Banks race to modernize cores for AI scale

By early 2026, industry reports like Temenos underscored that while many banks had invested heavily in enhancing digital front ends, they largely neglected the modernization of their core banking systems. This oversight has become increasingly unsustainable as outdated back-end infrastructures with poor data governance and inflexible architectures limit banks' ability to scale AI initiatives and monetize digital services, risking a loss in competitiveness as technology choices now have long-term strategic implications.

Visa’s Pismo executive Leonardo Collado highlighted that legacy core banking and payment systems, originally designed for batch processing and slower settlements, are now starkly incompatible with the demands of AI adoption and real-time payment innovations. The high maintenance burden—often consuming up to 70% of IT budgets—has elevated infrastructure modernization from a technical challenge to a strategic boardroom priority, as banks must move beyond mere stability to deliver speed, simplicity, and intelligent customer experiences.

Many banks remain tethered to monolithic core systems built on decades-old mainframes, where incremental layering of new technologies has created complex, fragile infrastructures that struggle to meet modern customer expectations for real-time information, payments, and AI-driven capabilities. Fear of high costs, security risks, and scalability issues has historically deterred comprehensive overhauls, resulting in technology stacks that impede agility and innovation, particularly for smaller banks with capital constraints.

Replacing legacy core banking systems is a monumental and multifaceted undertaking, often multiplying into dozens of interrelated projects due to the deep integration and hidden functionalities embedded over time. However, emerging end-to-end banking technology solutions, such as Constantinople, aim to consolidate this complexity, enabling banks to innovate and improve customer service. This modernization is also crucial for overcoming fragmented processes and data silos that currently hinder effective AI integration and real-time operational responsiveness.

By mid-2026, leading banks like Bank of America and Valley National Bank demonstrated that strategic modernization hinges on foundational infrastructure and data quality rather than flashy innovation. Bank of America’s CEO Brian Moynihan emphasized that perfecting data quality is the biggest hurdle in scaling AI, prompting multi-billion-dollar investments in data organization and cloud-native AI tools such as the Erica virtual assistant to boost operational efficiency. Similarly, Valley National Bank’s multi-year, cloud-first migration centralized over 80% of its data center capacity and created an enterprise data hub, which not only enhanced data quality but also enabled AI applications like the AML-focused ‘Tara’ agent to reduce false positives by 22%, showcasing how infrastructure overhaul directly translates into measurable financial and operational gains.

Valley National Bank’s modernization strategy extended beyond internal upgrades by cultivating an innovation ecosystem through entities like Valley Foundry and Valley Ventures, which integrate external fintech solutions to accelerate transformation. This approach underscores a broader industry trend where banks recognize that modern core banking systems must evolve from siloed, product-centric architectures into unified platforms centered on customer journeys. As Prashant Shah noted, the core is becoming a cohesive environment connecting payments, lending, fraud controls, and servicing, enabling banks to meet instant digital economy expectations while avoiding the pitfalls of patchwork technology layering that can entrench complexity.

Replacing or rebuilding core banking systems remains one of the most complex and strategic decisions banks face, involving long-term partnerships and significant operational challenges. Industry experts highlight that such replacements often multiply projects due to the core system’s deep integration with numerous functions, effectively creating a cascade of new initiatives. However, solutions like Constantinople’s end-to-end banking and technology platforms promise to consolidate these efforts, reducing complexity and enabling faster innovation and product delivery. Despite clear strategic intent, many smaller or community banks struggle with capital constraints that limit their ability to internally modernize, underscoring the need for scalable, partnership-driven modernization models.

By mid-2026, leading banks like Bank of America and Valley National Bank have moved decisively from AI pilot projects to extensive operational deployment, underscoring a strategic commitment to embedding AI deeply within core banking functions. Bank of America, having invested over $100 billion in technology over the past decade, now supports more than 300 approved AI use cases with over 100 deployed, empowering 200,000 employees with AI tools such as the assistant Erica, which automates routine tasks to free human bankers for higher-value client relationship management. Similarly, Valley National Bank’s COO Russell Barrett spearheaded a cloud-first migration and centralized data hub creation starting in 2021, enabling AI applications like the AML agent Tara to reduce false positives by 22% monthly and enhance risk management, illustrating how robust infrastructure underpins effective AI integration.

Leadership at these institutions is not only driving AI adoption but also framing it as a strategic enabler of growth, efficiency, and client service rather than a mere cost-cutting tool. Bank of America’s CEO Brian Moynihan highlights data quality as the critical challenge for scaling AI reliably, while CFO Alastair Borthwick emphasizes AI’s role in risk management and resiliency. Valley National’s CEO Ira Robbins characterizes AI as a 'connectivity solution' that enhances customer engagement without replacing human roles, reflecting a broader leadership ethos that balances technological innovation with workforce transformation—Moynihan’s candid remark to 'let attrition be your friend' signals a nuanced approach to AI-driven workforce shifts.

The formalization of AI and digital asset leadership roles marks a pivotal evolution in banking operations, with Bank of America appointing Sonali Theisen and Kevin Milsom in July 2026 to lead its global digital assets platform and AI transformation respectively. This move aligns with a wider industry trend to integrate AI and blockchain technologies into core market infrastructures, responding to rising client demand and a more favorable regulatory environment. Theisen’s collaboration with digital asset chief Adam Dixon exemplifies coordinated leadership efforts to scale and govern these emerging technologies, signaling that successful AI integration now depends as much on strategic governance as on technological capability.

By mid-2026, Moody’s issued a stark warning about the banking sector’s increasing dependence on a concentrated group of AI and cloud providers, highlighting systemic risks that could ripple through financial markets. This vendor concentration not only raises the specter of widespread operational disruptions—where an outage at a single dominant AI provider might cascade across multiple banks and sectors—but also fuels concerns over emerging vendor pricing power. As generative AI firms grapple with profitability pressures, dominant infrastructure providers could leverage their position to dictate AI service costs, potentially squeezing financial institutions further.

Despite these vulnerabilities, Moody’s underscores that banks are not entirely at the mercy of tech giants; they maintain control over critical assets like proprietary data and are proactively employing strategies to mitigate dependency. These include negotiating more favorable contract terms, integrating open-source AI models, and forging strategic partnerships to diversify their AI infrastructure footprint. Such measures reflect a nuanced approach to balancing innovation adoption with operational resilience amid an evolving AI ecosystem.

By mid-2026, regional banks have accelerated their consolidation efforts, with larger regional players acquiring mid-sized investment banks to boost scale and non-interest income streams, effectively mirroring Wall Street strategies to enhance market pricing and competitiveness. This trend reflects a broader industry shift where banks seek heft and technological readiness to compete with the largest institutions, signaling a strategic evolution in corporate lending and fee-based revenue models.

The fintech sector's maturation is catalyzing a wave of strategic mergers and acquisitions as banks and investors respond to eased regulatory constraints and the blurring boundaries between traditional banking and fintech innovation. Increased private equity activity and public market interest underscore a transition from a fragmented landscape to one characterized by consolidation, where digitization and AI integration are no longer optional but essential for survival in the next decade.

Looking ahead to 2030, Bain & Company forecasts a near doubling of US trillion-dollar banks, driven by a confluence of capital overhang, a more permissive regulatory environment, and AI-fueled M&A strategies focused on acquiring digital infrastructure and embedded finance platforms. This surge in deal activity is reshaping the wealth management sector and compelling banks to refine their acquisition criteria beyond traditional size and geography metrics to prioritize AI readiness and fintech capabilities, as demonstrated by Capital One’s $5.15 billion acquisition of AI-native platform Brex.

By early 2026, banks in the GCC are advancing beyond customer-facing digital enhancements to tackle the intricate backend technology landscape, particularly as they embrace open finance frameworks. This evolution is characterized by a strategic shift towards middleware and orchestration platforms, such as Velmie's API middleware, which serve as controlled layers between customer channels, core banking systems, and third-party providers. Pavel Shumsky, CMO at Velmie, emphasizes that this architectural approach is essential to prevent the proliferation of fragile point-to-point integrations, ensuring operational stability and scalability as banks integrate an expanding ecosystem of external services.

Within the GCC, modernization strategies reflect distinct regulatory and market nuances: Saudi Arabia initiated fintech licensing for open banking services in March 2026, signaling a focused approach on regulated fintech partnerships; the UAE has adopted a comprehensive open finance model that extends beyond banking to encompass a wider range of financial products through an API Hub and trust framework; meanwhile, Bahrain and Qatar are advancing regulatory frameworks and national strategies to foster fintech integration and digital transformation. These variations illustrate how regional banks tailor their modernization roadmaps to local regulatory environments while collectively moving towards modular, interoperable platforms.