Yapily and Cortena Enhance AI Finance Operations for SMEs

Table of Contents

• 1. Yapily and Cortena streamline finance for SMEs
• 2. Strategic Partnership Between Yapily and Cortena
• 3. Automation and Real-Time Connectivity for SMEs
• 4. Cortena’s AI Capabilities in Financial Operations
• 5. Transforming Finance Management for Small Enterprises
• 6. Bruno Pellicci’s Insights on the Collaboration
• 7. Benefits of AI-Driven Reconciliation Processes
• 8. The Future of SME Finance: Embracing AI and Open Banking
• 8.1 Transforming Financial Operations
• 8.2 The Path Forward for SMEs

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Yapily and Cortena streamline finance for SMEs

• Yapily and Cortena have formed a strategic partnership to bring AI-driven finance operations to SMEs across Europe.
• The integration combines Yapily’s Open Banking API connectivity with Cortena’s AI “execution layer” for operational finance.
• Cortena’s AI agents can access real-time bank data, reconcile transactions against invoices, flag mismatches, and trigger next workflow steps.
• The goal is to reduce manual reconciliation and move SMEs from retrospective accounting to proactive, “self-driving” finance.

Streamlined Cash and Reconciliation Workflow
If you run finance for an SME, this partnership is essentially aiming to replace three recurring “busywork loops”:
– Logging into multiple bank portals to piece together today’s cash position
– Exporting/importing statements (or waiting for bank feeds) before you can reconcile
– Chasing exceptions (missing references, partial payments, duplicates) after the fact
The promise is that live bank connectivity (via Yapily) plus an execution layer (via Cortena) turns those loops into a continuous workflow where the system does the first pass and humans focus on exceptions and decisions.

Strategic Partnership Between Yapily and Cortena

Yapily, described as Europe’s leading Open Banking infrastructure platform, has announced a strategic partnership with Cortena, a fintech building what it calls an AI execution layer for SME finance. The details below reflect what the companies shared in the partnership announcement and related coverage. The pitch is straightforward: connect bank data directly into operational workflows, then let AI agents do the repetitive work that typically consumes small finance teams.

For years, many SMEs have managed cash and reconciliation through a patchwork of tools and logins—multiple bank portals, exports, and manual data entry—just to answer basic questions like “What’s our cash position right now?” Traditional accounting platforms can help, but the partnership’s premise is that they often stop at static, balance-level information and still leave businesses with painstaking reconciliation work.

The Yapily–Cortena integration is positioned as a step beyond “bank feeds” and periodic bookkeeping. By integrating Yapily’s Open Banking API, Cortena’s AI agents can securely connect directly to bank accounts across Europe. That connectivity is the foundation for what both companies frame as the next phase of fintech: not only reading financial data, but executing actions against it inside finance operations.

In practical terms, the partnership aims to bridge a gap that has long existed in SME finance: bank data is available, but it’s not operationalized. By combining Open Banking connectivity with AI-driven workflow automation, the two firms are targeting the administrative burden that comes from fragmented systems—especially for lean teams that cannot afford to spend hours matching payments, chasing exceptions, and updating records by hand.

End-to-End Partnership Workflow
A simple way to think about how the partnership works end-to-end:
1) Connect: An SME connects its bank accounts through Yapily’s Open Banking API connectivity.
2) Ingest: Live balances and transactions flow into Cortena.
3) Interpret: Cortena’s AI agents apply user-defined rules/logic to classify, match, and detect exceptions.
4) Act: The agents reconcile, flag mismatches, and trigger the next workflow step (e.g., notify, escalate, or proceed).
Checkpoints finance teams typically care about during rollout:
– Coverage: Are all required banks/accounts supported for the SME’s footprint?
– Latency: How “real-time” is real-time for the specific banks in use?
– Exception paths: What happens when references are missing, payments are split, or invoices don’t match?
– Controls: Which actions are fully automated vs. require approval?

Automation and Real-Time Connectivity for SMEs

The core promise of the collaboration is “unprecedented automation and real-time bank connectivity” for SMEs. That matters because the day-to-day reality of SME finance is often less about high-level strategy and more about operational survival: tracking inflows and outflows, ensuring invoices are paid, and keeping records accurate enough to make decisions.

Open Banking connectivity, delivered through Yapily’s API-based infrastructure, is central here. Yapily provides secure connectivity to banks across the UK and Europe, enabling access to account and transaction data. It also supports payment initiation use cases, which is important because automation becomes more powerful when systems can both observe and act.

The partnership’s emphasis on real-time is also a critique of the status quo. When finance teams rely on manual processes—logging into portals, exporting statements, copying data into spreadsheets or accounting tools—cash visibility becomes delayed and fragmented. Even when accounting platforms ingest data, the information can remain “static” until reconciliation is completed, leaving teams with an incomplete picture.

With direct connectivity across multiple banks, SMEs can consolidate account data without hopping between portals. Cortena’s AI agents can then use that live data to drive workflows continuously rather than periodically. The result, in theory, is a shift from finance as a monthly close exercise to finance as an always-on operational system.

This is particularly relevant for SMEs because they typically operate with limited resources. Lean teams are more exposed to the cost of manual work and the risk of errors. Automating routine tasks—while keeping humans in control of rules and exceptions—can free time for analysis, planning, and growth-focused decisions rather than administrative maintenance.

Cortena’s AI Capabilities in Financial Operations

Cortena positions itself as a finance operating system where AI agents autonomously execute operational finance tasks, following user-defined rules and logic. In the Yapily partnership announcement, the focus is on what those agents can do once they have secure access to bank accounts.

According to the companies, Cortena’s AI agents—powered by Yapily connectivity—are designed to:

• Access real-time account data instantly across multiple European banks
• Autonomously reconcile transactions against invoices and complex payment workflows
• Identify inconsistencies or unmatched payments with “machine-level precision”
• Automatically trigger the next step in the finance workflow without human intervention

This is a meaningful distinction from automation that simply moves data from one system to another. The “execution layer” framing suggests an operational brain sitting on top of bank connectivity and finance tooling—one that can interpret events (a payment arrives, a transaction posts, an invoice is due) and then take action (match, flag, escalate, or proceed to the next step).

Cortena’s broader messaging also targets “pre-accounting” and reconciliation as the most broken parts of finance operations for SMEs. That’s where the time goes: categorizing, matching, checking, and correcting. If AI agents can reliably handle the bulk of that work, finance teams can spend less time on repetitive tasks and more time on oversight and decision-making.

The partnership also underscores a broader trend: as Open Banking matures, the differentiator shifts from access to data toward what you can do with it. Cortena’s AI agents are presented as the mechanism that turns connectivity into continuous execution—moving finance operations from a record-keeping function to a workflow engine.

AI Execution Flow in Finance Ops
A practical way to map “AI execution” in finance ops (and where humans typically stay involved):
– Detect: Watch live bank events (new transactions, balance changes) and pull relevant context (counterparty, reference, amount, timing).
– Match: Link transactions to invoices/expected payments using rules (amount tolerances, references, customer/supplier mapping, split/partial logic).
– Flag: Route exceptions (missing references, duplicates, unexpected fees, partials) to a queue with a clear reason code.
– Trigger: Take the next step (update status, notify, escalate, or proceed) based on the rule outcome.
Human checkpoints usually sit at rule definition, exception review, and any high-impact actions that require approval.

Transforming Finance Management for Small Enterprises

The collaboration is framed as a shift from retrospective accounting to proactive, “self-driving” finance. That phrase is doing a lot of work, but the underlying idea is concrete: if bank data is real-time and workflows are automated, finance becomes less about reconstructing what happened and more about managing what is happening.

In the traditional SME setup, finance teams often discover issues late—unmatched payments, missing invoice references, timing gaps between outgoing payments and recorded expenses—because reconciliation is manual and periodic. That delay can affect cash planning and operational decisions. By contrast, a system that continuously reconciles and flags inconsistencies can surface issues earlier, when they are easier to resolve.

The partnership also speaks to the reality that SMEs frequently maintain multiple bank accounts across institutions. Fragmentation increases complexity: more portals, more statements, more formats, more opportunities for human error. Open Banking APIs can unify access, but the operational benefit depends on whether the business can translate that access into action. Cortena’s AI agents are positioned as the layer that performs that translation.

There’s also a strategic implication for SMEs: automation can change what “good finance management” looks like. Instead of spending scarce time on administrative tasks, teams can focus on forecasting, scenario planning, and growth initiatives—assuming the automation is reliable and exceptions are handled appropriately.

Finally, the partnership is explicitly European in scope, with Yapily’s connectivity enabling Cortena to connect to bank accounts across Europe. For SMEs operating across borders—or simply banking with multiple institutions—this breadth matters. It suggests a path toward standardized, API-driven finance operations that are not tied to a single bank portal or a single accounting workflow.

Quantifying SME Automation Urgency
A few concrete, publicly stated data points that help quantify the “why now” for SME finance automation:
– €40,000+ per year: Cortena states that SMEs can lose over €40,000 annually due to inefficiencies in pre-accounting and reconciliation (company-stated figure).
– ~100,000 UK SME finance applications rejected annually: Yapily cites nearly 100,000 rejected SME finance applications per year in the UK, contributing to a £22 billion funding gap (company-cited estimate).
– 24 hours per application + ~80% rejection rate: Yapily also cites an average of 24 hours spent researching/submitting credit applications, with an 80% rejection rate (company-cited estimate).
These figures are directional (and in some cases company-reported), but they illustrate the scale of time and cost pressure that makes real-time data + automation attractive.

Bruno Pellicci’s Insights on the Collaboration

Bruno Pellicci, chief executive officer and co-founder of Cortena, has emphasized that the partnership was not only about technology, but also about alignment and execution. In his account, the collaboration began with early conversations that made it clear Yapily was the right fit.

“From our first conversation with Ioana, it was clear Yapily was the right partner. Not just the right technology.”
Bruno Pellicci, CEO and co-founder, Cortena

Pellicci highlighted that Yapily’s team understood what Cortena was building and contributed ideas, while also taking a phased approach that matched Cortena’s “early-stage reality.” That detail matters because integrations can fail not on capability but on mismatch: timelines, complexity, and the operational burden placed on a smaller company.

He also pointed to the simplicity of the integration as a surprise—an important signal in a space where connecting regulated financial infrastructure to new automation layers can be slow and resource-intensive.

“The team genuinely understood what we were building, came to the table with ideas, and took a phased approach aligned with our early-stage reality. The simplicity of the integration surprised us.”
Bruno Pellicci, CEO and co-founder, Cortena

Most notably, Pellicci described the practical outcome: Cortena’s agents can “reach directly into” client bank accounts to reconcile transactions, match payments, and trigger the next action automatically. That statement captures the partnership’s central claim: AI agents are not operating on stale exports or delayed feeds; they are operating on live bank connectivity.

“Cortena’s agents now reach directly into our clients’ bank accounts, reconciling transactions, matching payments, and automatically triggering the next action. The execution layer just got wider.”
Bruno Pellicci, CEO and co-founder, Cortena

The “execution layer” language also hints at ambition beyond reconciliation. If the layer “gets wider,” it can potentially cover more workflows over time—provided the underlying connectivity and controls remain robust.

From Matching to Action
What Pellicci’s comments imply for an SME buyer (beyond the announcement language):
– Phased rollout matters: start with a narrow workflow (e.g., reconciliation for one entity/bank) before expanding.
– Integration simplicity is a real differentiator: if setup is heavy, SMEs often never reach the “automation dividend.”
– “Trigger the next action” is the step-change: matching is useful; automated follow-through is where operational time is actually reclaimed.

Benefits of AI-Driven Reconciliation Processes

Reconciliation is one of the most time-consuming and error-prone parts of SME finance operations. The Yapily–Cortena partnership targets that pain directly by combining real-time bank data with AI agents that can match transactions to invoices and workflows automatically.

The immediate benefit is reduced manual overhead. Instead of finance staff spending hours comparing bank statements to invoices, the AI agents can reconcile continuously, flagging only the exceptions that require human judgment. That shift is especially valuable for SMEs with lean teams, where every hour spent on admin is an hour not spent on planning or growth.

Another benefit is improved cash visibility. When reconciliation is delayed, cash reporting can be misleading: balances may be accurate, but the underlying picture—what has been paid, what is outstanding, what is unmatched—can remain unclear. With real-time access to account data across multiple banks, combined with automated matching, SMEs can maintain a more current view of their cash position.

The partnership also emphasizes precision in identifying inconsistencies or unmatched payments. While “machine-level precision” is a qualitative claim, the operational point is that automation can apply consistent rules at scale, reducing the variability that comes with manual processes and fatigue.

Finally, AI-driven reconciliation supports a broader move toward proactive finance operations. If the system can not only detect mismatches but also trigger the next workflow step—such as notifications, escalations, or other operational actions—finance becomes less reactive. The goal is to eliminate manual reconciliation as a bottleneck and replace it with continuous execution, where humans focus on oversight, exceptions, and strategic decisions.

Reconciliation Automation Time Savers
Where reconciliation automation typically saves the most time (and what to verify):
– ☐ Auto-matching: payments ↔ invoices (including tolerances for fees/FX)
– ☐ Partial and split payments: correct handling without creating duplicate “unmatched” noise
– ☐ Exception queue: clear reason codes (missing reference, duplicate, unexpected counterparty)
– ☐ Escalations/notifications: the right people get pinged when an exception blocks a workflow
– ☐ Audit trail: who/what matched it, what rule fired, and what changed afterward
– ☐ Month-end readiness: fewer surprises because issues were surfaced continuously

The Future of SME Finance: Embracing AI and Open Banking

Transforming Financial Operations

The Yapily–Cortena partnership illustrates a broader direction in fintech: Open Banking provides the rails for secure, standardized connectivity, while AI provides the operational layer that can turn connectivity into action. Together, they point toward finance operations that run continuously rather than in periodic cycles.

For SMEs, the promise is not just convenience. It is a structural change in how finance work is done—less time spent gathering data and reconciling it, more time spent using accurate, up-to-date information to run the business. By bridging Open Banking and AI, the integration aims to move finance from record-keeping to execution.

This also reflects a shift in what platforms compete on. As access to bank data becomes more common, differentiation moves to workflow depth: how well a system can reconcile, detect exceptions, and trigger next steps across real operational complexity.

The Path Forward for SMEs

Adoption will ultimately depend on trust, usability, and how well automation handles real-world edge cases. But the direction is clear: SMEs are being offered tools that previously required larger teams and bespoke integrations.

In the near term, the most tangible gains are likely to come from automating reconciliation and improving cash visibility across multiple accounts. Over time, the “execution layer” concept suggests a broader expansion of AI-driven workflows—built on top of Open Banking connectivity—where finance teams define rules and AI agents carry out the routine work.

For SMEs that have long struggled with fragmented systems and manual processes, the combination of real-time connectivity and AI automation is being positioned not as an upgrade, but as the new baseline for modern finance operations.

From Announcement to Operations
What to watch next as AI + Open Banking moves from announcement to day-to-day operations:
– Adoption drivers: which workflows get automated first (reconciliation, cash visibility, collections, payables).
– Edge cases: partial payments, chargebacks/refunds, FX/fees, and missing references—do they improve or create new exception load?
– Controls & approvals: where SMEs keep humans in the loop (thresholds, new counterparties, unusual patterns).
– Workflow expansion: whether “trigger the next action” grows from notifications into broader execution (e.g., initiating payments) as confidence builds.
– Proof over time: measurable reductions in manual hours and exception rates as more SMEs run the system in production.
Context note: this partnership news is recent (March 2026), and broad, independently verified outcome data is still emerging.

Perspective note: This analysis is written from the viewpoint of Martin Weidemann (weidemann.tech), drawing on hands-on experience building and scaling technology-driven businesses in regulated fintech and payments environments, where reconciliation, cash visibility, and workflow automation are recurring operational bottlenecks.

This article reflects publicly available information about the Yapily–Cortena partnership at the time of writing, with a limited set of related figures for context. Some quantitative statements are company estimates and should be treated as directional until independently validated. Product capabilities, coverage, and performance may vary by bank, country, and SME workflow complexity, and details may change as new disclosures emerge.