The report titled “The New Physics of Financial Services” explores how AI will transform the realities of financial institutions. This transformation will be brought about by radically changing front- and back-office operations, creating major shifts in the structure and regulation of markets, and raising critical challenges for society to resolve.
“AI is rapidly reshaping the attributes necessary to build a successful business in financial services,” says Rob Galaski, Deloitte Global Banking & Capital Markets Consulting leader. “As AI drives operational efficiency, economies of scale alone will not sustain cost advantages. Consequently, in the future, financial institutions will be built on scale of data and the ability to leverage that data.”
“Increasingly bifurcated markets are already emerging where data sharing is critical to competitive success. First movers are positioned to distinguish themselves by delivering better advice, constant presence, and curated ecosystems. Firms that lag behind are finding that their old strengths may not keep them as competitive as they once were.”
Shaping the future of financial services
“The transformative impacts of AI will necessitate a level of public-private commitment. This is vital for understanding and continuously shaping the future of financial services,” says R. Jesse McWaters, Financial Innovation Lead at the World Economic Forum.
“Emerging questions about consumer protections and systemic risks remain the purview of regulators. Therefore, effectively responding to these challenges will require collaboration between public and private stakeholders. This is necessary in order to resolve regulatory uncertainties and manage the risks of AI in financial services.”
The report identifies nine key findings that demonstrate how AI is changing the physics of finance. It is weakening the bonds that have historically held together financial institutions. Simultaneously, it creates new centers of gravity where new and old capabilities are being combined in unexpected ways.
Among these insights, four core findings specifically explore how AI is radically transforming the front- and back-office operations of financial institutions.
1. Cost centers to profit centers
AI enabled back-office functions will allow financial institutions to turn their centers of excellence into services, while pushing them to outsource most other capabilities.
As financial institutions move towards a back-office as-a-service model, these processes will continuously learn and improve using data from its collective users. As a result, this both accelerates the rate at which capabilities improve while necessitating competitors to become consumers of that capability to avoid falling behind.
2. A new battlefield for customer loyalty
Past methods of differentiation for financial institutions—such as cost, speed and access—are eroding. AI is giving rise to a new set of competitive factors on which financial institutions can distinguish themselves to customers.
For example, the ability of institutions to optimize financial outcomes by tailoring, recommending and advising customers will allow them to compete on value offered. Additionally, the ability to engage users and access data through ongoing and integrated interactions beyond financial services will allow them to better retain customers.
Furthermore, curating ecosystems by bringing together data from multi-dimensional networks that include consumers, corporate clients and third parties will allow financial institutions to offer differentiated advice and improve performance.
3. Self-driving finance
Financial advice, part of every product, is often generic and impersonal. It also tends to be overly reliant on subjective advice from different customer service agents. A self-driving vision of finance could transform the delivery of financial advice, centering customer experiences around AI.
In this vision, individuals will increasingly interact primarily with a single platform or agent who will provide recommendations about the types of products they should engage with and advisory services around those products.
AI enables this vision in three key ways: empowered platforms which can compare and switch between products and providers; increasingly personalized advice based on data; and continuous optimization through algorithms which will automate most routine customer decisions.
It is unclear who will deliver the self-driving agent, whether it is incumbents, new entrants, or large technology companies. However, it is clear that self-driving finance will upend existing competitive dynamics, pushing returns to the customer experience owner while commoditizing all other providers.
4. Collective solutions for shared problems
While AI presents increased opportunities for competition, it also presents a strong mechanism to collaborate as the value of shared datasets is tremendous. There is great potential for cross-institutional collaboration on issues such as fraud prevention and anti-money laundering controls. They often run inefficiently and ineffectively today, and could benefit from collaboration.
Collaborative solutions built on shared datasets will radically increase the accuracy, timelines, and performance of non-competitive functions. Consequently, this creates mutual efficiencies in operations and improving the safety of the financial system.
Additionally, the report explores major shifts in the structure and regulation of financial markets and critical challenges for society to resolve.
- Bifurcation of market structure. As AI reduces search and comparison costs for customers, firm structures will be pushed to market extremes. Consequently, this will amplify the returns for large-scale players and creating new opportunities for 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. However, it will be 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, it becomes as important as traditional regulations to the competitive positioning of firms.
- Adapting talent strategies. Talent transformation will be the most challenging road block on institutions’ implementation of AI. It puts at risk the competitive positioning of firms and regions that fail to effectively transition talent alongside technology.
- New ethical dilemmas. Global communities have a joint interest in mitigating the risks and harms of rapid technological development. AI will necessitate a collaborative reexamination of principles and supervisory techniques. This is necessary to address the ethical concerns and regulatory uncertainty that are hindering institutions’ willingness to adopt more transformative AI capabilities.