Are AI And ESG Redefining Transfer Pricing Roles In Banking

The Banking Operating Model, Transformed

Banking groups face growing pressure to modernize their operating models to keep pace with digitalization, shifting customer expectations, and heightened environmental, social, and governance (ESG) requirements.

Intensifying competition from financial technology companies and digital-only institutions has accelerated the shift among banking groups to move away from traditional brick-and-mortar banking models. This shift includes neobanks, which provide app- or web based services through low-cost operating models, and challenger banks, which leverage digital platforms, agile product development, and customer-focused experiences to compete with traditional banks. Large traditional banking groups now face a customer base that demands seamless, digital-first banking experiences, challenging a model historically centered on branch networks and personal relationships with banking advisers.

In response, banks are investing heavily in the centralization of technology infrastructure, data analytics, artificial intelligence, and digital platform capabilities to enhance customer experience, improve operational efficiency, and foster innovation across business lines. This transformation is reshaping how banking groups create value and organize key functions, with significant implications for profit allocation and the transfer pricing treatment of centralized activities.

The Growing Importance of AI in Banking

In recent years, several leading banking and insurance groups have launched large investment programs to integrate AI into their core business operations, positioning it as a central pillar of their long-term strategy. Institutions such as AXA, Crédit Agricole, the BPCE Group, BNP Paribas, and the Generali Group have invested substantially in developing proprietary generative AI capabilities based on their internal data assets, often through partnerships with technology providers such as Microsoft, Anthropic, and Mistral AI.¹

The rapid advancement of AI has also prompted changes in governance structures. In June 2025² Société Générale, France’s third largest bank, announced the creation of a scientific advisory board comprised of leading experts in AI, technology, and societal issues to support the group’s long-term transformation and advise on the responsible deployment of emerging technologies. The bank also established a dedicated entity, SocGen AI, to manage AI integration across business functions.³

These developments frequently require substantial investments at the central level, particularly in technology infrastructure, data management, model development, and governance.

Concurrently, central entity roles may evolve considerably faster than traditional banks. A central entity that develops and controls proprietary AI models, manages strategic data assets, establishes pricing or risk parameters, and oversees AI governance may perform key value creating functions and control economically significant risks. Compared with more traditional models, in which the central entity primarily performs routine administrative or support functions, the enhanced role of a central entity and increased centralization of activities may justify a higher profit allocation to that entity.

Concerning transfer pricing, these developments challenge whether current remuneration models accurately reflect central banking entities’ functions, assets, and risks. Updating a value chain analysis may help address those questions and refine transfer pricing policies.

ESG Drives Organizational Change

Alongside shifting customer demands and increasing competition, other factors — such as more stringent ESG requirements — will likely drive banking institutions to restructure their activities and operating models. Recent communications from major French banking groups including BNP Paribas’ Growth, Technology, Sustainability strategy⁴ and BPCE’s Vision 2030 and first corporate sustainability reporting statement⁵, suggest that ESG considerations have become a strategic priority and a key component of regulatory compliance.

ESG-related initiatives can greatly affect banking institutions’ operational models by influencing credit underwriting, climate stress testing, capital allocation, product development, and governance structures. The European Banking Authority’s guidelines on the management of ESG risks require financial institutions to identify, measure, manage, and monitor ESG risks and to embed those efforts within governance arrangements, risk management processes, and strategic planning.⁶ Effective management of ESG risks and commitments protects an institution’s reputation, builds stakeholder trust, and ensures long-term competitiveness.⁷

Consequently, banking groups increasingly integrate ESG considerations into their value chains and financing decision-making processes. This requires centralized oversight of ESG data, methodologies, governance, transition strategies, and risk management across the entire institution.⁸ Institutions have invested significantly in ESG assessment methods and in integrating climate-related considerations into risk management and evaluation frameworks, reflecting both evolving regulatory requirements and increasing investor expectations regarding the identification, measurement, and management of ESG-related risks.

Investor expectations are reflected in the standards developed by the International Sustainability Standards Board, including IFRS S1 and IFRS S2, while supervisory expectations are set out in the European Banking Authority’s guidelines on the management of ESG risks and its climate-related supervisory framework. Accordingly, ESG considerations have become an integral part of banking groups’ business strategies, risk management frameworks, and operational decision-making processes.

Faced with these developments, many banking groups have adapted their governance structures by establishing dedicated ESG functions and committees, often with direct reporting lines to senior management and board level decision-makers. Because ESG-related activities are now central to risk management and business decisions, a question arises: should these ESG activities be treated as routine support activities (or shareholders’ activities) or value generating services? This evolution may have considerable transfer pricing implications, particularly because centralized ESG functions influence lending decisions, risk management, capital allocation, and regulatory compliance across multiple jurisdictions. Banks may need to adjust their transfer pricing policies to appropriately compensate the central functions that manage ESG strategy, data, governance, reporting, risk management, climate-risk modeling, and financing decisions.

Transfer Pricing Implications

The Traditional Role of Central Entities

Traditionally, central entities within banking groups have been remunerated primarily for management, support, and intragroup financing functions. Under OECD transfer pricing guidelines, these activities are often viewed as routine or low-value-adding services,⁹ and compensated accordingly, often through cost based transfer pricing methods. Central entities also commonly bear groupwide regulatory, governance, compliance, risk management, treasury, legal and other services, with those costs typically allocated to the benefiting entities based on transfer pricing policies.

While banking groups have long relied on internally developed assets such as risk models, IT systems, and customer relationship frameworks, these assets have generally been considered supporting operational activities rather than revenue-generating activities. Consequently, central entities are typically compensated as service providers rather than strategic value creators.

AI’s Effect on Value Creation and Risk Allocation

The rapid adoption of AI challenges the relevance of traditional transfer pricing frameworks. When central functions develop and deploy costly AI solutions that require long-term investments and generate competitive advantages, productivity gains, or future revenue streams, their value increases, necessitating a review of existing transfer pricing models.

Automating routine functions and centralizing decision-making capabilities within AI centers of excellence may increase banking groups’ reliance on centrally developed intangible assets. AI strategies are also intrinsically linked to the collection, management, and exploitation of customer and transactional data, which may heighten cybersecurity, operational, and regulatory risks. For instance, AI systems supporting credit assessment, fraud detection, AML/CFT monitoring, and customer analytics require centralized governance over data quality, model validation, cybersecurity controls, and regulatory compliance frameworks. Failures in those areas may lead to financial losses, regulatory sanctions, and reputational damage.

The European Banking Authority’s 2024 Risk Assessment Report observes that AI is increasingly embedded in core banking functions, including credit scoring, fraud detection, AML/CFT monitoring, regulatory reporting, and customer support. This demonstrates that AI has evolved beyond a back-office efficiency tool into a technology supporting economically significant business functions. This centralization also concentrates responsibility for data governance, model validation, cybersecurity, and regulatory compliance at the group level.¹⁰

AI implementation may create dependencies on external technology providers and expose banking groups to operational, financial, legal, and reputational risks.11 Simultaneously, large financial investments in AI models — subject to rapid technological obsolescence and dependent on scarce expertise — increase the risk of economic losses.

When central entities determine AI investment strategies, develop and maintain proprietary models, control key datasets, establish model parameters, and manage associated risks, they may perform functions beyond those of a traditional service provider. It may then be appropriate to reconsider whether a routine cost-plus remuneration remains consistent with the arm’s-length principle or whether a higher level of remuneration is necessary to reflect the functions performed, assets employed, and risks assumed.

ESG’s Effect on Value Creation and Risk Allocation

ESG initiatives — much like AI — demand significant investments in centralized governance, data infrastructure, risk methodologies, and group-wide policies that influence business decisions across multiple jurisdictions. For banks, centralizing ESG strategy, governance frameworks, societal and ethical policies, and regulatory compliance programs may help manage one of their greatest threats: reputational risk. By establishing centralized oversight over ESG policies, methodologies, disclosures, and internal controls, banking groups can promote consistency between their public sustainability commitments and business practices, thereby preventing greenwashing and mitigating regulatory and reputational risks.

ESG initiatives may also facilitate access to sustainable finance markets and ESG-oriented sources of capital, strengthen brand value, improve stakeholder confidence, and enhance the resilience of banking groups by reducing exposure to climate-related and reputational risks. ESG considerations are also increasingly embedded in lending policies, risk management frameworks, and strategic decision-making processes. For transfer pricing, this aligns central ESG functions more closely with value creation, which may in turn create a need to revise the impact of ESG factors in the value chain for the core banking businesses and to revisit related transfer pricing policies.

When a central entity designs, implements, and manages ESG policies while bearing the associated costs and risks, it may no longer be appropriate to characterize those activities as routine support functions. To the extent that these activities influence core business decisions, involve proprietary methodologies or data, or require the control of significant risks, they may justify remuneration that differs from that traditionally associated with low-value-adding intragroup services.

Generally, AI and ESG developments could significantly alter the risk profile of central entities, potentially outpacing the evolution of traditional transfer pricing models. Consequently, the transfer pricing models remunerating central entities may necessitate reassessment.

Assessing the Transfer Pricing Impact

Assessing the transfer pricing implications of these developments presents numerous practical challenges.

First, banking groups may face difficulties identifying and isolating the costs associated with newly developed intangible assets within central entities. Measuring the benefits generated by centralized AI platforms or ESG frameworks across multiple business lines and jurisdictions can also be highly complex. For instance, an AI model developed centrally may be deployed by retail, corporate, and private banking businesses in several jurisdictions, while a centralized ESG framework may simultaneously influence lending decisions, capital allocation, regulatory compliance, and sustainable finance activities throughout the group. In such cases, the resulting benefits are often shared, indirect, or long-term, making it difficult to determine the arm’s-length value of the services or the appropriate allocation of returns among group entities.

Second, central entities frequently perform functions involving support and governance activities, treasury operations, intragroup financing, and customer-facing financial activities, among others. Distinguishing between routine and nonroutine contributions within those integrated operating models may prove difficult.

Other challenges include potential discrepancies between AI development expenditures and actual productivity, the unresolved nature of AI-related intellectual property rights, conflicts when integrating new technologies and legacy systems, and navigating cross-border issues regarding tax and regulatory compliance requirements.

Consequently, determining whether AI- and ESG-related activities warrant changes to existing transfer pricing arrangements requires a detailed analysis of the bank’s value creation process, control over risk, and economic significance of centrally performed functions. The analysis may involve a comprehensive value chain review to determine whether existing transfer pricing policies reflect the functions performed, assets employed, and risks assumed by the relevant entities.

Conclusion

Banking groups are undergoing a major transformation in which central capabilities — particularly AI platforms and ESG governance — now drive value creation, risk management and decision-making. As a result, the role of central entities in transfer pricing will likely rapidly evolve to adapt to changing business models, value chains, and risk management processes.

When central entities undertake substantial investments, control economically significant risks, and manage strategic intangible assets, it may no longer be suitable to view them solely as providers of routine support services. The functions performed, assets employed, and risks assumed may sometimes justify remuneration that differs from the traditional approaches applied to low-value-adding intragroup services.

As AI and ESG become more critical, companies will face mounting pressure to reevaluate the central transfer pricing, despite considerable practical challenges in quantifying value creation and assigning returns. Consequently, banking groups should review their transfer pricing frameworks to ensure that they align with the economic realities of their evolving operating structures.

Footnotes

1 Danièle Guinot, “How AI Is Shaking Up the Finance Professions,” Figaro Economie Enterprises (Jan. 23, 2026) (translated to English by the authors).

2 Société Générale, “Societe Generale Unveils the Composition of Its Scientific Advisory Council” (June 18, 2025).

3 Société Générale, “Innovation and Digital Serving the Client Experience” (n.d.).

4 BNP Paribas, “BNP Paribas CSR Strategy: Investors’ Presentation” (Sept. 2025).

5. Groupe BPCE, “Groupe BPCE Publishes Its First Sustainability Statement Pursuant to the European CSRD Directive and Reaffirms the Impact Strategy Embedded in Its Vision 2030 Project” (Mar. 31, 2025).

6 European Banking Authority, “The EBA Publishes Its Final Guidelines on the Management of ESG Risks” (Jan. 9, 2025).

7 Radka Margitova et al.,“Compliance Playbook to Accelerate Bank ESG Risk Management,” Oliver Wyman (2024).

8 European Banking Authority, supra note 6.

9 OECD, “OECD Transfer Pricing Guidelines for Multinational Enterprises and Tax Administrations 2022” (Jan. 20, 2022).

10 European Banking Authority, “Risk Assessment Report — November 2024 — Special Topic — Artificial Intelligence” (Nov. 2024).

11 Xavier Boileau and Jean-Paul Mazoyer, “Artificial Intelligence and Financial Service Risks,” 159 Revue d’Economie Financiére (2025).

Originally published by Tax Notes International.

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