The New Arc of Corporate Banking Treasury Services: Real-time, Embedded and AI Infused
The transformation of corporate cash management has been underway for over a decade. Banks have digitized channels, accelerated payments, exposed APIs, improved intraday visibility, and automated elements of liquidity management. These advances were meaningful against the goalposts that banks were chasing. They moved treasury services beyond batch processing and closer to the digital economy. However, few banks significantly elevated or reimagined the underlying operating models. In 2026 and beyond, banks now face shifting goalposts across three dimensions.
Firstly, retail banking behaviors have quietly reset how money is expected to move. Instant balances, real-time payments, proactive alerts, and embedded financial actions have become the norm. And as enterprises digitized customer journeys, supply chains, and revenue models, the dynamics propagated directly into business’ treasury operations. Thus, banks’ treasury business lines did not simply face higher expectations, it is now compelled to evolve because their business clients began to operate on real-time financial dynamics.
Secondly, the pressures have intensified as enterprises adopt continuous operating models. Revenue is increasingly platform- and marketplace-driven. Financial actions execute inside business workflows rather than in isolated banking systems. Consequently, the gap between how businesses operate and how cash is governed can no longer be closed through existing archetypes. Treasury operations must match the speed, continuity, and intelligence of the enterprise. Finally, AI as a technology is expected to disrupt all functions in banking and treasury services is no exception.
For banks, these Next-Gen goalposts collectively demand structural shifts - enabling it as a real-time, embedded, and AI-augmented operating layer.
Authors:
Justin Karl Silsbury, Sudhindra Kadur Keshava Murthy, Rajalakshmi Sridharan
Corporate cash management thus must be re-architected simultaneously across three dimensions:
- How cash is monitored, governed, and moved by corporates (real-time)
- Where cash services are offered and consumed (embedded)
- How decisions across cash, liquidity, and risk are made (increasingly AI-powered, event-driven, and explainable)
These are not sequential shifts. They are mutually reinforcing - and together they define the next phase of corporate cash management evolution.
Retail-Grade Immediacy Drives the Real-Time Treasury Operating Model
The influence of retail financial behavior on enterprise treasury has been foreseeable and gradual. Early adoption of instant payments, intraday reporting, and automated sweeps clearly signaled the direction of travel. What began as selective enablement is now becoming an enterprise-wide expectation - retail normalized immediacy, and treasurers were inevitably drawn into the same real-time operating rhythm. The next step for banks is no longer innovation, but industrialization at scale. There are seven priorities for banks to succeed in realizing the real-time treasury operating model that are listed below.
Delivering these requires banks to move from enabling capabilities to running a real-time cash management archetype built on cloud-native cores, API-first integration, event-driven processing, and agentic AI. From here on, real-time is no longer aspirational. It starts to become the minimum viable proposition.
Priorities for Banks
Case in Point: RBC Clear by RBC Capital Markets, US
Royal Bank of Canada envisioned its Next-gen cash management platform offering seamless onboarding, transparent payments, real-time visibility, and a unified data store, boosting RBC’s US expansion strategy.
Embedded Cash Management Deepening More Decisively into Enterprise Workflows
Embedded cash management is moving from the periphery of corporate banking to its center of gravity. What was once viewed as one of the distribution channels is now becoming one of the most important primary ways in which enterprises consume treasury services. Embedded cash management is no longer about API access or integration depth, it defines how banks participate in enterprise value chains. In 2026 and beyond, more banks will adopt decisive transformations efforts into making this a reality.
The scale of the opportunity makes this shift unavoidable. Global financial transaction volumes are projected to grow from $5.9 trillion in 2023 to $20.8 trillion by 2030, with B2B transactions accounting for $13 trillion. This growth is being driven by the digitization of enterprise workflows, the rise of platform-led business models, and the rapid adoption of industry-specific software and marketplaces. Cash no longer moves through bank portals, it moves through business platforms, and eco-systems in larger contexts.
What Changes for Embedded Cash Management in 2026
From 2026, the dynamic of embedded banking will shift materially. Embedded execution becomes the default operating model, not an exception. Payments, liquidity positioning, FX, and controls increasingly execute natively within ERP systems, TMS platforms, procurement tools, and vertical SaaS, exactly where commercial decisions originate. Treasurers expect banking to be present contextually, as hygiene, at the moment of action.
At the same time, distribution power decisively migrates to platforms as Fortune 500 firms scale platform-centric operating models. Banks that fail to embed risk being abstracted into invisible balance-sheet utilities.
Critically, embedded cash management evolves into a revenue engine. Embedded banking revenues are projected to reach $45 billion by 2030, with platform-led services exceeding $74 billion by 2034, up from $20 billion in 2024. Monetisation shifts beyond transactions toward subscriptions, usage-based pricing, revenue sharing, white-labelling, and data-driven services. In effect, 2026 marks the pivot from embedded banking as an option to embedded cash management as the dominant operating and distribution model.
Case in point: Neo for Corporates (NFC) is a next-generation digital banking platform by Axis Bank. NFC provides a robust API suite, enabling businesses to integrate with bank services independently and create innovative business models.
Operationalizing AI in Cash Management: Progress Expected in 2026 and beyond
Treasurers are expected to manage liquidity with precision, forecast cash flows accurately, and respond to disruptions in near real time. Yet most remain constrained by fragmented banking relationships, legacy platforms, and manual processes. Forecast variances exceeding 20% and liquidity buffers of 15–20% are not edge cases - they reflect structural limitations. While AI is often positioned as an immediate fix, the reality is more gradual. Meaningful impact will play out through 2026 and beyond, with progressive banks moving faster than the rest.
AI will first be applied where outcomes are measurable and explainable. Cash flow forecasting will evolve from static models to adaptive, event-driven projections that ingest ERP data, intraday balances, and external signals. Leading banks will focus less on prediction alone and more on variance explanation, helping treasurers understand why forecasts is likely to shift, and what actions to take.
Reconciliation will be another beneficiary. Banks with modern data foundations will deploy self-learning AI powered engines to automate matching at scale, particularly across real-time payments and virtual accounts. Others will continue to rely on rule-based automation, limiting efficiency gains.
In payments and liquidity management, AI adoption will be uneven. A few banks will introduce agentic capabilities covering dynamic payment routing, scenario-based liquidity recommendations, and policy-driven automation, while most will restrict AI to advisory roles rather than execution.
Finally, fraud and risk controls will move upstream for advanced institutions, embedding behavioural and contextual intelligence directly into cash workflows, while laggards remain dependent on post-transaction alerts.
Overall, AI will not transform cash management overnight. Differentiation will emerge from execution discipline, with leaders embedding AI into decision flows and others progressing incrementally as data, governance, and risk comfort mature.
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The Strategic Takeaway
Cash management services by corporate banks is no longer a static, back-office centric function. It is evolving, gradually but decisively, into a real-time operating model, an embedded layer within enterprise workflows, and an increasingly intelligent liquidity architecture. This transition will not occur uniformly or overnight; it will unfold through 2026 and beyond, shaped by data readiness, governance maturity, and risk appetite.
Banks that pursue these shifts in isolation, or treat AI as a standalone overlay, will see limited impact. Those that align real-time execution, embedded distribution, and AI-powered decisioning into a coherent operating model will pull ahead. Over time, they will emerge as orchestrators of enterprise liquidity, powering platforms, enabling scalable global growth, and defining the next generation of corporate cash management.
References
- https://www.deloitte.com/us/en/Industries/financial-services/articles/transforming-commercial-embedded-banking.html
- https://www.gartner.com/en/documents/5935907
- https://www.deloitte.com/us/en/Industries/financial-services/articles/transforming-commercial-embedded-banking.html
- https://www.eba.europa.eu/single-rule-book-qa/qna/view/publicId/2020_5264
- https://www.marketreportsworld.com/market-reports/virtual-account-system-market-14721562
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