Ivo Langhans

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Before thinking about banking in the clouds, we should first take a look at how banks look behind the scenes today. The daily banking transactions, financial record keeping, and loan and credit processing capabilities are facilitated by a back-end system called the Core Banking System (CBS) (Gartner, 2019). While Core Banking Systems are often specialized in a certain kind of banking (e.g. retail banking, wholesale banking), they all have in common that they should enable some possibility of standardized connection and simple integration of third-party systems via open interfaces (Thalhammer, 2019).

So far, so good. But how did the “Core” of banking change in the past, and how is it changing right now? Around 25 years ago, the core banking functionalities where still core capabilities of a bank´s IT department. Around that time, external providers started to offer their comprehensive solutions. Although many banks still rely on their own Core Banking Systems today, these systems are aging and might need replacement soon (Kronberg Advisors, 2019). Therefore, it is not surprising to see more and more startups moving into the space with the aim to offer Core Banking Systems superior to the solutions offered by incumbents.

But how do these challenger Core Banking Systems compare to established offerings? They are faster, more flexible, and cheaper. Besides others, this is made possible by using new technologies that have not been around 25 years ago. First, the challengers focus on core functionalities, so banks gain flexibility and can add further third party functionalities if needed. Second, the challengers embed more openness within their architecture; this allows easier integration of external solutions and flexibility to react to regulatory changes. Third, challengers ́ solutions are often cloud-native leading to lower infrastructure requirements and implementation efforts. Fourth, the novel solutions are SaaS deployable allowing banks to be more flexible and to variabilize costs (Kronberg Advisors, 2019).

While banking in public clouds offers many benefits, it also comes with downsides. Banks might not be ready to fully rely on public clouds and would prefer to stay in control of the Core Banking System. Further, security concerns still seem to be an issue. Nevertheless, I believe that in the long term the benefits will outweigh the downsides; the technology will develop further, the financial sector will become accustomed to it, and more and more banking tasks will move to the public cloud.

Gartner, Inc 2019, Core Banking System, Gartner, Inc, viewed 18 Sep 2019, <https://www.gartner.com/it-glossary/core-banking-systems>.

Kronberg Advisors 2019, Core Banking Systems: Will Banks choose the Challengers over the Incumbents?, Kronberg Advisors, viewed 18 Sep 2019, <https://www.kronberg-advisors.com/blog/core-banking-systems-will-banks-choose-the-challengers-over-the-incumbents>

Thalhammer, K. 2019, Kernbanken Systeme, Payment & Banking, viewed 18 Sep 2019, <https://paymentandbanking.com/infografik-kernbanken-systeme/>.

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Martin Casado and Peter Lauten, both Partners at the well-known venture capital fund Andreessen Horowitz (with investments in Facebook, Twitter, Groupon, Airbnb…), recently published a post called “The Empty Promise of Data Moats” on their fund´s website. Within this post, they present a framework aiming to help understanding the data journey of a start-up building up its business and database.

They start with the “chicken-egg” problem, meaning that in a business requiring a network it can be difficult to acquire the first customers or nodes. Yet, they point out that for a business with data scale effects, the initial data required for first training (of an AI) can also be gathered by data captured from available sources such as the web. This shows that the data set required for a minimum viable product (MVP) is not enough to defend a business, as it often contains relatively cheap and publicly available data.

In the next step, Casado and Lauten point out that data acquisition costs, as opposed to traditional network effects, go up over time. This is explained by an increase in difficulty to find unique data that improves the model, as much of the newly acquired data overlaps with the existing set, which also makes the data less valuable.

As a last point, the venture capitalists mention the need of data freshness. This refers to changes in the underlying real-world state, which does not stand still. For instance, a fictive algorithm designed in the 1990s with the purpose to help users chose which clothes to wear each day would need a high amount of work and fresh data to not propose outdated outfits.

When looking at these points, one might wonder why it often seems that all value and defensibility was inherent to data. Although data plays a significant role in a company´s success, one should not think that having a lot of data translates into having long-term defensibility. Rather, a company needs to add specific strategies that allow defensibility when paired with data. These strategies are for instance the securing of proprietary data sources, understanding which data improves the core product or strategies that keep the data freshness up. Therefore, data alone are not the key to defend a business from competitors moving into its space.

Bibliography:

Martin Casado and Peter Lauten 2019, The Empty Promise of Data Moats, Andreessen Horowitz, viewed on 14 September 2019, https://a16z.com/2019/05/09/data-network-effects-moats/

Banking – the sky is the limit?

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Are data the key to defend your business?

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