The High-Risk AI Compliance Delay: The Checklist Fintech Startups Actually Need in Europe

Direct answer: The compliance deadline for high-risk AI systems used in areas like credit scoring, recruitment, and education has been pushed to December 2027, giving fintech startups in Europe more runway than they thought they had. That extra time is only useful if it goes into building auditable, well-documented credit and risk systems now, rather than being treated as a reason to defer the work entirely.

According to the EU AI Act timeline update, 2026, the compliance deadlines for high-risk AI systems covering recruitment, credit scoring, and education have been pushed back to December 2027. This is one of several phased dates the AI Act sets out across different obligation categories, and it is the one most directly relevant to any fintech startup whose product touches lending, underwriting, or credit-line decisions. For a fintech startup running AI-assisted credit decisions, that is a meaningful change to the calendar most compliance and engineering teams had already built their 2026-2027 roadmaps around. A precise breakdown of which sub-obligations move together with this date is not publicly available at this level of detail, and we won't guess at it — but the general pattern is clear: the EU has repeatedly shown it will extend high-risk timelines rather than force under-prepared systems into production under a hard deadline. For a startup building a credit scoring engine, a fraud detection model, or an onboarding decision system, this is genuinely good news, but only if the extra time gets spent building the compliance groundwork the regulation ultimately wants, not spent assuming the requirement has gone away. Founders who read this as "we can now ignore the AI Act" are misreading the situation; founders who read it as "we now have a realistic window to build this properly instead of rushing it" are the ones who will actually be ready when the deadline lands.

What the December 2027 Delay Actually Changes

The EU AI Act classifies certain AI use cases as "high-risk" because they materially affect a person's access to opportunity or resources — credit decisions, hiring, and education access are three of the clearest examples. High-risk classification triggers a specific set of obligations: risk management systems, data governance requirements, technical documentation, human oversight mechanisms, logging and traceability, and conformity assessments before the system can be deployed at scale.

What moved, per the EU AI Act timeline update, 2026, is the date by which systems in these categories must meet those obligations — now December 2027 rather than the earlier target. This is not a repeal of the requirements themselves. Nothing about what counts as high-risk has changed, and nothing about the substance of the obligations has been softened based on what's publicly known. What changed is the runway.

Why This Kind of Delay Happens

Large-scale regulatory frameworks that touch software systems as complex as AI-assisted credit engines tend to get phased-in dates adjusted when regulators recognize that the compliance infrastructure — accredited assessors, standardized documentation templates, national enforcement bodies — isn't fully in place yet. That is a normal pattern in EU digital regulation, not a signal that the underlying rules are in doubt. Startups that built their internal roadmaps assuming the original date should now have room to do the underlying engineering work with less time pressure, not room to stop the work.

Why Fintechs Specifically Should Care

Credit scoring is one of the three categories named explicitly in the delay. If your fintech startup in Europe touches lending decisions, buy-now-pay-later underwriting, credit line increases, or any automated risk scoring that determines what a customer is offered, you are squarely inside the high-risk category this delay applies to. That makes the extra runway directly relevant to your product roadmap, not a peripheral policy footnote.

Why This Matters Specifically for Fintech Startups in Europe

European fintech startups sit in an unusual position relative to this regulation. Many are small teams — sometimes under twenty engineers — building credit and risk products that, by their nature, fall into the highest-risk category the AI Act defines. A larger bank has compliance departments, external counsel, and dedicated AI governance teams already tracking every clause. A fintech startup usually has a founder, a head of product, and an engineering lead trying to ship a lending product while also keeping an eye on regulatory exposure they didn't budget headcount for.

The practical effect of the December 2027 date is that these startups now have roughly another year of runway to build the technical documentation, audit trails, and human-oversight mechanisms the regulation will eventually require — without having to freeze feature development to hit an earlier deadline. That is a real advantage if it's used to build the underlying system correctly. It is a liability if it's used to defer the work until the deadline is imminent again, because the kind of documentation, logging, and explainability infrastructure this regulation wants cannot be retrofitted onto a credit engine in a few weeks. It has to be designed into how the system is built, tested, and deployed from the start.

There's also a competitive dimension. Fintech startups that can demonstrate a credible, well-documented compliance posture — even ahead of the legal deadline — have an easier time in due diligence conversations with banking partners, payment processors, and investors who are increasingly asking AI governance questions as a matter of course. A startup that treats the extended deadline as a shortcut to defer this work is choosing to walk into those conversations later with less to show.

Consider also the operational reality of raising a Series A or B round as a European fintech in 2026 and beyond. Investors doing technical due diligence on a lending or credit product are no longer satisfied with a slide that says "we use AI to score applicants." They ask how decisions are logged, whether a human can review and override an automated outcome, and how the company would respond if a regulator asked for a full audit trail on a specific applicant's decision six months after the fact. A startup that can answer those questions concretely, with a system built to support the answer, moves through diligence faster than one that has to promise it will "get to compliance before the deadline." The extended timeline doesn't remove that pressure from investors and partners — if anything, it raises the bar, because there's no longer a credible excuse for not having started.

There's a second, quieter reason this matters for fintech startups specifically: talent and hiring. Engineers and product leads who have worked at regulated financial institutions increasingly expect to see evidence of serious compliance engineering when they evaluate a startup to join. A credit product built with no thought given to traceability or oversight can be a red flag in interviews with exactly the senior hires a startup most wants to attract. Building this well is not only a regulatory hedge — it's also a signal about how seriously the engineering organization takes the product it's building.

What Changes in Practice for Your Product and Codebase

For a founder or CTO at a European fintech startup, the delay changes timing, not scope. Here is what still needs to be true of any AI-assisted credit, risk, or eligibility system by the time the new deadline arrives, and what it means for how the system should be architected now.

Traceability Has to Be Built Into the System, Not Bolted On

Every credit or risk decision your AI system makes needs to be traceable back to the inputs that produced it, the model version that ran, and the logic path that led to the outcome. This is fundamentally a software architecture decision. If your scoring pipeline logs decisions as opaque model outputs with no structured record of inputs, feature versions, and model versions, you're building technical debt that becomes a compliance emergency later. Custom software built with this in mind from day one — structured decision logs, versioned model artifacts, reproducible scoring pipelines — is dramatically cheaper to build than trying to retrofit auditability onto a system that was never designed to explain itself.

Human Oversight Needs a Real Interface, Not a Checkbox

High-risk AI obligations require meaningful human oversight of automated decisions, which in practice means your product needs actual screens and workflows for a human reviewer to inspect, question, and where necessary override an automated credit decision. This is a UX and engineering problem as much as a compliance one. If a declined applicant's case file, when a human reviewer opens it, is just a raw score with no context, that's not oversight — it's theater. Building the moments where a decision doesn't go the applicant's way, and where a reviewer needs to inspect what happened, deserves the same care as the paths where everything goes smoothly. Our thinking on Empty States and Error Screens: Designing for the Moments Users Struggle applies directly here — a rejection or override screen that a compliance reviewer or an affected customer will actually look at is not a place to leave a bare error message or an unexplained decline.

Data Governance Needs to Match the System That Actually Runs in Production

Data governance obligations under the AI Act require documented data quality, bias testing, and provenance tracking for training and scoring data. If your credit engine pulls data from three different internal systems — a CRM holding customer relationship history, a transactions database, and a third-party bureau feed — that data lineage has to be documented and reproducible. This is another argument for investing in the underlying data infrastructure now rather than later. If your customer data currently lives in a CRM that wasn't built with this kind of governance and export requirement in mind, it's worth revisiting that decision early. Our comparison of Custom CRM Development: Build vs Buy for Indian Businesses walks through the trade-offs between an off-the-shelf CRM and a custom-built one when your data governance requirements are unusually specific — a decision that applies just as directly to European fintechs building regulated data pipelines.

This matters more than it might seem at first glance, because most off-the-shelf CRM and data platforms were not designed with regulatory audit trails in mind — they were designed to make sales and support workflows fast, which usually means data gets edited, merged, and overwritten in place rather than versioned. A credit decision made using a customer record that has since been changed needs to be reconstructable using the record as it existed at decision time, not the record as it exists today. That single requirement alone rules out a lot of generic tooling and points toward a data architecture that treats every meaningful change as an append rather than an overwrite. Getting this right earlier, while your customer base and data volume are still manageable, is considerably less painful than migrating a live production system later under deadline pressure.

Monetization and Access Tiers Need Documentation Too

If your product gates access to credit features, premium risk tools, or advanced scoring insights behind a subscription or in-app purchase, the eligibility logic behind those gates can itself become part of the audit trail if it interacts with a high-risk decision. Getting the technical implementation of tiered access right matters here too — our In-App Purchases and Subscriptions: Implementation Guide for Mobile Apps covers the mechanics of building subscription and access-tier logic cleanly, which is directly relevant if your credit product has paid tiers that interact with automated eligibility decisions.

What to Do About It Now

The honest answer for most European fintech startups is: use the extra runway to build the system you would have needed to rush by the earlier deadline, at a pace that doesn't compromise quality. Concretely, that means:

1. Audit your current credit or risk-scoring pipeline for whether decisions are traceable, versioned, and reproducible today. If they aren't, that's the first gap to close.
2. Design human oversight into the product, not as an afterthought screen but as a genuine review workflow with the context a reviewer needs.
3. Document your data lineage from source systems through to the features your model actually uses.
4. Treat this as a software architecture project, not a legal-only exercise — the underlying obligations largely translate into engineering requirements: logging, versioning, explainability, and access control.
5. Build incrementally rather than in a rush before the new deadline — a system built calmly over twelve to eighteen months will be more reliable and more defensible than one assembled in the final quarter before December 2027.

Sequencing matters here as much as the list of tasks itself. Starting with the audit gives you a factual baseline instead of a guess, and it's usually the fastest of the five steps to complete, which makes it a sensible first move even for a startup with limited engineering bandwidth to spare this quarter. From there, the logging and versioning work tends to unlock everything else — once decisions are traceable, building the oversight interface and documenting data lineage both become easier because you finally have the underlying data to work from. Startups that try to build the oversight interface first, before the underlying logging exists, usually end up building a screen that has nothing meaningful to show.

It's also worth being realistic about who should own this work internally. In a very early-stage team, the same engineer building the credit product may end up owning the compliance architecture too, and that's workable as long as it's treated as a first-class part of the roadmap rather than something squeezed in between feature sprints. As the team grows, it's worth designating a specific owner — whether that's a senior backend engineer or eventually a dedicated compliance engineering hire — so this work doesn't quietly slip whenever a new feature deadline takes priority.

This is exactly the kind of work that benefits from being built as custom software rather than assembled from generic tooling that wasn't designed with this level of auditability in mind. Scult's Custom Software Development practice is built around exactly this kind of problem — systems where traceability, data governance, and explainable decision logic aren't nice-to-haves but core requirements from the first line of code.

Pricing Context: What This Kind of Work Typically Falls Under

Building or retrofitting a credit-decision or risk-scoring system for AI Act-aligned traceability is a scoped engineering project, and the right tier depends on how much of the pipeline needs rebuilding versus incrementally strengthening.

Tier	Typical scope for this scenario
Essential ($1,000)	Audit and documentation of an existing scoring pipeline's data lineage and decision logging, with a gap report against high-risk obligations
Growth ($2,000)	Adding structured decision logs, model versioning, and a human-oversight review interface to an existing credit or risk product
Enterprise ($4,000+)	Full custom build or rearchitecture of a credit-scoring engine with end-to-end traceability, data governance, and audit-ready reporting baked in

These figures reflect what this kind of engagement typically starts at; the right scope depends on how much of your current stack already supports traceable, versioned decisions versus how much needs to be built from scratch.

Key Takeaways

• The high-risk AI compliance deadline for credit scoring, recruitment, and education has moved to December 2027, per the EU AI Act timeline update, 2026 — this is a timing shift, not a reduction in scope.
• Fintech startups running AI-assisted credit or risk decisions in Europe fall squarely into the high-risk category this delay applies to.
• Traceability, human oversight, and data governance need to be architected into the system now — they cannot be retrofitted quickly once the deadline is close again.
• Rejection and override screens for automated decisions deserve real product design, not placeholder error states.
• Data lineage and CRM architecture decisions made today directly affect how defensible your compliance posture will be later.
• Use the extra runway to build calmly and correctly rather than to defer the work until the pressure returns.

The extra time on the calendar is only valuable if it gets spent building the traceable, well-documented system your credit or risk product will eventually need to demonstrate. If you want help figuring out where your current pipeline stands against these requirements and what to build next, book a meeting with our team.

Frequently Asked Questions

What is the EU AI Act's high-risk classification, and why does credit scoring fall under it?

The AI Act classifies AI systems as high-risk when they materially affect a person's access to opportunity, resources, or fundamental rights. Credit scoring qualifies because an automated decision can determine whether someone gets a loan, a credit line, or favorable terms, directly affecting their financial life.

What exactly changed with the December 2027 date?

The compliance deadline by which high-risk AI systems in recruitment, credit scoring, and education must meet the AI Act's obligations was pushed back to December 2027, according to the EU AI Act timeline update, 2026. The obligations themselves were not removed or softened based on what's publicly known — only the date by which they must be met has shifted.

Does this delay mean fintech startups can ignore AI Act compliance until 2027?

No. Treating the delay as permission to ignore compliance work is the riskiest possible interpretation. The obligations — traceability, human oversight, data governance — take significant engineering time to build properly, and starting late means rushing exactly the kind of system that should not be rushed.

Is my credit-scoring model definitely "high-risk" under the AI Act?

If your AI system makes or materially informs decisions about whether someone receives credit, what terms they're offered, or whether their application is approved, it is very likely to fall under the high-risk category. The specifics of borderline cases should be confirmed with legal counsel familiar with the regulation.

What counts as "traceability" in a credit decision system?

Traceability means every automated decision can be tied back to the specific inputs used, the version of the model that ran, and the logic path that produced the outcome. It requires structured, versioned logging built into the scoring pipeline itself, not just a final score stored without context.

Why can't traceability be added to an existing system quickly?

Retrofitting traceability onto a system that wasn't designed to log inputs, model versions, and decision paths usually means rebuilding core parts of the scoring pipeline rather than adding a feature on top. Systems built without this in mind from the start tend to require substantial rearchitecture to become genuinely auditable.

What does "human oversight" actually require in a live product?

It requires a real interface where a human reviewer can inspect an automated decision, see the context behind it, and override it when necessary. A raw score with no supporting detail does not satisfy meaningful oversight — the reviewer needs enough information to make an informed judgment.

How does this affect a rejection or decline screen in my app?

A decline screen tied to an automated credit decision is one of the places a compliance reviewer or an affected customer is most likely to scrutinize closely. It deserves the same design care as any other critical user moment, with enough clarity about why a decision was made and what recourse exists.

What is data governance in this context, and why does it matter for fintechs?

Data governance means documenting where your training and scoring data comes from, how it's maintained, and how it's tested for quality and bias. For a fintech pulling data from a CRM, a transaction database, and possibly a credit bureau feed, that lineage needs to be reproducible and defensible.

Does my choice of CRM affect AI Act compliance?

Yes, indirectly. If your CRM wasn't built with structured data export, lineage tracking, or governance requirements in mind, it can become a bottleneck when you need to demonstrate where scoring data actually came from. This is a factor worth weighing when deciding between an off-the-shelf and a custom CRM.

What is the difference between building this compliance work as custom software versus buying a compliance tool?

A generic compliance tool can help track policy and documentation, but the underlying traceability and oversight mechanisms have to live inside your actual scoring pipeline and product. That typically requires custom software development rather than a bolt-on tool, since the requirements are specific to how your particular system makes decisions.

How long does it typically take to build an AI Act-aligned credit scoring pipeline?

It depends heavily on how much of the existing system already supports structured logging and versioning. An incremental strengthening of an existing pipeline might take a few months; a full rearchitecture for a system with no existing traceability could take considerably longer, which is exactly why starting now with the extended deadline matters.

What happens if a fintech startup misses the December 2027 deadline?

Specific enforcement mechanisms and penalties are set out in the AI Act itself and vary by the nature of the non-compliance; a precise breakdown for every scenario is beyond what we can respond on without legal review. Startups should treat the deadline as firm and plan to be fully compliant with meaningful margin before it.

Should we wait to see if the deadline moves again before starting this work?

No. Regulatory deadlines for complex frameworks have moved before and could move again, but building traceable, well-governed systems is good engineering practice independent of the exact date. Waiting on a hypothetical future extension is a weak basis for delaying work that improves your product either way.

Is this delay specific to the EU, or does it affect fintechs elsewhere too?

The delay described here applies specifically to the EU AI Act's timeline. A European fintech operating across multiple markets should still check whether other jurisdictions it operates in have their own AI governance requirements with different timelines.

What is a conformity assessment, and does my startup need one?

A conformity assessment is a formal evaluation confirming a high-risk AI system meets the required obligations before it can be deployed at scale. Whether your specific system needs one, and what form it takes, depends on the final implementing rules; this is an area to confirm with legal counsel as the deadline approaches.

How does this affect an early-stage fintech that hasn't launched yet?

An early-stage fintech has an advantage here — it can build traceability, oversight, and data governance into the architecture from the very first version of its credit or risk product, rather than retrofitting it onto something already in production. This is meaningfully cheaper than remediation later.

What's the risk of treating this purely as a legal problem rather than an engineering one?

Legal teams can interpret the obligations, but implementing traceable logging, versioned models, and oversight interfaces is engineering work. Treating this as a legal-only exercise typically produces a policy document with no corresponding system behind it, which won't hold up under actual audit.

Does this apply to fraud detection models as well as credit scoring?

Fraud detection sits in a different regulatory bucket depending on exactly how it's used; some fraud-related decisions may not be classified the same way as creditworthiness decisions. It's worth reviewing each AI-assisted decision point in your product individually rather than assuming one classification covers all of them.

What should be logged for every automated credit decision?

At minimum: the specific inputs used, the model version and its training data reference, the score or outcome produced, and any human review or override that followed. This should be structured and queryable, not just captured in unstructured application logs.

How does model versioning work in practice for a credit scoring system?

Every deployed version of a scoring model should have a unique, immutable identifier tied to its training data snapshot and parameters, so that any historical decision can be reproduced or explained using the exact model version that made it, even after the model has since been updated.

What's a reasonable first step for a startup that hasn't started any of this yet?

Start with an audit of your current pipeline: can you trace a decision made last month back to its inputs and model version today? If not, that gap is the priority, and it's a smaller, well-scoped project to close before attempting the full oversight and governance build-out.

Does bias testing need to happen before or after the model is deployed?

Both. Bias testing should happen before deployment as part of validating the model, and then on an ongoing basis after deployment since data drift and changing applicant populations can introduce bias that wasn't present at launch.

How does this affect third-party AI vendors we use for scoring or fraud detection?

If you rely on a third-party model or API for a high-risk decision, you still bear responsibility for ensuring the overall system meets the obligations, which means you need visibility into how that vendor's model makes decisions, not just its output. This is worth raising directly with vendors now.

What does "explainability" mean in a regulatory context versus a technical one?

Technically, explainability might mean feature importance scores from a model. In a regulatory and human-oversight context, it means a reviewer or affected customer can understand, in plain terms, why a particular decision was made — which is a higher bar than a raw technical explanation.

Can we use a general-purpose AI model or LLM in a credit decision system under these rules?

Yes, but the same traceability, oversight, and governance obligations apply regardless of the underlying model architecture. Using a general-purpose model doesn't reduce the requirement to log, version, and explain decisions — it can make it harder, since general-purpose models are often less inherently interpretable.

How should a startup budget for this kind of compliance-driven engineering work?

Budget it as a discrete engineering project with clear scope, not an open-ended ongoing cost. A gap audit is a smaller, well-bounded first step; the full build-out of logging, oversight interfaces, and governance is a larger project that can often be phased across the runway to the new deadline.

What's the relationship between this compliance work and general software quality?

Most of what the AI Act requires for high-risk systems — structured logging, versioning, reproducibility, clear data lineage — overlaps heavily with what good software engineering practice already recommends. Startups that invest here are typically improving system reliability and debuggability at the same time as building toward compliance.

Will investors and banking partners ask about this before December 2027?

Increasingly, yes. Due diligence conversations with banking partners, payment processors, and investors are already including AI governance questions as standard practice, independent of the exact regulatory deadline. Being able to answer clearly ahead of the deadline is a competitive advantage.

What's the biggest mistake a fintech startup can make with this extra runway?

Treating the extended deadline as a reason to deprioritize the work entirely, only to find themselves rushing the same build in the final months before December 2027 under the exact time pressure the delay was meant to relieve.

How does subscription or tiered pricing interact with AI Act obligations?

If access to certain credit features or scoring insights is gated behind a paid tier, and that gating interacts with an automated eligibility decision, the logic behind that gate can become part of what needs to be documented and auditable, since it affects the outcome a customer experiences.

Does a startup need a dedicated compliance hire to handle this?

Not necessarily at first. Many early-stage fintechs handle this through a combination of external legal counsel for interpretation and an engineering team building the technical infrastructure, adding dedicated compliance headcount as the product and regulatory surface area grow.

What's the difference between a risk management system and a compliance checklist?

A risk management system under the AI Act is meant to be an ongoing, living process — continuously identifying and mitigating risks in how the AI system behaves — rather than a one-time checklist completed before launch and left unrevisited.

How often should a credit scoring model be re-evaluated for compliance once it's built?

Regularly, and certainly whenever the model is retrained, the underlying data sources change, or the product surface using the model changes. A model evaluated once at launch and never revisited will likely drift out of alignment with its own documentation over time.

What should the technical documentation for a high-risk AI system actually contain?

Typically a description of the system's purpose and intended use, details of the data used to train and validate it, its performance characteristics, known limitations, and the human oversight measures in place — structured clearly enough that an external reviewer could understand the system without deep internal context.

Can a small fintech startup realistically meet these obligations without a large compliance team?

Yes, if the underlying system is architected with traceability and oversight from early on. The obligations are more achievable when built incrementally into the product than when treated as a massive one-time project attempted right before a deadline.

Does this delay affect only new AI systems, or existing ones already in production too?

The high-risk obligations apply to a system based on its use case and function, not on when it was built. An existing credit-scoring system already in production that falls into the high-risk category is still subject to the same obligations by the new deadline.

What's a practical way to test whether our current system has adequate human oversight?

Have a compliance-minded reviewer, not an engineer, attempt to understand and if necessary override a real automated decision using only the interface currently available. If they can't get enough context to make an informed judgment, the oversight mechanism isn't there yet.

How does this relate to GDPR, which fintechs already have to comply with?

GDPR and the AI Act overlap in places — both care about data provenance, individual rights around automated decisions, and transparency — but they are separate frameworks with separate obligations. Compliance with one does not automatically satisfy the other.

What kind of engineering team is best suited to build this kind of system?

A team experienced in building auditable, well-documented backend systems with strong data governance practices, rather than a team optimized purely for rapid feature shipping. The two skill sets can coexist, but this work rewards deliberate architecture over speed.

Is there a risk in building this compliance infrastructure too early, before final implementing rules are settled?

Some risk exists that specific technical standards could be refined before the deadline, but the core principles — traceability, oversight, governance — are stable enough that building toward them now is a reasonable bet, and far lower risk than not starting at all.

How should a startup prioritize between new feature development and this compliance work?

Treat the compliance-related engineering as part of the core product roadmap for any credit or risk-scoring feature, not as a separate track competing for resources. Traceability and oversight design decisions made during initial feature development are far cheaper than adding them later.

What does "reproducibility" mean for a scoring decision?

It means that, given the same inputs, model version, and configuration, the system produces the same outcome every time, and that historical decisions can be recreated for audit purposes using the exact conditions under which they were originally made.

What's the role of a CTO versus a compliance lead in preparing for this deadline?

The CTO typically owns the technical architecture decisions — logging, versioning, data pipelines — while a compliance lead or external counsel interprets the specific legal obligations and confirms the technical implementation satisfies them. Close collaboration between the two is essential.

How do we know if our current scoring pipeline is a good candidate for incremental improvement versus a full rebuild?

If the pipeline already has some structured logging and versioning but lacks completeness, incremental improvement is usually more efficient. If decisions are effectively untraceable today with no versioning at all, a fuller rearchitecture is often the more honest and ultimately cheaper path.

What's a realistic timeline to start seeing compliance-ready systems in place?

Given the December 2027 deadline, a startup starting now has roughly a year and a quarter of runway, which is enough time for a phased build — audit, then core logging and versioning, then oversight interfaces, then full documentation — if the work begins in earnest soon rather than closer to the deadline.

What should we tell customers about how AI is used in their credit decisions?

Clear, honest disclosure of where AI is involved in a decision, and what recourse exists if a customer wants a decision reviewed by a human, is both good practice and increasingly an explicit expectation under evolving AI governance frameworks.

How does this affect fintech startups that use AI only for internal operations, not customer-facing decisions?

Internal-only AI use, such as an operations team using AI to triage support tickets, generally falls outside the high-risk category tied to credit or eligibility decisions. The obligations discussed here specifically concern systems that affect a customer's access to credit or similar outcomes.

What's the first conversation a fintech founder should have this quarter about this deadline?

A conversation between engineering leadership and whoever owns regulatory strategy about which product surfaces currently make or influence credit decisions, and an honest assessment of how far those systems are from being traceable and auditable today.

Where can we get help assessing our current system against these requirements?

A focused technical audit of your existing scoring or risk pipeline, mapped against the traceability, oversight, and data governance requirements discussed here, is a practical starting point — book a meeting to talk through what that would look like for your specific system.