“The value of an idea lies in the using of it”

First off, before getting to this post — I would like to extend my own congratulations to the folks at?Credit Kudos, and their various venture capital backers. Though the exit price might look quite a bit less attractive than many others, and the multiples were very good (by my reckoning), rather than an outright “home run”, the fact that?Apple?chose to buy a UK based B2B provider trying to address accessibility and affordability challenges, underscored a primary use case of the Open Banking initiative. Furthermore, if the rumors about?Apple’s new subscription model and participation in the BNPL world prove correct, the acquisition will further support the idea of Bigtech becoming a major force in alterative lending to both consumers and businesses.

Now to the post…

Overview

Since November, I have seen significant questions asked about the success of Open Banking. On one side sit critics who argue that the mechanisms that enable account aggregation haven’t really delivered anything meaningful, and certainly have not catalyzed a new dynamism among banking service providers. On the other sit organizations that have built specific types of propositions among the 5.4ml or so individuals and businesses (growing by some reports at a 50% CAGR) who are engaged with the principles of open banking. These “fintech” firms have enabled individuals to start interacting with their financial situation differently, which in turn is introducing these clients for the first time to a broader spectrum of financial products, some oriented toward facilitating “better borrowing” and others to supporting the prospect of faster and more suitable wealth accumulation.

My colleagues and I at?Porthos & Co (www.porthos.co)?have been gaining a first-hand experience of the principles of Open Banking and can appreciate why some organizations have been critical of the evolution of the solution design we have seen supported from the initially mandated CP9 and others. In particular, in our own interactions with the various api services that are generally accessible through one of the established TPPs, we have discovered

1) Certain types of transactional categories can be difficult to fully understand. Many of these relate to activities that involve the direct transfer of funds between bank accounts as opposed to other types of transactional situations. Identifying these incorrectly, I have concluded, can have an adverse effect on understanding a client’s spending vs. savings behaviour, as well as the use of payment management techniques which are used to reduce fees/charges, and friction.

2) Sourced information in terms of completeness, and identification, can be inconsistent from provider to provider, as well as in relation to the account type. I have concluded that this creates challenges in relation to understanding money movements through different credit and debit vehicles, and building a consolidated picture over a set time period (i.e. 1yr). A lack of consistency in the availability and completeness of transactional data undermines attempts to create alternative affordability models from those that have relied mostly on credit history.

3) The notion of a “funding” account approach in which money moves from a current account into other types of accounts, i.e. savings, investments, loans can be easily mistaken as discretionary expenses. Since we know that deductions from a cash balance that goes toward reducing debt or increasing wealth accumulation represents good client behavior, improper categorization only rectified through data management challenges efforts to build underwriting decisions that can be traced back to good client actions on a regular basis.

4) That while Machine Learning will certainly have a place in identifying regular and irregular client behaviour, for the moment, categorization, in my opinion, needs to be managed with a combination of business logic, data analysis, and user intervention. Self-served data management is normally a very hard sell for end clients unless it directly impacts a high value need (such as purchasing a house, getting a private school loan etc.), and thus will necessitate for a while, a client service function to support this need. We are already building into our system design the sorts of data support services that are frequently associated with big data recommendation systems such as those delivered by Spotify and Amazon.

Why is all of this important to know?

In my opinion, some of the most important objectives of Open Banking that were conceived by the regulator, are derived from the fact that both consumer and SME banking has historically been very poor in understanding its own client’s behaviour. Instead of building from its own data banks, and its payment interconnectivity to other participants in the financial system a broad and connected picture of its client’s financial behaviour, banking and lending institutions have left it to credit bureaus (on the lending side), and financial advisors (on the wealth side) to inform them on matters related to affordability and to independent financial advisors operating in mostly sub-scale environments, suitability for wealth accumulation and retirement management.

The implications of this are significant , profound, and ultimately cataclysmic — while our incumbent banks, building societies and traditionally lenders, can build financial products that utilize broad levers like rates, terms, structure, and capital, they neither possess the knowledge nor the systems to hyper-personalize these products to individual user behaviour. Perhaps this would be OK if the broad realities of most of our lives was built around the sole tinkering with generic product designs as it was up to the financial crisis of 2008/2009, but with BigTech showing that immense data analytics can drive personal recommendations within an acceptable data privacy framework (for most), incumbent financial institutions are now finding themselves stuck at the starting gate with a race already well underway and advancing at pace.

Conclusion

When one considers this situation within the realities of Open Banking, it is not surprising that thus far the level of engagement and innovation by financial institutions has been very limited, ensuring that all of the work, and additional costs on building value falls directly on any type of third parties that intend to build hyper personalized and segment-oriented business models. This shouldn’t be surprising, as financial institutions continue on their desired journey of trying to turn all fintech innovators into low-cost client introduction and acquisition channels (as per comparison sites), but for those of us who want to deliver better client outcomes and more personalized solutions by re-writing outdated affordability and suitability rules, and not merely optimizing the cost/return ratio, it is a real challenge, and barrier to success that Open Banking has thus failed in its effort to tackle.

We at Porthos believe that affordability built on a client’s behaviour, and life choices when it comes to work, wealth building and debt management represents the future that underpins good borrowing outcomes and appropriate wealth investment decisions. It will be a journey we and are clients will go on that will be hampered by data management challenges, but will be one which ultimately will lead to changes into how client affordability is determined, as well as how clients leverage their current net worth, and future net worth potential.

Banks, Building Societies, Card Providers, and even many Investment platforms seem determined to make it hard for clients to get the access, and opportunities to financing and wealth solutions that they want and deserve, but ultimately in the same way that Bigtech has challenged the notion that hyper-personalization at scale cannot be delivered cost effectively, new fintech and wealthtech firms will prove, in my opinion, the same is achievable in the world of individual finance. The clock is ticking.