Are the canonical observations of Abernathy and Utterbach (1975) on the transition from experimental innovation to applied innovation still valid half a century later? Will the example of AI applied to banking activities make it possible to verify this?
Since the 2020s, banks have been engaged in a vast process of AI-driven innovation of their products, services and systems, as well as of their relationships with their customers, their staff, their partners and regulators. These innovations aim to personalize products and services according to customer segments (households, businesses, local authorities, governments), their risk-return profiles, and the types of services provided (payment, credit, investment, etc.). They are seeking to diversify the current banking interface and integrate the payment function into various objects (telephones, homes, vehicles, glasses, watches, etc.). Their aim is to adapt credit and insurance offers to each purchase, and to automate investment management according to the risk profiles of savers and their ESG scoring requirements. They aim to ensure that each transaction is supported by a “robot advisor” or an AI-“augmented” banking advisor, capable of carrying out prospecting, projections, simulations and training. Thanks to AI, they are striving to better support start-ups in their “valleys of death” and industrial or financial groups in their merger and acquisition projects (especially cross-border), with the help of structured multi-disciplinary networks. In the structured products market, AI applications already allow for the automatic selection of counterparties, an analysis of the levels of protection and barriers offered, the coupons proposed, the pricing of embedded options, the strength of balance sheets and the rating of issuers, and the depth and quality of the secondary market. But above all, AI makes it possible to ensure better security for customer data, processing, transactions, settlement-delivery and securities custody. Achieving these objectives already requires the most advanced AI applications, such as biometric identification, automatic flow traceability, scheduled data dissemination, the systematisation of smart contracts (in MNBC and tokens), the control of loans financing investments with ESG impact (such as Tree Token), the development of “complementary currencies” (such as Bancor) and micro-payments promoting the inclusion of unbanked people (such as Arcadia Blockchain), etc.
The staff of each bank must therefore demonstrate “innovism” (Phelps et al., 2020), so that their bank is not a “follower” but a “pioneer” in the application of AI. Innovation encompasses the ability to anticipate new, abnormal or crisis situations by setting up “innovation laboratories”, creating a financial “imaginarium” and stimulating the “desire to create” among staff at all levels. It therefore seems that, in the light of this example, the technological acculturation of companies is both more comprehensive and faster than it was during the “thirty glorious years”.
Abernathy, W. J., & Utterback, J. M. (1975). A Dynamic Model of Process and Product
Innovation. Omega, 3(6), 639-656.
Phelps, E., Bojilov, R., Hoon, H. T., & Zoega, G. (2020). Dynamism: The Values That
Drive Innovation, Job Satisfaction, and Economic Growth. Cambridge, MA: Harvard University Press.
Jean-Jacques Pluchart