Industry Outlook: Banking & Financial Services — Week of June 22, 2026

Market Outlook

• Core banking landscape reconfigures around specialists. Finastra’s sale of its Universal Banking core platform to Pollen Street Capital signals a sharper strategic split between core banking and high-growth adjacencies like payments and lending. At the same time, First Commerce Bank’s selection of FIS as its modern, AI-ready core underlines that even sub-$2B institutions now see core modernization as existential, not optional. Expect more PE-backed core vendors and a more fragmented, competitive supplier landscape for core transformation programs.
• Payments consolidation and expansion accelerate. Deluxe’s $625m acquisition of processor Celero and Elavon’s expansion of its all-in-one payments platform across North America reflect ongoing consolidation in merchant acquiring and integrated payments. This is about owning the full commerce stack—gateway, risk, orchestration, and settlement—across channels, with real-time and embedded finance capabilities as differentiators. Banks that lack a coherent acquiring and embedded payments strategy risk being relegated to low-margin balance sheet utility roles.
• Regulatory mood in Europe hints at banking relief. A leaked European Commission proposal suggests the EU will ease cross-border capital and liquidity frictions to boost bank competitiveness. If implemented, this could lower internal frictions for pan-EU banks, enabling more unified digital platforms and cross-border product strategies. It also creates a more favorable backdrop for cross-market open banking offerings and shared real-time liquidity infrastructure.

Discussion: CTOs should assume a more competitive, specialized vendor market for core and payments, and a potentially more permissive EU regulatory environment for cross-border platform consolidation. This is a good week to reassess vendor concentration risk and the scalability of your current cross-border architectures.

Headwinds

• AI adoption collides with job risk and governance. NatWest’s CEO publicly acknowledged that AI will replace some existing banking roles, while Deutsche Bank leadership is touting major project time reductions from AI. This combination of workforce disruption and aggressive productivity gains raises expectations from boards and regulators around explainability, workforce transition planning, and operational resilience. Without a clear AI governance and change-management framework, technology teams will face internal resistance and external scrutiny as they scale AI beyond pilots.
• Fraud and scam controls under regulatory spotlight. HSBC Australia’s A$35m fine over scam protection failures, plus industry moves by lenders like Fifth Third and Star One to bolster elder fraud defenses, show regulators are shifting from disclosure to active expectations on scam prevention. Liability is increasingly being judged on whether banks deployed modern, real-time detection and customer-intervention capabilities. Legacy fraud stacks and siloed data will be seen not just as technical debt but as regulatory and reputational liabilities.
• AI model access and geopolitical risk tighten. JPMorgan’s decision to block Anthropic’s Claude for Hong Kong staff, alongside enterprises shifting to cheaper Chinese AI models due to cost pressures, underscores a new triangle of risk: cost, compliance, and jurisdiction. Access to frontier models is becoming entangled with data residency, sanctions, and national security concerns. Fragmented AI tooling across regions will complicate standardization of development practices, monitoring, and model risk management.

Discussion: Defensive priorities this week are strengthening AI governance (including regional access policies), accelerating scam/fraud modernization, and mapping where regulatory or geopolitical constraints could abruptly change your AI or cloud provider options.

Tailwinds

• Agentic AI shows real commercial uplift. EBAday 2026 heavily emphasized the shift from passive AI tools to autonomous agents, and early adopters in other sectors like Instacart are reporting larger basket sizes from conversational, agentic AI assistants. In banking, similar patterns can drive higher product penetration, better cross-sell, and lower servicing costs through AI-led financial guidance, onboarding, and servicing. The narrative is moving from experimentation to measurable revenue and efficiency outcomes, strengthening the business case for scaled deployment.
• Community banks lean into modern AI-ready cores. First Commerce Bank’s move to an FIS core explicitly for an “AI-ready foundation” shows that smaller banks are no longer waiting for big-bank playbooks—they are designing for real-time data access and embedded analytics from the outset. This creates an opportunity for CTOs to reframe core modernization as an AI and data enablement program, not just a cost or compliance exercise. Vendors are increasingly packaging AI capabilities (e.g., predictive insights, anomaly detection) as native services around the core.
• RegTech momentum around exams and compliance. The GAO’s push for FDIC to rotate examiners to mitigate independence risks signals a broader trend toward more structured, data-driven supervisory processes. As regulators demand more standardized, auditable evidence for decisions, banks that invest in RegTech—automated reporting, model governance, and continuous compliance monitoring—will reduce exam friction and supervisory surprises. This is fertile ground for AI and workflow automation that directly lowers compliance cost-to-serve.

Discussion: To capitalize, anchor AI investments in specific revenue and regulatory outcomes, and ensure core and data modernization programs are explicitly framed as enablers of agentic AI, real-time analytics, and RegTech automation.

Tech Implications

• Core modernization must prioritize openness and data. The Finastra UB carve-out to Pollen Street and FIS’ win at First Commerce underscore that core banking is moving toward modular, API-centric, cloud-friendly platforms with clear separation from payments and lending. For CTOs, this means future core decisions should be evaluated on ecosystem fit—API richness, event streaming, data access patterns, and ability to integrate with AI and real-time payment rails—rather than monolithic feature checklists. Vendor solvency, PE ownership, and roadmap transparency now become critical architectural-risk considerations.
• Fraud and scam defense require real-time, omni-channel data. HSBC Australia’s penalty and peers’ focus on elder fraud highlight the inadequacy of batch-based, card-centric fraud engines. Effective scam detection now requires unified behavioral analytics across payments, digital channels, call centers, and even device intelligence, with real-time decisioning and friction-insertion capabilities. Architecturally, this pushes banks toward streaming platforms, graph analytics, and explainable ML models that can support regulator-friendly narratives about why interventions did or didn’t occur.
• AI platforms need regional controls and cost governance. JPMorgan’s restriction on Claude in Hong Kong and enterprises’ shift to cheaper Chinese AI models show that AI platforms must support fine-grained regional access, data residency controls, and cost observability. Banks should be designing multi-model, multi-cloud AI fabrics with policy-driven routing (e.g., which model can be used for which use case in which jurisdiction) and strong internal chargeback or cost-guardrails. This will influence choices around internal model hubs, vector databases, and observability stacks that can span both vendor and open-source models.

Discussion: Engineering teams should double down on event-driven architectures, unified data platforms for risk and fraud, and an AI platform strategy that assumes heterogeneous models, jurisdictions, and cost constraints by design.

CTO Action Items

Reassess your core and payments vendor landscape in light of Finastra’s UB divestiture and ongoing payments consolidation; update your vendor risk register and validate that your architectures can tolerate ownership or roadmap shifts. Prioritize a cross-channel scam and fraud modernization review, focusing on where batch processes or siloed data could fail under regulatory scrutiny similar to HSBC Australia’s case. Formalize an AI governance and platform strategy that includes model access controls by region, cost guardrails, and clear workforce impact planning, anticipating board and regulator questions. Finally, ensure current AI and core modernization initiatives are explicitly tied to measurable business outcomes—revenue lift, loss reduction, or compliance efficiency—so funding is resilient as macro and regulatory conditions evolve.