Vertical AI Agents in Healthcare and Banking: Inside 2026's Highest-Stakes Agentic AI Deployments

Direct answer: Vertical AI agents — AI systems built for the specific workflows, data, and rules of a single industry rather than generic office productivity — are furthest along in healthcare and banking, the two regulated industries with the deepest deployment evidence so far. Gartner projects 80% of enterprises will have adopted vertical AI agents by 2026; healthcare agents are already handling clinical documentation and claims work with a projected $150 billion in annual savings, and banks are reporting double-digit efficiency and capacity gains from agents deployed against real operational workloads. That combination of real measured results and real regulatory stakes makes these two industries the clearest proof points — and the clearest risk test cases — for agentic AI outside generic use cases.

The Two Industries Where Agentic AI Actually Has to Prove Itself

Most agentic AI coverage focuses on horizontal use cases — a customer support agent, an internal knowledge assistant, a sales-qualification bot — that look impressive in a demo and carry a forgiving downside if something goes wrong: a wrong answer gets corrected, a missed lead gets found another way. Healthcare and banking are a different category entirely. Both are built on decades of regulatory infrastructure specifically designed around the fact that mistakes are expensive, sometimes irreversible, and heavily audited after the fact. An agent that gets a clinical note wrong, misapplies a lending decision, or mishandles a compliance check isn't a minor product bug — it's a regulatory incident with a paper trail, and often one with a named regulator, a specific reporting obligation, and a timeline for remediation attached to it, none of which apply when a horizontal chatbot gives an unhelpful answer.

That is exactly why these two verticals matter more than their market size alone would suggest. Gartner projects that 80% of enterprises will have adopted some form of vertical — meaning industry-specific, not general-purpose — AI agent by 2026, a figure cited widely across industry analyses including Turing's and OneReach.ai's separate 2026 research. But adoption breadth is a different question from adoption depth, and healthcare and banking are the two verticals where the research base is deep enough to actually assess what's working, not just what's being piloted. A vertical AI agent, in this framing, is not simply a general model with an industry-specific prompt bolted on — it's built around the specific data formats, workflows, and compliance obligations that a generic assistant has no native understanding of: a claims adjudication rule set, a clinical documentation standard, a know-your-customer checklist that differs by jurisdiction.

The reason healthcare and banking specifically have pulled ahead of other regulated verticals in the evidence base isn't accidental. Both industries have enormous volumes of highly structured but still judgment-requiring paperwork — clinical notes, insurance claims, loan applications, transaction monitoring — that sits exactly in the zone agentic AI is best suited for: too variable for a fixed rules engine, too high-volume for a purely manual process to scale affordably, and valuable enough that even a partial improvement justifies real investment. That combination of scale, structure, and stakes is what turned these two industries into 2026's clearest test of whether agentic AI actually works under real-world regulatory pressure, rather than just in a controlled pilot.

This also reshapes how a healthcare or banking organization should actually evaluate a vendor pitching "AI agents" for their business. A demo that looks impressive against a clean, hypothetical scenario tells you very little about how the same system behaves against the messy edge cases that make up a meaningful share of real clinical documentation or real transaction monitoring — the ambiguous case, the incomplete record, the borderline judgment call that a human specialist would flag for a second opinion. The vendors producing genuinely credible results in this space are, almost without exception, the ones who can show how their system handles exactly those edge cases, with an auditable record of what it did and why, rather than only showcasing the cases where the answer was obvious. That is a meaningfully higher bar than most horizontal AI product demos are built to clear, and it is the bar regulated-industry buyers should actually be holding vendors to.

Healthcare: From Clinical Documentation to Claims

Healthcare's adoption curve is the more advanced of the two by several measures. KPMG's research, cited in OneReach.ai's 2026 "Agentic AI Stats" analysis, found that 68% of healthcare organizations are already using AI agents in some capacity — not evaluating, not piloting in isolation, but actively using them — and 84% of respondents reported being comfortable with agents making autonomous decisions within defined boundaries. That comfort level is notable given how conservative healthcare technology adoption has historically been, and it suggests the value proposition in the highest-friction administrative workloads has become hard to argue with even for a famously risk-averse sector.

The dollar figure behind that comfort is substantial: Accenture's projection puts potential annual healthcare savings from AI agents at $150 billion by 2026. That number is a system-wide estimate, not a single organization's result, but it points to where the value concentrates — administrative and documentation overhead that consumes clinician time without directly improving care, which is exactly the kind of workload agentic AI is best positioned to absorb without touching actual clinical decision-making.

The most concrete evidence comes from AtlantiCare's case study, cited in the same research: a 42% reduction in documentation time after deploying AI agents into clinical workflows. That is a specific, measurable operational result, not a projection, and it maps directly onto healthcare's best-known burnout driver — clinicians spending a disproportionate share of their working hours on notes, coding, and administrative documentation rather than direct patient care. An agent that reliably drafts, structures, or pre-fills documentation from a clinical encounter, leaving a clinician to review and finalize rather than compose from scratch, is solving a problem hospital administrators have been trying to solve with process changes and staffing for years, without the sustainable success documentation automation now appears to be delivering.

Claims processing is the other major healthcare application, and it follows a similar logic: an insurance claim is a highly structured document that still requires judgment calls — is this procedure covered, does the documentation support the billing code, does this claim need human escalation — that a purely rules-based system handles poorly and a purely manual review process handles slowly. Vertical agents built specifically around claims adjudication rules and coding standards, rather than a general-purpose assistant improvising against the same rules, are where healthcare's claims-side gains are concentrating.

What separates a genuinely vertical healthcare agent from a generic assistant pointed at healthcare data is usually invisible from the outside but decisive in practice: it's built around the actual coding standards (ICD, CPT, and payer-specific rules), the actual documentation formats clinicians already use, and the actual escalation paths a hospital's compliance team already has in place, rather than asking clinicians to adapt their workflow to fit the tool. A generalist model can often produce a plausible-looking clinical note or a plausible-looking claims justification, but "plausible-looking" is precisely the failure mode healthcare can least afford — a confidently wrong summary of a patient encounter, inserted into a permanent medical record, is a categorically worse outcome than a documentation delay would have been. That is why the healthcare deployments with real staying power are, almost universally, narrow and specialist rather than broad and generic, and why 68% adoption coexists comfortably with a still-cautious approach to what, specifically, gets automated.

Banking and Financial Services: Efficiency Under Regulatory Scrutiny

Banking's numbers tell a similar story of measurable operational gain layered under real regulatory weight. The World Economic Forum and Accenture project $97 billion in AI investment across banking by 2027 — a figure that signals banks are treating this as core infrastructure spending, not an experimental line item that gets cut in the next budget cycle.

Bradesco's case study, also cited in OneReach.ai's research, is the clearest single data point: 17% of capacity freed up and a 22% reduction in lead times after deploying AI agents into its operations. Read together, those two figures describe exactly the kind of result that justifies continued investment — not a marginal efficiency tweak, but a meaningful capacity unlock that lets existing staff handle more volume or shift attention to higher-judgment work, paired with a genuinely faster cycle time on whatever process the agents were deployed against.

Both figures matter for a reason that's easy to miss if you only look at the headline percentages: capacity freed up is a very different kind of result than lead-time reduction, and a bank getting both from the same deployment suggests the agent wasn't just handling more volume faster — it was removing steps from the process itself, not simply accelerating the same steps a human used to do manually. That distinction is a useful diagnostic for evaluating any banking AI agent pitch: ask whether the claimed efficiency gain comes from the agent working faster than a human at the same task, or from the agent eliminating a handoff or a review step that no longer needs to exist at all. The second kind of gain tends to compound in ways the first doesn't, because it changes the shape of the underlying workflow rather than just its speed.

Fraud Detection at Massive Scale

Fraud detection deserves its own callout because it is the banking vertical-agent application with the clearest, most quantifiable stakes. Feedzai, a fraud-detection-focused vertical AI company cited in Turing's "Vertical AI Agents Reshaping Industries" research, protects more than 1 billion consumers and secures roughly $8 trillion in payments annually. Those are not modest numbers by any measure, and they illustrate something important about vertical AI economics that goes beyond banking specifically: a vertical agent company built around one deeply specific problem — in this case, distinguishing fraudulent transactions from legitimate ones in real time, at a scale no rules-only system could keep pace with — can reach genuinely enormous scale precisely because it never tried to be a general-purpose AI platform. It built the narrowest possible product for the highest-stakes possible problem, and depth won over breadth.

That pattern — depth beating breadth in exactly the highest-consequence workflows — is a big part of why banking and healthcare specifically, rather than industries with lower regulatory and financial stakes, have produced the most convincing vertical AI agent evidence so far. The stakes forced the specialization that made the agents actually good at their one job.

Why Vertical Agents Are Outpacing Horizontal Platforms Right Now

The market-level evidence backs up what the healthcare and banking case studies suggest anecdotally. Bessemer Venture Partners projects that the vertical-AI market cap could grow to 10 times larger than legacy SaaS, and separately finds that vertical AI companies founded after 2019 are already reaching roughly 80% of traditional SaaS companies' contract values while growing 400% year-over-year — a growth rate that would be an outlier even by software industry standards, let alone by the standards of enterprise software sold into conservative, regulated buyers.

A separate market-discovery data point reinforces this at the deal-flow level: vertical AI agents accounted for 48.3% of 2026 year-to-date agentic-AI deals and 54.6% of capital deployed into agentic AI overall, concentrated specifically in healthcare operations, finance operations, compliance, and insurance. That concentration is the clearest signal available that investors, not just industry analysts, believe the deepest near-term value in agentic AI sits in exactly the vertical, regulated-industry applications this piece is about — not in the more general-purpose agent platforms that dominate early press coverage.

The underlying logic is straightforward once you look past the marketing language both categories use. A horizontal platform has to be good enough at many things to justify broad adoption, which means it rarely gets deep enough at any one regulated workflow to displace a purpose-built alternative in that specific domain. A vertical agent company can pour its entire engineering and domain-expertise budget into one workflow — clinical documentation, fraud detection, KYC processing — and reach a level of accuracy and compliance-readiness a generalist platform structurally cannot match without years of additional, narrowly-focused investment. In regulated industries specifically, where the cost of being wrong is a fine or a licensing action rather than an annoyed customer, that depth advantage is worth paying a premium for, which is exactly what the contract-value data above suggests buyers are doing.

There's a second, less obvious factor behind the funding and contract-value numbers: regulated-industry buyers are themselves slower and more deliberate purchasers than the average SaaS customer, which means a vertical AI company that survives its first two or three enterprise sales cycles in healthcare or banking has already proven something a horizontal AI startup usually hasn't had to prove yet — that its product can pass procurement, security review, and compliance sign-off, not just a technical evaluation. That survivorship effect is part of why the vertical AI companies with real traction in these two industries tend to command premium contract values: the buyer isn't just paying for the software, they're paying for a vendor that has already been through the regulatory gauntlet their own compliance team would otherwise have to navigate from scratch.

The Stakes: What Happens When a Regulated-Industry Agent Gets It Wrong

The flip side of healthcare and banking's outsized value potential is outsized downside risk, and nowhere is that clearer than in how regulators are already responding. Nabla, a clinical-AI vendor operating in the European market, has been classified "high-risk" under Annex III of the EU AI Act — a classification that comes with real, binding obligations: audit trails detailed enough to reconstruct how a specific output was generated, documented risk management processes, and ongoing conformity assessment rather than a one-time certification. That is not a hypothetical compliance burden being discussed in a policy paper; it's an active regulatory classification already shaping how a real clinical-AI vendor has to operate in Europe.

The lesson generalizes well beyond that one vendor and one jurisdiction. Any organization deploying a vertical AI agent into clinical decision support, credit decisioning, or anti-money-laundering compliance should assume regulatory classification and audit-readiness are not optional add-ons to bolt on later — they are core requirements that shape the architecture from day one, the same way a hospital's documentation systems are built around audit and compliance requirements before a single feature gets added for convenience. An agent that performs well in testing but can't produce an auditable record of why it reached a specific conclusion is not deployment-ready in either industry, regardless of how good its accuracy numbers look in isolation.

The evaluation discipline this demands is also different in kind, not just in degree, from evaluating a general-purpose assistant. A support chatbot can be judged mostly on whether its answers are helpful and accurate on average. A clinical documentation agent or a fraud-detection agent has to be judged on how it behaves specifically in its worst cases — the rare, high-stakes, easy-to-miss scenario — because that is exactly where the cost of an error concentrates. A fraud model that is 99% accurate overall but systematically misses one specific pattern of high-value fraud has a much more dangerous failure profile than the raw accuracy number suggests, and the same logic applies to a clinical agent that performs well on routine encounters but degrades on complex, multi-condition cases. Evaluation frameworks for these two industries need to be built around exactly that asymmetry — testing deliberately against rare and edge-case scenarios, not just against a representative sample of routine ones — which is a meaningfully more expensive and more deliberate testing investment than most horizontal AI deployments require.

This is also where the comfort level noted earlier in healthcare — 84% of respondents comfortable with autonomous agent decisions — needs a caveat: comfort with autonomy inside a well-defined, well-audited boundary is a very different thing from comfort with unrestricted autonomy, and the organizations getting real value from these deployments are almost universally the ones that drew that boundary carefully rather than maximizing how much decision-making they handed off on day one. The same discipline applies in banking, where a fraud-detection agent flagging a transaction for review is a very different risk profile than an agent empowered to freeze an account or deny a loan without a human confirming the call.

The practical way to think about this boundary is to separate three distinct levels of agent involvement, because "autonomous decision-making" collapses a genuine spectrum into one loaded phrase. At the first level, the agent investigates and surfaces information — reviewing a claim or a transaction and presenting the relevant facts to a person, with no decision authority of its own. At the second, the agent recommends a specific action — approve, deny, escalate — but a human has to actively confirm it before anything happens. At the third, the agent acts independently within a pre-approved, narrow boundary, such as auto-approving a claim only when it meets every criterion for a low-risk, low-value category the organization has explicitly defined in advance. Nearly every credible healthcare and banking deployment reviewed in this research sits at the first or second level for anything with real financial or clinical consequence, reserving the third level for the narrowest, lowest-stakes slice of a workflow where the definition of "safe to automate fully" has been made explicit rather than assumed.

How This Is Playing Out Region by Region

The regional picture for vertical healthcare and banking agents is genuinely uneven, and worth walking through honestly rather than smoothing into a single global narrative.

In the United States, this research did not surface a distinct US-only statistic separate from the KPMG, Accenture, and WEF figures already cited above — those figures are effectively US/global-blended in how they're reported, which is common for research produced by firms with a heavy US enterprise client base, but means there isn't a clean US-specific number to cite independently.

The UK shows no distinct healthcare- or banking-specific vertical-agent statistic beyond the general production-adoption figures already established for agentic AI more broadly.

The UAE and Dubai stand out as a genuinely distinct regional story: financial institutions based in the DIFC and ADGM — the UAE's two major financial free zones — are specifically reported to be leading AI-agent adoption for regulatory reporting and automated KYC and anti-money-laundering processing. That positions the UAE's financial sector as an early, deliberate mover in exactly the compliance-heavy banking use case this piece covers, rather than a market following behind US or European adoption.

Australia did not surface a distinct regional statistic in this research.

Germany leads Europe in industrial and manufacturing vertical agentic AI specifically, rather than healthcare or banking — a genuinely different vertical focus than the one this piece covers, and no Germany-specific healthcare or banking agent statistic was found to report here.

France and Europe show two distinct data points worth separating. Druid AI's 2026 benchmark cites an unnamed "leading European telecommunications provider" and Auchan, the European retail chain, as vertical multi-agent case studies — again, outside healthcare and banking specifically. Within France directly, the Nabla/EU AI Act Annex III classification described above is the concrete, healthcare-specific regulatory data point, and it's a significant one: it shows a European clinical-AI deployment already operating under binding high-risk obligations rather than under voluntary guidelines.

China shows the most industry-specific vertical deployment detail of any region in this research: Ping An and Ant Group are named for insurance claims pre-review, and JD.com and SF Express for supply chain — with finance, e-commerce, manufacturing, and government named as the top adoption-priority industries in a 2026 China-focused agent guide. Insurance claims pre-review sits close enough to this piece's healthcare-and-banking focus to be directly relevant, and it shows Chinese vertical deployment concentrating in large, established platform companies rather than the smaller specialist vendors that characterize the Western vertical-AI funding story.

Stepping back across all seven regions, the pattern worth naming honestly is uneven visibility rather than uneven adoption. It's entirely possible that healthcare and banking vertical agents are being deployed quietly in Australia, the UK, or Germany without yet producing the kind of named case study that made it into this research — absence of reporting is not evidence of absence of activity. What the regional picture does show clearly is where the documented proof points currently concentrate: the UAE's financial free zones for compliance-heavy banking use cases, France for the regulatory consequences of clinical AI specifically, and China for large-platform-company insurance and supply-chain deployment. Any organization benchmarking its own market against "what everyone else is doing" should treat a region's absence from this list as a research gap to investigate locally, not as proof that vertical agents haven't reached that market yet.

Assessing Readiness and Building Responsibly

For a healthcare or financial-services organization evaluating whether to invest in a vertical AI agent, the evidence above suggests a fairly specific set of questions worth answering before committing budget, rather than starting from "what can AI do for us" in the abstract. Where does documentation, claims, or compliance work currently consume disproportionate staff time relative to the judgment it actually requires? Is the workflow structured enough that a vertical agent built specifically around it — not a generic assistant — could plausibly reach the accuracy and audit-readiness the regulatory environment demands? And critically: has the organization mapped what happens when the agent is wrong, and who is accountable for catching it, before a single line of the system goes live against real patient or customer data?

That last question is where the AtlantiCare and Bradesco results actually came from — not from maximizing how much was automated, but from scoping a specific, well-bounded workflow, building the guardrails and audit trail the regulatory environment required, and only then measuring the result. Our industries overview covers how this scoping discipline plays out across healthcare, financial services, and other regulated sectors specifically, and our case studies page shows what a properly scoped build looks like end to end rather than as a pilot that never left the sandbox.

A short, practical checklist worth working through before committing budget to a vertical agent build in either industry:

• The specific workflow's failure mode is mapped in writing — what happens, concretely, if the agent gets this wrong, and who catches it before it reaches a patient or a customer
• The workflow is structured enough to define clear rules for what the agent handles independently versus what it escalates, rather than leaving that boundary to be discovered after launch
• Audit logging and an auditable reasoning trail are part of the initial build, not a follow-on phase added after a regulator or an internal audit asks for one
• A named clinician, compliance officer, or equivalent domain specialist reviews a sample of the agent's real output on an ongoing basis, not just during initial testing
• The vendor or build partner can point to how their system handles ambiguous, borderline cases specifically, not only the clean cases that make for a good demo
• There's a documented plan for what happens if the agent needs to be paused or scoped back after launch — not because failure is expected, but because a plan made calmly in advance is always better than one improvised during an actual incident

For organizations further along and evaluating specific vendors or build partners, the EU AI Act classification story above is a useful test question to ask directly: can this vendor produce an auditable record of how a specific decision was reached, today, not as a roadmap item? Our compliance resources cover the broader governance discipline that question sits inside, and if you're scoping a vertical agent build for a regulated workflow and want that audit-readiness designed in from the start rather than retrofitted after a regulator asks, that is core to the AI agents and automation work we do at Scult — the architecture decisions that make a system genuinely audit-ready are far cheaper to make before launch than to add afterward.

What Healthcare and Banking Leaders Are Actually Asking About Vertical AI Agents

What's the difference between regular AI and agentic AI in healthcare and banking?

"Regular" AI in these industries typically means a model that answers a question or classifies a document when asked — useful, but passive. Agentic AI goes further: it takes a goal (process this claim, review this transaction) and works through the multiple steps needed to complete it, checking relevant systems and applying the specific rules of that workflow along the way, only stopping to hand off to a human at a defined point. In healthcare and banking specifically, that distinction matters because the multi-step, rule-heavy nature of claims processing, clinical documentation, and compliance review is exactly the kind of task a single-turn model can't meaningfully automate but a properly scoped agent can.

What ROI should a bank or hospital realistically expect from deploying AI agents?

The realistic range, based on the case studies available, is a meaningful operational efficiency gain in a specific, well-bounded workflow rather than a transformation of the entire organization overnight. AtlantiCare saw a 42% reduction in documentation time; Bradesco saw 17% of capacity freed and a 22% reduction in lead times. Both results came from scoping a specific process tightly rather than automating broadly, which is the pattern worth replicating: pick the highest-volume, most rules-bound workflow currently consuming disproportionate staff time, and measure the result there before expanding scope.

Which industries are actually leading vertical AI agent adoption?

Healthcare and banking/financial services have the deepest published evidence base, per Gartner's 80%-of-enterprises-by-2026 vertical-adoption projection and the case studies from KPMG, Accenture, and Bradesco covered above. Beyond those two, insurance (claims pre-review), manufacturing, and supply chain show up repeatedly across regional data, including Ping An and Ant Group in China's insurance sector and Germany's manufacturing-focused vertical agentic AI. The common thread across every leading industry is high-volume, rules-heavy work that still requires enough judgment to resist full automation by a fixed rules engine alone.

How do we assess whether our organization is ready for vertical AI agents?

Start by mapping where documentation, claims, or compliance work consumes disproportionate staff time relative to the actual judgment involved — that's the workload profile every successful case study above shares. Then assess whether the workflow is structured enough for a vertical agent to reach the accuracy and audit-readiness your regulatory environment requires, and whether there's a named person accountable for reviewing agent decisions after launch. Organizations that skip this mapping step and instead start with "what can AI automate here" in the abstract are the ones most likely to launch a pilot that never reaches production.

How much can healthcare AI agents actually save on clinical documentation time?

The most concrete published figure is AtlantiCare's case study: a 42% reduction in documentation time after deploying AI agents into clinical workflows. That's a specific, measured operational result rather than an industry-wide projection, and it targets exactly the workload clinicians most often cite as the biggest drag on their time — notes, coding, and administrative documentation that takes time away from direct patient care. Results at other organizations will vary with how the agent is scoped and how much of the existing documentation workflow it's actually integrated into, but a result in that general range is a reasonable benchmark for a well-scoped clinical documentation deployment.

Can AI agents be trusted to handle KYC and anti-money-laundering compliance?

They're already being trusted with meaningful parts of it — UAE financial institutions in the DIFC and ADGM are specifically reported to be leading adoption of AI agents for regulatory reporting and automated KYC/AML processing, which suggests regulators and institutions in at least one major financial hub see this as viable at real scale. The trust boundary that matters in practice is usually not "can an agent review a KYC file" but "who confirms the final compliance decision" — the workable pattern in most live deployments is the agent doing the investigation and flagging, with a compliance officer making the final call on anything ambiguous or high-risk, rather than full unsupervised automation of the decision itself.

Do vertical AI agent companies really out-earn traditional SaaS companies?

The data suggests they're closing the gap fast, if not fully there yet. Bessemer Venture Partners finds that vertical AI companies founded after 2019 are already reaching roughly 80% of traditional SaaS companies' contract values while growing 400% year-over-year — a growth rate well beyond typical enterprise software benchmarks. Feedzai's scale (protecting over 1 billion consumers and securing roughly $8 trillion in payments annually) shows what that trajectory looks like at the high end for a company that specialized narrowly rather than building a general-purpose platform. Depth in one regulated, high-stakes workflow appears to be a genuinely strong business model, not just a good technology story.

What happens if a clinical AI agent gets classified high-risk under the EU AI Act?

Using Nabla's classification under Annex III as the concrete example, it means binding obligations, not voluntary guidelines: the vendor has to maintain audit trails detailed enough to reconstruct how specific outputs were generated, documented risk-management processes, and ongoing conformity assessment rather than a one-time certification that's filed away. For any organization evaluating a clinical AI vendor operating in or selling into Europe, this makes "can you show us your Annex III compliance posture, today" a legitimate, concrete procurement question rather than a hypothetical one — our compliance resources cover the broader governance pattern this kind of obligation sits inside.

Is agentic AI a bigger near-term opportunity in healthcare or in banking?

Both show comparably strong signals through different lenses, which makes this less a competition than two industries validating the same underlying pattern from different angles. Healthcare shows the deeper adoption penetration — 68% of organizations already using AI agents in some capacity per KPMG — alongside the larger headline savings figure ($150 billion projected annually by Accenture). Banking shows the larger forward investment commitment ($97 billion projected by 2027 per WEF/Accenture) and arguably the single most dramatic proof point in Feedzai's fraud-detection scale. Rather than picking a winner, the more useful takeaway is that both industries are validating the same thesis: regulated, high-volume, judgment-requiring workflows are where vertical agents currently deliver the clearest, most measurable value.

How many consumers do fraud-detection AI agents actually protect at scale?

Feedzai, the vertical AI company focused specifically on fraud detection cited in Turing's research, protects more than 1 billion consumers and secures approximately $8 trillion in payments annually. That scale is worth sitting with because it demonstrates something beyond just Feedzai's own success: a vertical agent built around one narrowly defined, high-stakes problem — telling fraudulent transactions apart from legitimate ones in real time — can reach global-infrastructure scale precisely because it never diluted its focus trying to be a general-purpose AI platform. That depth-over-breadth pattern is a recurring theme across the strongest vertical AI results in both banking and healthcare.