4. 4
AI is changing the physics
of financial services….
weakening the bonds that have
historically held together financial
institutions, while creating new
centers of gravity where new and old
capabilities are being combined in
unexpected ways
From the work of Alan Turing in the 50ies laying the ground for robotics and AI to mainstream success (DeepBlue in chess, Go victory or even poker today), AI has gone through peaks and drought over the last 70 years.However, the view today is that it is here to grow at an unprecedented rate with technology solutions delivery capacity unseen before, a growing pool of talent and also strong investment from large institutions betting heavily on AI and aiming at positive ROI.
There is no clear agreement on what AI is but it is also clear that people are meaning something when they refer to “AI”
When business talks about AI, they are talking about a set of capabilities that allows them to run their business in a new way
Pattern detection Recognize (ir)regularities in data
Foresight Determine the probability of future events
Customization Generate rules from specific profiles and apply general data to optimize outcomes
Decision-making Generate rules from general data and apply specific profiles against those rules
Interaction Communicate with humans through digital or analogue mediums
Today, one can easily find white papers and reports on the emergence of AI use cases
And technical foundations are also well documented to help you implement and optimize AI solutions
But how will this technology change the shape and structure of financial institutions and the competitive nature of financial markets?
Scale of assets Economies of scale presented a cost advantage Scale of data As AI drives operational efficiency, economies of scale alone will not sustain cost advantages
Mass production Physical footprint and standardized products drove cost-effective revenue growth Tailored experiences AI allows the scaled distribution of highly customized products and personalized interactions
Exclusivity of relationships direct access to many markets and connections to investors was a critical differentiator Optimization and matching Connections are digitized, increasing the importance of optimizing the best fit between parties
High switching costs High barriers to switching providers drove customer retention High retention benefits Continuously improving product performance to offer superior customer outcomes and new value will keep clients engaged
Dependence on human ingenuity Processes scaled through additional labour and functional training => Value of augmented performance The interplay of strengths across technology and talent amplifies performance
Cost to Profit center: turn AI-enabled operations into external services, both accelerating the rate at which these capabilities improve and compelling others to become consumers of those capabilities to avoid falling behind
Customer Loyalty: opportunity for institutions to escape a “race to the bottom” in price competition by introducing new ways to distinguish themselves to customers
Self-driving finance: Future customer experiences will be centered on AI, which automates much of customers’ financial lives and improves their financial outcomes
Collective solutions for shared problems: Collaborative solutions built on shared datasets will radically increase the accuracy, timeliness and performance of non-competitive functions, creating mutual efficiencies in operations and improving the safety of the financial system
Bifurcation of market structure: As AI reduces search and comparison costs for customers, firm structures will be pushed to market extremes, amplifying the returns for large-scale players and creating new opportunities for niche and agile innovators
Uneasy data alliances: In an ecosystem where every institution is vying for diversity of data, managing partnerships with competitors and potential competitors will be critical, but fraught with strategic and operational risks
The power of data regulators: Regulations governing the privacy and portability of data will shape the relative ability of financial and non-financial institutions to deploy AI, thus becoming as important as traditional regulations to the competitive positioning of firms
Finding a balanced approach to talent: Talent transformation will be the most challenging speed limit on institutions’ implementations of AI, putting at risk the competitive positioning of firms and geographical areas that fail to effectively transition talent alongside technology
New ethical dilemnas: AI will necessitate a collaborative re-examination of principles and supervisory techniques to address the ethical grey areas and regulatory uncertainties that reduce institutions’ willingness to adopt more transformative AI capabilities. Safety of the financial markets as well as of the customers and workforce are important questions and remain grey areas that need to be addressed
While the potential benefits of AI will be striking, its potential risks to societal and economic well-being are too great to be left unaddressed
Be first and best in the deployment of AI Because those institutions that are able to establish an early lead in using AI as a competitive differentiator will be rewarded by virtuous feedback cycles that compound their advantages and leave second movers struggling to catch up
Collaborate with many stakeholders Because unlocking the full potential of AI requires an extensive network of partnerships and only collective efforts by financial institutions, alongside regulators and the broader public sector, can ensure that the expanded use of AI in finance benefits society as a whole