Instant Payments Expose Operating Model Gaps Banks Can No Longer Ignore

Why instant payments has become an operating model issue

By 2026, the shift to instant payments has moved beyond a technology integration milestone. Many banks have achieved baseline connectivity to real-time rails, yet still struggle to operate those rails reliably at enterprise scale. The gap is not simply throughput or latency; it is the institution’s ability to manage a permanently “open” payments environment in which control decisions, customer outcomes, and liquidity consequences occur in seconds rather than hours.

Industry commentary on payments modernization increasingly reflects this change in emphasis: real-time payments creates new expectations for continuous availability, faster decisioning, and end-to-end visibility across the payment lifecycle. The practical implication for executives is that an “instant payments program” is inseparable from operating model design—roles, decision rights, monitoring, exception handling, and resilience measures that can withstand both technical faults and adversarial behavior at all hours.

Connectivity is not readiness

Rail connectivity can be achieved through a range of approaches, including overlays, translation layers, and incremental integration patterns. These approaches can be rational near-term choices, but they can also mask structural gaps that only appear under stress: fraud spikes, data quality failures, liquidity pressures, or service disruptions. Several 2026-focused perspectives highlight how quickly payments performance becomes a competitive and risk management differentiator, particularly as fraud pressure increases and regulators intensify expectations for robust controls and governance across faster payment ecosystems.

Executives should treat “readiness” as a capability statement rather than a project status. The question is whether the bank can sustain straight-through processing while performing real-time risk checks, support customers and investigations on a 24/7 basis, manage liquidity continuously, and process enriched payment data natively enough to reduce rework and operational friction.

The capability gap framework for real-time rails

Instant payments amplifies weaknesses that were tolerable in batch environments. The gaps that matter most in 2026 tend to cluster into five interdependent capability domains: real-time financial crime and compliance controls, 24/7 operations and customer support, ISO 20022 data integrity and native processing, continuous liquidity management, and verification controls such as Verification of Payee in markets where it is mandated.

Real-time fraud and compliance must run inside the payment flow

Batch-era screening models were designed for time buffers: overnight runs, queued investigations, and post-event recovery. Instant rails compress those buffers and raise the cost of late detection. 2026 payments and fraud outlooks consistently emphasize that fraud pressure is rising on instant payment rails and that the control posture must evolve from after-the-fact detection to in-flow decisioning that learns continuously from new attack patterns.

The operating model gap is usually not a lack of tools; it is fragmentation. Fraud teams, AML teams, and sanctions teams often operate on separate data sets, metrics, and escalation paths. A unified financial crime framework—shared analytics backbone, consistent decision logging, and aligned thresholds—becomes essential to preserve straight-through processing while meeting control expectations. Where AI and advanced analytics are introduced, governance discipline matters as much as model performance: explainability, accountability, and model risk controls must be compatible with real-time decision latency and audit requirements.

24/7 operations is not an extension of business hours

Many banks still run payments support as a back-office capability with limited operating windows. Real-time rails turns that posture into an enterprise risk. Exceptions, customer inquiries, and potential fraud events occur continuously; investigations and reversals are constrained by time and by scheme rules; and customer expectations are shaped by consumer platforms that resolve issues immediately.

The gap typically shows up in three places: (1) insufficient always-on operational coverage for exception handling and investigations, (2) unclear ownership between technology operations, payments operations, and financial crime teams, and (3) weak “customer-to-ops” feedback loops that translate call center signals into operational action. Closing the gap requires explicit service operating models—rotas, escalation tiers, handoffs, and performance measures—that are designed for permanence rather than for temporary program support.

ISO 20022 readiness is measured by native processing, not translation

Across modernization priorities, ISO 20022 is repeatedly framed as more than a messaging format change. It is a data and operating model change because richer, structured data enables better automation, improved screening, and more reliable reconciliation—if the institution can actually process it natively. When banks rely heavily on translation wrappers or partial field mapping, the result is often an expanding layer of operational workarounds: repairs, manual enrichment, and inconsistent downstream interpretations.

The executive risk is that “technical compliance” can look sufficient until volumes scale, cross-border interactions increase, or scheme and market requirements tighten. At that point, rejections, investigation workload, and customer friction rise sharply, and the bank pays twice—first for integration and then again for operational remediation. Modernization perspectives for 2026 repeatedly highlight that investments “beyond migration” are required if banks want to unlock value and reduce operational drag from ISO 20022-driven change.

Liquidity management must become continuous and automated

Instant settlement changes liquidity dynamics by reducing predictability and removing the comfortable cadence of end-of-day netting and batch cutoffs. The core gap is not that treasury teams are unaware of the issue; it is that many operating models and systems still assume periodic updates and delayed feedback. In a 24/7 environment, liquidity forecasting, intraday monitoring, and automated decisioning around funding movements must keep pace with transaction flow and operational incidents.

Payments modernization discussions increasingly connect liquidity readiness to broader infrastructure choices: API-first architectures, modern transaction engines, and improved data timeliness. Without those enablers, banks tend to compensate with manual buffers and conservative limits that protect safety but constrain growth and customer experience.

Verification of Payee exposes data and customer journey weaknesses

Verification controls such as Verification of Payee (VoP) are often treated as a compliance checkbox. In practice, they are a forcing function for the bank’s data quality, real-time lookup capability, and customer experience integration. Where regulations or scheme requirements mandate verification steps, banks need real-time access to authoritative customer name data, consistent matching logic, and user experiences that can resolve mismatches without increasing abandonment or call volumes.

The operating model gap is frequently visible in ownership and accountability: which function owns matching policies, customer communications, and dispute handling? Without clear decision rights and feedback loops, VoP can increase friction and operational workload while still failing to materially reduce misdirected payments risk.

Architecture and platform choices determine whether gaps persist

Many of the 2026 priorities cited across industry viewpoints—cloud-native design, API-first integration, and modernization of core components—matter in this context because they affect the bank’s ability to deliver real-time decisioning and continuous operations without fragile coupling. When the estate relies on brittle integrations, delayed data propagation, and tightly coupled batch processes, banks tend to “solve” instant payments by surrounding it with compensating controls: manual monitoring, conservative limits, and expanded operational teams.

Those compensations are not cost-neutral. They shift risk and cost into operations, increase the probability of customer-impacting incidents, and complicate governance because responsibility becomes diffuse across workarounds. By contrast, modernization approaches that emphasize resilient foundations, well-governed APIs, and consistent data models make it easier to embed controls into flow, automate liquidity signals, and reduce exception rates. Perspectives focused on core banking features and payments modernization priorities reinforce that robust APIs and modern architectures are increasingly prerequisites for scaling new payment capabilities while maintaining control effectiveness.

AI-driven orchestration is promising, but it heightens governance expectations

Several 2026 predictions and fraud-focused articles point toward AI-driven orchestration: moving from reactive alerting to more autonomous detection and response patterns. In the instant payments context, the operational appeal is straightforward—real-time systems generate high-velocity signals that are difficult to triage manually, especially across multiple channels and products.

However, the executive constraint is equally straightforward. As AI becomes more central to control decisions, banks must reconcile speed with accountability: model risk governance, data lineage, and decision explainability cannot be deferred. If governance cannot keep pace, the bank may be forced into conservative operating modes—more manual review, more false positives, and slower customer resolutions—that undermine the strategic intent of instant payments modernization.

How to identify capability gaps without turning the program into a diagnostic exercise

Instant payments readiness assessments often fail because they ask the wrong question. Instead of “are we connected,” executives should test whether the bank can operate the service as a continuously available, control-intensive, customer-visible capability. A practical gap identification approach evaluates the end-to-end service across operating model dimensions, using evidence that can be challenged in governance forums.

Control-in-flow proof: demonstrate where fraud, AML, and sanctions checks occur, what decisions are automated versus escalated, and how the design preserves straight-through processing under peak loads.
24/7 service ownership: document who owns incidents, customer complaints, exceptions, investigations, and scheme communications at all hours, including escalation thresholds and handoffs.
ISO 20022 data integrity: test whether enriched fields are processed natively end-to-end, how data quality issues are detected, and how downstream systems consume and reconcile structured information.
Liquidity automation: validate continuous monitoring and funding decisioning behaviors, including scenario responses for sudden volume spikes or operational outages.
Verification experience and policy: confirm matching logic ownership, customer journey integration, and operational handling of mismatches and disputes.

This evidence-based posture reduces the tendency to treat modernization as a collection of parallel technical workstreams. It makes the operating model explicit and keeps strategic ambition grounded in the bank’s ability to sustain safe and reliable outcomes in production.

Strategy validation and prioritization through payments capability gap identification

Identifying capability gaps in real-time rails is a strategy validation exercise because it reveals which ambitions are constrained by operating reality. The most consequential gaps are rarely isolated. Real-time fraud and compliance depends on data quality and decision latency; 24/7 operations depends on clear ownership and automation; ISO 20022 value depends on native processing and downstream readiness; liquidity resilience depends on timely signals and platform integration; and verification controls depend on accurate customer data and well-designed customer journeys.

A structured maturity lens helps executives translate these interdependencies into sequenced priorities and risk-informed commitments. By evaluating capabilities across architecture and integration foundations, data management, operational processes, control effectiveness, and governance readiness, leaders can distinguish between gaps that are acceptable in early adoption and gaps that will undermine safety, customer trust, and scalability as volumes grow. This is where the DUNNIXER Digital Maturity Assessment fits naturally into executive decision-making: it provides a disciplined way to benchmark current capabilities, surface blind spots that connectivity milestones can hide, and test whether the bank’s instant payments ambitions are realistic under 24/7 operational demands and evolving compliance expectations.

Reviewed by

Ahmed Abbas

The Founder & CEO of DUNNIXER and a former IBM Executive Architect with 26+ years in IT strategy and solution architecture. He has led architecture teams across the Middle East & Africa and globally, and also served as a Strategy Director (contract) at EY-Parthenon. Ahmed is an inventor with multiple US patents and an IBM-published author, and he works with CIOs, CDOs, CTOs, and Heads of Digital to replace conflicting transformation narratives with an evidence-based digital maturity baseline, peer benchmark, and prioritized 12–18 month roadmap—delivered consulting-led and platform-powered for repeatability and speed to decision, including an executive/board-ready readout. He writes about digital maturity, benchmarking, application portfolio rationalization, and how leaders prioritize digital and AI investments.