Sopra Steria's Digital Banking Experience Report is an annual update on the banking industry and its trends, with a strong emphasis on innovation through digitalisation. The report aims to evaluate banks' global strategic focus and future readiness in digital banking and financial ecosystems. Read on for some of the highlights from the 2025 edition.
AI at the heart of operational efficiency
The research indicates that AI will usher in substantial improvements to banks’ operational efficiency. Some 61% of decision-makers believe AI will substantially impact software development efficiency, updating legacy code, and enhancing Know Your Customer (KYC) and Anti-Money Laundering (AML) processes.
Over half of leaders (59%) forecast that generative AI will significantly improve fraud prevention and management applications. This shift from reactive to proactive protection represents a profound change in banking security.
AI for new user experiences
AI can fundamentally change how banks engage with customers around their finances: indeed, 60% of leaders anticipate that it will revolutionise the creation of personalized experiences at scale. Imagine a world where your banking app anticipates financial stress before it occurs by detecting a pattern of declining balances, upcoming large payments, and changing spending habits. Then, it proactively suggests personalized adjustments three weeks before your account would typically go into overdraft. This isn't science fiction, but the emerging reality of AI-driven financial well-being tools that can take banking from reactive crisis management to proactive financial wellness coaching.
Perhaps most significantly, AI-powered tools are also rewriting the narrative of financial inclusion. For decades, traditional credit systems have underserved significant portions of the population. Now, banks are developing new credit assessment methodologies that look beyond conventional metrics. By analysing real-time salary flows, consistent rent payment patterns, and other non-traditional indicators, financial institutions are opening doors previously closed to underserved populations. This approach isn't just socially beneficial: it's smart business, expanding the customer base while managing risk more effectively through more comprehensive data analysis.
Implementing these alternative credit scoring methods is particularly common among mature and future-ready institutions. These institutions are leading the charge by recognising that financial well-being—a state where customers feel secure with their finances and can meet their needs and goals—is increasingly important. We found that 52% of firms are already taking action to enhance this.
As a result, the vast majority of banks expect to allocate budget increases of 10% or more to emerging technologies like AI-enabled assistants and chatbots, recognising that investment in these technologies isn't optional but imperative in an increasingly competitive landscape.
Navigating the implementation maze
Yet, this journey toward AI-powered banking isn't without its challenges. Data privacy and security concerns loom large, and most organisations are looking to improve their user consent management for effective personal data sharing. This highlights the delicate balance banks must strike between leveraging customer data for better service and respecting privacy boundaries.
AI model bias and transparency issues present additional hurdles. Nearly half of decision-makers say that a key concern is deploying AI appropriately to avoid bias while ensuring transparency and interpretability. The spectre of 'black box' algorithms making critical financial decisions without explainable logic threatens both regulatory compliance and customer trust.
Additionally, 51% of decision-makers highlight compliance and security as top concerns when implementing private AI models, reflecting the complex intersection of innovation, regulation and risk management. This is compounded by the finding that 49% of respondents view the complexity of integrating private generative AI models with existing enterprise applications as a significant challenge.
Cost factors cannot be overlooked, either. 49% of banks agree that developing and maintaining private generative AI models requires substantial investment in hardware, software, and human resources. Meanwhile, 52% consider scalability a significant challenge as data volumes increase and computational demands grow.
The hybrid approach
These challenges, however, should not be viewed as roadblocks but as necessary stepping stones on the path to transformation. The emerging consensus across the banking sector suggests a future defined not by AI replacing human expertise, but by sophisticated human-machine collaboration. Our research indicates that while 62% of decision-makers expect significant impact from AI, they simultaneously recognise the irreplaceable value of human judgment in complex scenarios. This hybrid approach, what industry analysts increasingly refer to as 'augmented intelligence', combines AI's computational power with human contextual understanding, ethical reasoning, and relationship management.
By implementing robust governance frameworks and maintaining meaningful human oversight, banks can harness AI's analytical power while ensuring decisions remain fair, ethical, and aligned with organisational values. This approach is essential for building and maintaining customer trust, which remains a critical competitive advantage.
Orchestrating the future
This human-AI partnership signals a fundamental paradigm shift in how banking is conceptualised and managed. The journey isn't without its challenges, but the potential rewards (for institutions, customers, and the broader financial ecosystem) are substantial. As we navigate towards this exciting frontier, the winning formula will combine technological innovation with human wisdom, creating financial systems that are not only more efficient but profoundly more human in their protection of customer well-being.
To go further, read our Digital Banking Experience Report 2025.