The European banking sector is facing a new wave of consolidation. The Netherlands is unwinding its state stake in ABN Amro, which means foreign major banks may consider an acquisition (Rotteveel, 2024). Moreover, UniCredit has unexpectedly shown interest in Germany’s Commerzbank (Couwenbergh et al., 2024). These developments show that consolidation is on the agenda. But what does this mean for the use of customer data, and what risks does AI-driven decision-making pose?
Banks are increasingly consolidating to strengthen their competitive position and benefit from economies of scale. Technological advances are accelerating this trend, as large banks have more resources to invest in technology (Dontis-Charitos & Leong, 2023). As a result, the use of disruptive technologies, such as AI, is growing (Agarwal et al., 2021). Banks are increasingly operating from an ‘AI factory,’ where AI drives decisions (Iansiti & R. Lakhani, 2021).
As this consolidation takes place, big banks have huge data sets that can be used to analyse customer behaviour, make credit decisions or offer personalised services. This sounds efficient and interesting but also raises questions. Using personal data in AI models also carries risks. For example, when algorithmic bias can influence financial decision-making which can lead to uneven or biased outcomes.
In my view, it is crucial to properly identify these potential dangers and, where necessary, start creating legislation from higher authorities. Indeed, it is important that there is more transparency in the future about how AI models use personal data. Although laws such as Article 15 of the AVG give the right to access, true transparency becomes more complex when information is deeply embedded in AI models (Uitvoeringswet Algemene Verordening Gegevensbescherming, 2021). This is precisely why stricter regulation and supervision is indispensable to protect the interests of individuals.
In conclusion, consolidation of European banks and the increase in application of AI within banks presents opportunities, but also risks for the use of personal data. While technology promotes efficiency, biases in AI systems can lead to unfair outcomes. Therefore, strict regulation and increased transparency is necessary to safeguard the interests and privacy of individuals.
References
Agarwal, A., Agarwal, Singhal, C., Singhal, & Thomas, R., Thomas. (2021). AI-powered decision making for the bank of the future. In Global Banking & Securities. https://www.mckinsey.com/~/media/mckinsey/industries/financial%20services/our%20insights/ai%20powered%20decision%20making%20for%20the%20bank%20of%20the%20future/ai-powered-decision-making-for-the-bank-of-the-future.pdf
Couwenbergh, P., Dohmen, J., & Hekking, H. D. (2024, September 13). Fusiegolf? EU-landen niet happig op buitenlandse kopers van ‘hun’ banken. Het Financieele Dagblad. https://fd.nl/bedrijfsleven/1530458/fusiegolf-eu-landen-niet-happig-op-buitenlandse-kopers-van-hun-banken
Dontis-Charitos, P., & Leong, S. H. (2023). Bank consolidation, connectedness, and systemic risk. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4468356
Iansiti, M., & R. Lakhani, K. (2021, January 21). Competing in the Age of AI. Harvard Business Review. https://hbr.org/2020/01/competing-in-the-age-of-ai
Rotteveel, M. (2024, September 12). Staat neemt verlies op ABN Amro en voedt speculatie over een overname. Het Financieele Dagblad. https://fd.nl/politiek/1530683/staat-neemt-verlies-op-abn-amro-en-voedt-speculatie-over-een-overname
Uitvoeringswet Algemene verordening gegevensbescherming. (2021, July 1). https://wetten.overheid.nl/BWBR0040940/2021-07-01#Hoofdstuk2
