indicate something big such as:

  • The investors are top tier. Sequoia Capital has the best track record of spotting primary innovation early and Michael Moritz (who led Sequoia Capital in it’s current incarnation) chose Klarna as the only European Board to join. General Atlantic (who came in after Sequoia Capital) only buy when the financial metrics are strong. So while I don’t have access to Klarna’s financials, I assume they are strong.

So much for the straws, what about the wind?

In my book Mindshare to Marketshare (shameless promotion here), I describe turning “secret sauce” into “unfair advantage”.

Secret sauce is the primary innovation. It has to be solving a big problem/filling a real need (aka “value proposition”). In Klarna’s case, that is:

Less time/hassle buying on a mobile phone (consumer)

Less mobile shopping cart abandonment (merchant).

What Klarna offers is actually very old fashioned – buy now, we will collect money after you have the goods. It is what you do in the physical world (pay the barista only after you have your coffee). Doing that digitally means managing fraud and deadbeats (which is really what the Credit Card industry does). Klarna’s secret sauce was simply to track the user via their confirmed national identity. This is easy in a small homogenous country like Sweden. It turns out it also works in Germany (thus the Sofort experience) and I know it works in Switzerland where I live.

America is not fundamentally different, even though “homogenous” is not a normal descriptor. Social Security works as ID. ID theft is a big issue; it will be interesting to see how Klarna navigates that issue.

It is easy to conclude that the Rest (every other than Europe, America, Japan) is different. Klarna has its hands full in Europe and America for now, but they will soon have to tackle Asia. This where the Unique Identification Authority of India is significant.

Could this be the first European primary innovation that launches concept arbitrage in America? Affirm looks like a candidate. I don’t think concept arbitrage will work in this market because Klarna already has top tier American investors.

So, does this make Stockholm the Fintech Capital of the World? No, but Nordic Fintech is also well represented in Saxo Bank.

This will be my last post for this week. Happy Holidays.