At a Glance
Establishing the as-is state in digital banking requires mapping journeys, channels, platforms, data, controls, costs, and ownership, quantifying performance and risk gaps to create a factual baseline that guides sequencing, investment, and accountable transformation.
Why “as-is” baselining has become a board-level discipline
As of February 2026, digital banking is no longer a convenience channel layered on top of legacy operations. It is an AI-first ecosystem in which customer experience, risk controls, and distribution economics are intertwined. That shift has made “as-is” baselining a governance necessity: leaders need a shared, evidence-based starting point before they can validate strategic ambitions or prioritize investment sequencing.
In practice, the strongest baseline language avoids ambiguous terms like “digital-first” and replaces them with observable statements: which journeys are truly end-to-end, which decisions are automated (and auditable), which real-time signals are available, and where the bank still depends on batch processing, manual reconciliation, or exception-heavy operations. This approach turns the “as-is” state from a narrative into a constraint map for strategy.
Current state market baseline: scale, adoption, and competitive pressure
Leaders commonly begin by anchoring internal baselines to external market conditions. The purpose is not to chase market size numbers, but to establish what customers and competitors now treat as default: mobile-first access, embedded distribution, and AI-mediated service expectations.
| Market signal (early 2026) | Baseline implication for banks |
|---|---|
| Digital banking usage is mainstream globally | Digital experience is the bank’s primary “perceived institution,” not an auxiliary channel; failure modes become reputational events faster. |
| Market growth is concentrated in mobile-first and embedded models | Distribution increasingly depends on APIs, identity assurance, and partner governance, not just app features. |
| Neobanks and BaaS platforms accelerate time-to-market expectations | Traditional banks face a delivery and operating model gap as much as a product gap; speed must remain auditable and resilient. |
| Regional differentiation remains material | Capabilities must be evaluated against local regulatory constraints and risk expectations rather than assuming global uniformity. |
Baseline phrasing that holds up in reviews: “Our market starting point is that customers expect mobile-first service, near-real-time resolution, and consistent handoffs across channels. Our internal baseline will show where we can meet those expectations without manual workarounds.”
Key technology pillars defining the 2026 “as-is” state
Executives increasingly describe the current state through a small number of pillars that shape both opportunity and constraint. The goal is not to list technologies, but to define how they change operating requirements and risk posture.
Agentic AI and hyper-personalization
Banking has shifted from chatbots to agents that can execute multi-step tasks across defined workflows. “As-is” language here should distinguish between recommendations and actions: what the agent is allowed to do, what is observed and logged, and where human oversight is mandatory. When leaders describe agents as customer “financial CFOs,” a credible baseline specifies which actions are bounded (for example, transfers within limits) and which remain advisory or escalated (for example, credit decisions or disputes).
Digital assets as settlement infrastructure
With regulatory regimes normalizing stablecoins and tokenized deposits in some jurisdictions, digital assets increasingly appear in payment and settlement roadmaps. A defensible “as-is” baseline clarifies whether the bank has operational controls for custody, transaction monitoring, wallet identity, reconciliation, and incident response, or whether digital assets remain an external dependency handled through partners.
Open banking and embedded distribution via APIs
Banking services are increasingly embedded into ERPs, e-commerce, and non-financial platforms. Baselining needs to focus on API reliability, consent controls, partner onboarding, third-party concentration risk, and the bank’s ability to evidence obligations across distributed journeys.
Advanced biometrics and digital identity
As biometrics become standard in digital access, leaders baseline not just adoption rates but identity assurance: coverage across channels, exception handling, fraud telemetry, and privacy and residency controls. The core question is whether identity is a consistent control system across journeys or a set of siloed authentication implementations.
Leading platforms and players: how leaders baseline against the market
Competitive baselines are typically organized by capability categories rather than vendor lists. This helps leaders avoid the false comfort of “we have the same vendor” and instead compare the outcomes those platforms can deliver under the bank’s constraints.
| Category | Representative platforms and providers | What to baseline internally |
|---|---|---|
| Traditional core software | Temenos, Oracle FLEXCUBE, Finacle (Infosys), FIS, Finastra | Real-time readiness, change throughput, resilience, and how well the core can be wrapped and observed. |
| Cloud-native and composable layers | Mambu, Backbase, SDK.finance, nCino, 10x Banking | Journey orchestration, API reliability, data product availability, and control evidence at speed. |
| Major neobanks | Revolut, Chime, N26, Monzo | Customer expectations for onboarding speed, dispute handling, and engagement; delivery pace and operating leverage assumptions. |
| BaaS providers | Marqeta, Solarisbank, Galileo, Treezor, Intergiro | Third-party governance, shared responsibility boundaries, incident reporting, and concentration risk controls. |
Regulatory and risk landscape: what “as-is” must make explicit
The 2026 regulatory state is marked by localization and divergence across major jurisdictions. For baselining, this means a bank cannot claim “global readiness” without mapping where controls, evidence pathways, and operational resilience differ by region and product set.
AI governance
Boards increasingly treat explainability and auditability as standing agenda items. “As-is” language should capture where models and agents are used, what decisions they influence, and how the bank can prove oversight: decision logs, monitoring for drift and degradation, human-in-the-loop triggers, and escalation ownership.
Cybersecurity and fraud economics
AI-powered deepfakes and sophisticated phishing have changed the fraud frontier. A credible baseline describes time-to-detect and time-to-contain performance, identity telemetry coverage, and how fraud controls operate across embedded and partner journeys where the bank may not control the front end.
Baseline phrasing leaders use: “Our control posture is defined by how quickly we can detect and contain threats across critical customer journeys, including those delivered through partners.”
Market projection snapshot: how leaders use it without overfitting to it
Executives often include simple market projection visuals in “as-is” packs to align stakeholders on urgency. The risk is treating projections as strategy validation. Used responsibly, the projection simply reinforces the direction of travel: digital banking scale and embedded distribution are expanding, and the bank must baseline whether its operating model can keep up.
Global digital banking market projection (illustrative)
Values shown in trillion USD as presented in the input. The intent is alignment on urgency, not precision forecasting.
Baseline language leaders use to make the “as-is” state actionable
Strong baseline language has three characteristics: it is observable, it is tied to constraints, and it avoids conflating ambition with capability. The following examples illustrate the style that holds up in steering committees and supervisory discussions.
- From “We are AI-first” to “We use models and agents in these journeys, with auditable decision logs and defined human escalation thresholds.”
- From “We are real-time” to “These services operate event-driven with defined latency SLOs; these domains remain batch-bound and require reconciliation windows.”
- From “We have open banking” to “We expose these APIs with reliability targets, consent controls, and partner governance; these dependencies represent concentration risk.”
- From “We are secure” to “We can detect and contain incidents within defined thresholds for material services; we have tested recovery playbooks and can evidence actions taken.”
This language style keeps the baseline aligned to strategy validation: it clarifies what is feasible now and what requires prerequisite capability building.
Creating an objective baseline to validate digital ambitions
Baselining becomes materially more valuable when it can be compared across domains that do not naturally share language: customer journeys, data platforms, AI governance, third-party risk, and resilience engineering. A structured assessment provides that comparability by translating evidence into consistent capability statements that leadership teams can use to test whether ambitions are realistic under current constraints.
Used as a strategy validation instrument, DUNNIXER connects the “as-is” fact base to the practical trade-offs leaders face in early 2026: speed versus auditability, embedded distribution versus third-party concentration, autonomy versus controllable blast radius, and real-time expectations versus operational resilience. Executives use those comparisons to improve readiness judgments and sequencing through the DUNNIXER Digital Maturity Assessment.
Reviewed by
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.
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