Payments firms, neobanks, lenders, wealthtech, and infrastructure providers run on real-time decisions at a scale legacy institutions never face. In 2026 the hard part is shipping AI that scores fraud in milliseconds, underwrites thin-file customers on alternative data, automates KYC and AML without an army of analysts, and stands up to the EU AI Act — all in production, at volume. We build fintech AI that holds at speed, scale, and audit.
Digitally-native finance has none of the cover that legacy banks lean on: no overnight batch window, no branch network to absorb errors, and a customer base that churns on a single bad block. Over 90% of fintechs already run AI in core operations — the question in 2026 is whether those models survive real-time volume, adversarial fraud, and a regulator that now treats credit and fraud scoring as high-risk. The constraints below decide whether your AI scales or stalls.
When funds settle in seconds, fraud models built for ACH timelines are useless. Account takeover, scams, and synthetic identity move in milliseconds, so detection has to score every transaction inline — without false positives that block genuine customers and drive them to a competitor.
Manual onboarding and alert review scale headcount linearly with users — the one thing a fast-growing fintech cannot afford. The 2026 shift is to automate document checks, screening, and case triage so compliance keeps pace with sign-ups instead of throttling them.
The customers fintechs are built to serve often have no traditional credit history. Lending on alternative and transactional data can cut approval times from days to minutes — but only if every decision carries reasons specific enough to explain a decline to the customer and the regulator.
AI agents now initiate and authorise payments inside apps and partner products, and embedded finance puts your rails inside someone else’s checkout. That widens the attack surface and the accountability surface at once — every automated action needs validation and a control before it moves money.
Regulation now arrives faster than compliance teams can read it. Turning regulatory text into executable, testable checks is hard precisely because the rules are full of thresholds, exceptions, and cross-references — and getting it wrong at fintech volume is a systemic risk, not a single case.
From August 2026 the EU AI Act treats credit scoring, fraud profiling, and automated access decisions as high-risk — requiring logging, human oversight, and audit-ready documentation. Fintech teams ship weekly; the governance has to move at that cadence, not block it.
Fintech infrastructure carries live money across payments, lending, and embedded rails with no batch window to hide behind. We engineer the AI that runs on those rails — real-time scoring, automated decisions, and agentic operations — with the latency budgets, monitoring, and controls a payments-grade system demands. The standard is simple: it has to hold when the volume spikes and when an examiner asks how it works.
Scored inline
Decisions made in real time inside your transaction path — sized to your latency budget, not bolted on after the fact.
You own it
Code, models, prompts, and documentation are yours, designed so your engineering and risk teams can operate and validate them independently.
Each of these maps to an area we build in — engineered for fintech speed and volume, and governed so the decision can be explained and defended. Every route below links to how we deliver it.
Real-time models that score transactions and behaviour as they happen, adapt to new fraud and scam patterns, and forecast credit and portfolio risk — tuned to catch loss without burying genuine customers in false positives.
Predictive analytics →Decisioning that scores creditworthiness on transactional and alternative data, not just a thin credit file — with the reasons attached to every outcome, so an adverse action can be explained to the customer and defended to the regulator.
Decision intelligence →Identity documents, proof of address, and business-verification packs still arrive as photos and scans. We build extraction that reads them accurately, flags the exceptions, and cuts onboarding from days to minutes — with provenance back to the source.
Document intelligence →Agentic workflows that resolve support tickets, triage AML alerts, and clear operational exceptions with a human in the loop on the cases that matter — so a growing user base does not mean a linearly growing operations team.
AI agents →Grounded assistants over regulations, internal policy, and procedure docs — so compliance, risk, and support teams find and cite the controlling rule fast. Every answer carries a source the human can check before acting on it.
Knowledge & retrieval →The layer that gets the rest into production: validation, monitoring, explainability, and audit-ready documentation aligned to the EU AI Act high-risk regime — built so your risk team can own and defend every automated decision.
AI governance →Moving fast is the whole proposition — but a model that cannot be explained, monitored, or audited is a model a regulator can shut down at scale. We treat the controls below as part of the build, engineered to run inline at volume rather than added as paperwork at the end.
Scoring and decisions that run inside the transaction path, within the latency your product can spend.
Decision reasons attached to every prediction, not reconstructed after a complaint or a chargeback.
Performance, bias, and fraud-pattern drift watched continuously — with alerts before a model degrades in production.
Technical documentation, logging, and human-oversight controls mapped to EU AI Act high-risk obligations.
Finance covers banks and capital markets firms working under model risk management and legacy core integration. Fintech is the digitally-native side — payments, neobanks, lenders, wealthtech, and infrastructure or API companies. The pressures are different: real-time settlement, growth that outruns manual compliance, thin-file underwriting, and AI that has to ship at startup cadence while standing up to the same regulators.
Yes — that is the design constraint we start from. We engineer detection to score inline within your latency budget, using a fast model in the transaction path and heavier analysis where a few hundred milliseconds are available. The goal is to catch loss and adapt to new attack patterns without false positives that block genuine customers.
With transactional and alternative data, scored by models that attach specific reasons to every outcome. That lets you lend to thin-file customers and cut approval times to minutes — while keeping each decision explainable enough for an adverse-action notice and defensible to a regulator. We build the explainability in, not after the fact.
If you use AI for credit scoring, fraud profiling, or automated decisions that affect access to financial services, the relevant systems are classified high-risk — with obligations including logging, human oversight, and audit-ready documentation taking full effect from August 2026. We map your systems to those obligations and build the controls in, so readiness is part of delivery rather than a scramble before the deadline.
That is the point of doing it. We automate document checks, screening, and alert triage so compliance scales with sign-ups instead of with headcount — with a human in the loop on the cases that genuinely need judgement, and a clear audit trail on every automated step.
Yes. The code, the models, the prompts, the monitoring, and the documentation are all yours — designed so your engineering and risk teams can operate, retrain, and validate independently. Enablement is built into the engagement, not sold as an upsell.
A 30-minute conversation with a senior consultant. Bring a fraud model that is buckling under volume, an underwriting flow stuck in pilot, or a compliance backlog growing faster than the team. We will tell you what is ready to ship, where the gaps are, and what a focused engagement would surface.
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