Chapter 12: Learnings and Outlook
Chapter 12: Learnings and Outlook 12.1 Generalising Findings Across the Financial Services Industry more likely than other digital platforms to exhibit a ‘superstars and long tails’ set of dynamics. Under this dynamic, a few large firms establish an entrenched dominance in a product or service, and the remaining firms engaged in this space satisfy themselves with serving as highly specialised niche providers.
The components necessary to build an effective AI model are generalisable across sub-sectors of Financial Services, and indeed across every industry; however, successful ways of applying these models to drive commercial success are likely to differ across sectors and entity types. Generalisable properties are as follows: • AI models are a product of the combination of algorithms and training data. While the algorithms enabling AI are complex, the majority of underlying resources are open source (e.g., TensorFlow). As a result, the primary differentiation between strong and weak AI models is the data that can be used to train it. This means that for any firm seeking to develop a successful AI model, securing training data is critical. Ideally, this training data would be a constantly refreshing (and growing) flow, not a ‘one time’ stock of data, thus allowing the AI model to learn and develop in response to the evolving data flow.
At the same time, the results of this study have shown that many aspects of what makes for a successful implementation of AI may be contingent on company sizes, company maturity, existing organisation structures, as well as being specific to certain financial service sectors.
• The most competitively defensible AI models in any industry establish a ‘moat’ in one of two ways. The first is to secure a unique and useful set of data from which they can exclude other parties. The second is to leverage the ‘AI flywheel’ effect to continuously draw in more training data, and in doing so to establish a scale of data that is difficult for any newcomer to compete with.
For example, many players in Investment Management are clearly focused on identifying unique training data inputs (e.g. satellite imagery) in order to improve the accuracy of their stockpicking models. Meanwhile, network players in Payments and Capital Markets are seeking to leverage the scale of data flowing through their systems to create new advisory and value-added security (e.g. anti-fraud capabilities).
• The overriding need for data makes digital platform models that form a data-rich interface between buyers and suppliers for a set of services highly amenable to the development of AI models. This is well illustrated in the tech sector by players such as Google who have leveraged the selfreinforcing characteristic of AI at scale to establish dominance in search. Areas where digital platforms and AI meet may be even
Moreover, while AI Leaders appear to be using more complex technology compared to Laggards, this higher degree of sophistication follows from the fact that AI Leaders have been able to create viable use cases for these technologies and overcome pertaining hurdles such as acquiring data, talent, and trust from stakeholders. Employing state-of-the-art technology is thus secondary to identifying the most profitable use cases of AI (which, as suggested by various
While the fundamental dynamics of AI may be consistent across industries, it is not clear how the pressures they create will reshape the structure and competitive dynamics of the financial sector, nor can it be concluded that they will have the same impact across multiple subsectors of Financial Services.
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