As expectations rise for credible ESG performance, banks must move beyond rhetoric. Melanie Zimmerling of Sopra Steria Next explores how AI is enabling measurable impact and more authentic client engagement.
How many banks have been caught in the trap of a "green fund" that some consider insufficiently sustainable? This dissonance between corporate rhetoric and operational reality is far from trivial: 53% of consumers today view banking institutions' ESG initiatives as primarily marketing-driven. Faced with this credibility deficit, one question arises: how can banks transform their sustainability commitments into tangible value, both for their clients and their business?
Melanie Zimmerling, an expert in banking digitalisation and regulation at Sopra Steria Next, the consultancy division of the Sopra Steria Group, analyses how artificial intelligence is redefining this equation by enabling financial institutions to move from compliance to authentic value creation.
The authenticity gap
The divide between ESG promises and client perception has widened in recent years, crystallising widespread distrust. For Zimmerling, the diagnosis is unequivocal. The gap exists, she explains, "because banks talk a lot but show too little", whilst "clients expect concrete outcomes." Authentic efforts are distinguished by their integration into core business operations and their translation into real products and services, far beyond communication exercises.
This distrust feeds on recurring strategic errors that the expert clearly identifies: vague promises without concrete objectives, over-communication about minor initiatives, and above all, ignoring the negative impacts of core business activities: "Clients notice when there's no consistency between words and actions."
But how can we move towards evidence-based discourse? Measuring ESG impact is a first answer. Zimmerling emphasises the need to establish precise KPIs: "Authentic impact is measured through financed emissions, the share of sustainable assets, diversity in leadership bodies, or energy consumption reduction in daily operations." Compliance, she specifies, is limited to ticking regulatory boxes, whilst true metrics demonstrate structural transformation of portfolios and operations. And these metrics could well evolve significantly with the development of artificial intelligence.
AI as a catalyst for sustainable transformation
AI tools are indeed radically disrupting the economic equation of banking sustainability. Where ESG initiatives were perceived as cost centres, AI opens the way to new revenue models. According to Zimmerling, this technology enables "personalising sustainable investment recommendations, automating carbon tracking for clients, and creating transparency in ESG data." AI-driven tools are thus already concretely guiding clients towards greener portfolios, generating measurable engagement and loyalty.
This transformation is no longer theoretical: 58% of banks now consider sustainability-focused technologies as significant revenue sources. They are creating new products (sustainable mortgages, ESG investment funds, etc.) and establishing measurable trust that translates into long-term loyalty. The business model is shifting.
Prioritising technology investments depends on the targeted time horizon. The expert distinguishes two phases: in the short term, operational efficiency and regulatory reporting deliver rapid, quantifiable returns. But it's in the long term that AI-driven investment recommendations and carbon footprint tracking reveal their full potential in terms of client value creation and sustainable competitive differentiation.
Personalisation and accessibility: Democratising ESG
The main obstacle to mass adoption of sustainable solutions paradoxically remains complexity. Many clients lack basic ESG knowledge, hindering their engagement. AI provides a concrete solution by enabling, according to Zimmerling, the ability to "translate complex ESG data into simple visuals, advice, or product comparisons." Chatbots and personal finance applications transform abstract concepts into informed decisions, showing clients "the impact of their spending or investments in an easy and engaging way."
Beyond education, personalisation opens a new commercial field. AI enables a granular approach that can transform the banking relationship into personalised support for the sustainable transition.
Data transparency constitutes the third pillar of this transformation. The expert emphasises that "AI helps clean, structure and analyse enormous volumes of ESG data", making reporting more reliable. This processing capability allows clients to precisely visualise how their money generates impact, thus bridging the trust gap initially identified.
From regulatory constraint to strategic asset
ESG reporting, long perceived as an administrative burden, is metamorphosing into a strategic management tool. For Zimmerling, automation is changing the game: "AI can automate reporting, identify risks and opportunities in portfolios, and simulate scenarios." This transformation frees teams from repetitive tasks and elevates reporting to a decision-support tool, enabling anticipation of regulatory developments and early detection of opportunities.
Success KPIs are evolving accordingly. Banks now track assets under management in ESG products, green loan adoption, cross-selling rates and client satisfaction scores related to sustainability. These commercial metrics are progressively replacing purely regulatory indicators, signalling a fundamental shift in institutional approach.
Client segmentation moreover reveals significant disparities. According to Zimmerling, "younger generations, high-net-worth individuals and companies with strong ESG commitments are most willing to pay for sustainable solutions that align with their values." These clients no longer merely express preferences: they accept paying a premium for products aligned with their values, creating a viable and growing market.
The balance between profitability and authentic impact rests on clear strategic alignment: "Offering products that are both profitable and sustainable, measuring real results, and being transparent about trade-offs", summarises Zimmerling. This pragmatic approach avoids greenwashing pitfalls whilst maintaining economic viability. But how can this be implemented?
Roadmap and outlook
For banks starting their transformation, Zimmerling recommends a progressive three-stage approach: define ESG priorities and data requirements, build quick-win AI use cases such as reporting automation, then scale up towards personalised client products where the true differentiation potential lies.
The main challenge lies neither in technology, regulation, nor even client adoption. The expert is categorical: "All four matter, but the biggest challenge is cultural change." The technology exists, but its appropriation requires trust and buy-in at all levels of the organisation. Hence the crucial importance of partnerships: "No bank can do this alone, because sustainability requires high-quality data and innovation." Collaborations with fintechs, ESG data providers and technology platforms are becoming essential.
The banking landscape over the next three years will be shaped by this ability to create authenticity through technology. Zimmerling thus anticipates a major reconfiguration: "Banks that use AI for transparent ESG data, personalised products and authentic impact will stand out." In three years, she predicts, sustainability will no longer be merely a compliance matter but a major differentiator for client trust and market growth. Institutions that manage to bridge the authenticity gap will not simply meet regulatory expectations: they will create a new source of sustainable competitive advantage.