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.
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.
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,
- Banks need to have a function to deliver Intelligence capability.
- This function needs its own budgets and operating model.
- Very similar to data governance models, the Intelligence organisation needs to be federated across the firm.
- This federated model needs to cover parts of the bank that benefits most from AI, but also where data and processes are more matured
- Implementation of AI in these parts of the bank could spark viral uptakes across the firm
- 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.
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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