Karsten Wenzlaff, Advisor
August 26th, 2025

On March 9, 2026, the U.S. Securities and Exchange Commission held its 45th Annual Small Business Forum. The agenda moved from early-stage entrepreneurs to growth companies and smaller public companies. Market participants could propose recommendations and vote on which should be prioritized for the SEC and Congress.
The U.S. has not solved small-business capital formation. That is partly why the process is useful. Questions around finders, investor eligibility, offering rules, fund structures, secondary liquidity and smaller public-company economics keep returning as markets change.
Canada is now opening several parts of its financial economy at the same time. Capital programs, SME financing, payments access, consumer-driven banking and retail private-market initiatives are moving from policy design toward operating tests. The question is no longer whether access exists on paper. It is whether more businesses, investors and challengers can use it economically.
The Forum looks across the financing lifecycle
The SEC brings founders, investors, advisers and intermediaries into one recurring process. Its 2026 Forum program again moved from early-stage financing to growth capital and smaller public markets.
The same frictions keep returning in new forms
Finders, investor eligibility, offering limits, fund structures, secondary liquidity and small-public-company economics remain active issues because one reform can solve one bottleneck while exposing another.
The transferable lesson is not a U.S. securities rule. It is the habit of bringing market participants back into the process and testing whether a framework is producing the market it was intended to create.
Canada already has detailed market evidence
CVCA tracks venture and private equity. NACO tracks angel investing. Regulators and departments publish market studies, consultations and program data. Canada does not lack information about every part of the financing system.
Canada is also actively intervening
The federal government is preparing another C$1 billion venture and growth capital program. The Competition Bureau is studying SME financing. Payments, data access and retail private markets are also being redesigned.
Canada already has consultations, programs and market data. The harder test is whether each reform produces enough real participation to change who can compete, invest and scale.
Institutional venture capital is getting a larger engine
The Venture and Growth Capital Catalyst Initiative is designed to attract more private and institutional capital into Canadian venture funds, strengthen fund managers and support high-growth companies from pre-seed through growth.
SME financing is being tested against a broader business population
The Competition Bureau's SME financing competition, including lender entry, expansion and switching barriers.
Growth VCCI can deepen capital for companies that fit venture mandates. It does not automatically finance every viable manufacturer, service company or local employer whose growth profile, asset base or financing need sits outside institutional venture economics.
Financial data is moving toward regulated access
The proposed Canada's open banking rules bring accreditation, liability, data scope, security and technical standards into one operating framework.
Core payment infrastructure is opening to a wider membership base
PSPs and more credit unions can join Payments Canada, while the Real-Time Rail rules and access are moving toward the planned Q4 2026 launch. Wider eligibility gives PSPs and credit unions a clearer route into core payment infrastructure.
Formal access changes who is allowed to participate. Competition changes only when entrants can absorb compliance, technology, integration and operating costs and still build products customers want.
Fintechs can gain more control over the customer experience
Directer access to data, payments and settlement can reduce dependence on incumbent-controlled infrastructure and give challengers more control over pricing, product design and service delivery.
Smaller financial institutions can compete through shared capabilities
Credit unions and regional firms may not need to build every payments, AI, compliance, data or digital-asset capability internally if specialized providers can deliver those functions at workable scale.
The payoff is not a longer list of fintech entrants. It is more providers controlling enough of their infrastructure and economics to put sustained pressure on incumbents.
Managed access gives households professional selection
Ontario's long-term asset fund work could give retail investors diversified exposure to venture capital, private equity, private debt, infrastructure and other long-duration assets through professionally managed funds.
Direct access gives households the company decision
Equity crowdfunding lets an investor choose an individual company. It can connect businesses with customers, employees and supporters, but it also concentrates risk and usually offers little liquidity.
Managed access can broaden exposure to private-market returns. Direct access can broaden the number of people deciding which companies receive their money. Both can widen participation, but they create different markets.
Canada is building the managed channel for wider retail use
Managed structures can bring diversification, diligence, portfolio construction and product-level controls around valuation and liquidity. They can also preserve professional gatekeeping over where retail capital is deployed.
Canada's direct channel remains comparatively constrained
NI 45-110 allows a Canadian issuer to raise up to C$1.5 million in 12 months. Ordinary investors are generally limited to C$2,500 per offering, or C$10,000 when a registered dealer determines suitability.
If more company value is created while businesses remain private, wider retail access affects more than issuer financing. It influences which households can accept productive risk and participate earlier in private-market returns.
Canadian direct demand can reach the existing ceiling
Blossom, Edison Motors and Gander have used community capital alongside accredited, offering memorandum or other financing. Their raises show direct retail capital can complement professional capital rather than replace it.
International peers provide more room for direct participation
Australia permits eligible issuers to raise A$5 million in 12 months and caps retail investment at A$10,000 per company annually. U.S. Regulation Crowdfunding allows eligible issuers to raise up to US$5 million.
Canada's smaller market does not prove regulation caused weak activity. Issuer quality, investor demand, distribution, awareness, liquidity and platform execution also matter. It does show why market-opening rules should eventually be judged by whether enough issuers, investors and intermediaries can participate economically.
One future produces more viable participants
New payment participants build useful services. Open-banking firms turn permissioned data into products customers adopt. Smaller institutions buy modern capabilities instead of rebuilding them. More businesses find financing that fits their stage and economics.
The other future opens rules without changing market power very much
Accreditation, integration, compliance, distribution and technology remain expensive enough that the largest institutions and professional managers capture most new activity. Formal access widens while competitive intensity changes only at the margin.
The evidence will be practical. Entrants that survive. Products customers use. Capital reaching different kinds of companies. Investors using managed and direct routes. Smaller institutions offering capabilities once reserved for much larger competitors.
Better participation can improve the inputs to productivity
More financing choices, faster settlement, stronger data access and better financial tools can give businesses more capacity to invest, automate, hire, commercialize and serve customers.
Stronger companies can create the next round of participation
Businesses that build revenue, productivity and international reach create more investable opportunities. Successful founders, employees and investors can recycle capital, experience and networks into the next generation.
More viable participants can increase competition. Better competition can improve products, distribution and capital allocation. Better tools and financing can support more investment. Stronger companies can create more opportunities for households and institutions to participate again.
The U.S. process expects the friction to change
Market participants return because new rules, market conditions and business models keep changing the problem. A recommendation can be implemented and still leave a new bottleneck elsewhere.
Canada will need the same feedback discipline across more than capital
As payments, data, private markets and financing become more open, policymakers will need to know who entered, who could not, which businesses became sustainable and where access failed to generate enough economic activity.
Canada has spent years opening doors. The next phase is finding out which openings create viable markets. That means judging regulation and public programs by the participation, competition and productive activity they generate while preserving the protections that made wider access possible.
Participation is not a complete explanation for Canada's productivity problem. Management capability, commercialization, industrial structure, R&D, domestic demand, risk appetite and global scale all matter.
But Canada is now creating new access points across capital, payments, data and investing at the same time. That gives Canada a rare four-year window to see whether productive participation becomes a real growth mechanism rather than a policy slogan.
Talking Point
Canada may already possess much of the capital, technology, talent and institutional capacity needed for stronger growth. The opportunity between now and 2030 is to make more of those assets economically usable by more businesses, investors and financial challengers. If today's reforms create viable participation rather than permission alone, Canada could end the decade with more competition, more investable companies and more ways for households and institutions to share in productive growth.
The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem that provides education, market intelligence, industry stewardship, networking and funding opportunities and services to thousands of community members and works closely with industry, government, partners and affiliates to create a vibrant and innovative fintech and funding industry in Canada. Decentralized and distributed, NCFA is engaged with global stakeholders and helps incubate projects and investment in fintech, alternative finance, crowdfunding, peer-to-peer finance, payments, digital assets and tokens, artificial intelligence, blockchain, cryptocurrency, regtech, and insurtech sectors. Join Canada's Fintech & Funding Community today FREE! Or become a contributing member and get perks. For more information, please visit: www.ncfacanada.org
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Aug 17, 2026

When the first DORA Register of Information submissions arrived in April 2025, European supervisors kept hitting the same wall. Financial entities could not produce an accurate, current list of their own ICT assets. The data sat in spreadsheets, in a departed engineer's notes, and across two or three tools that disagreed with each other. The EBA flagged widespread gaps and sent institutions back to resubmit, in several cases more than once.
None of that was a security failure in the usual sense. The controls were often in place. What was missing sat one level lower: a reliable inventory of what the firm actually runs. For a fintech, that absence is not a documentation nuisance. Operational resilience – keeping payments, ledgers, and customer access working through a disruption – rests on knowing what you run, where it runs, and what stops when a component fails. You cannot map a dependency you never recorded, and you cannot restore a service whose parts you cannot name.
DORA (Regulation (EU) 2022/2554), in force since 17 January 2025, states the requirement plainly. Article 8 obliges financial entities to identify and classify all ICT assets and information assets, document the links and interdependencies between them, and keep those inventories current – refreshed after every major change, with a dedicated risk assessment of legacy systems at least once a year. DORA requires EU member states to lay down effective, proportionate and dissuasive penalties for financial entities. The sanctions that apply depend on national law and on the circumstances of the breach.
The UK sets a parallel bar. Under FCA policy statement PS21/3 and PRA supervisory statement SS1/21, the transitional implementation period ended on 31 March 2025. Firms must identify their important business services, set impact tolerances, and map the resources each service depends on, including technology, data, facilities, and people. That mapping collapses without an accurate asset layer beneath it. In the US, the 2020 interagency paper on operational resilience points the same way, tying resilience to a clear view of critical systems and their dependencies.
Enforcement is tightening rather than loosening. Germany's BaFin declared the DORA “transformation year” over at the end of 2025, a signal that supervisors now expect working inventories, not remediation plans. Three regulators, one shared premise: transparency of IT assets is the precondition for everything built on top of it.

Figure 1
Figure 1. IT asset transparency is the base layer every resilience process depends on.
Transparency is not a spreadsheet exported once a quarter. It is three capabilities working together, and the weakest one sets the ceiling.
Automated hardware and software auditing finds devices, virtual machines, cloud instances, and installed packages without waiting for anyone to complete a form. Fintechs churn infrastructure quickly, so a hand-maintained list is stale within weeks. Agent-based and agent-less scanning each catch what the other misses – agents report from laptops that leave the network, while agent-less scans reach devices where you cannot install software.
A configuration management database (CMDB) records that a specific payment API runs on these servers, reads from that database cluster, and backs a named customer-facing service. During an incident, that relationship graph gives you blast radius in seconds instead of a war-room reconstruction. A flat asset list cannot answer the question that matters: if this fails, what else goes with it?
Every asset needs a criticality rating, a named owner, a lifecycle state, and a link to the business function it supports. That is close to a word-for-word restatement of what DORA Article 8 asks a financial entity to hold, which is why an inventory missing those fields tends to fail at submission time rather than during an outage.
Table 1. What each resilience obligation actually needs from the asset layer.
| Resilience obligation | Asset data it requires | Consequence of a gap |
| DORA Article 8 inventory and classification | Full list of hardware, software, and cloud services with a criticality rating and named owner | Incomplete Register of Information; repeated resubmission cycles |
| Dependency mapping (DORA Art. 8; UK important-business-service mapping) | CMDB relationships tying assets to services, users, and third parties | Cannot scope incident impact or evidence a recovery path |
| Incident response and recovery | Live location, configuration, and ownership for every asset | Longer time-to-restore; recovery steps improvised during the outage |
| Yearly legacy-system risk review | Lifecycle state, end-of-life flags, and patch status | End-of-life systems stay live and unassessed |
| Third-party and concentration risk | Register of vendor-linked assets and their interconnections | Blind to a supplier dependency during a supplier outage |
The failure modes are predictable. Cloud and SaaS growth push assets outside the corporate network, where an on-network scanner never sees them. Shadow IT – a product team standing up a service on a corporate card – never reaches the register at all. Remote and field laptops drop off the VPN and stop reporting, so their patch state quietly goes unknown. And the most common failure is the humblest one: the inventory lives in spreadsheets and email threads that no discovery tool feeds, so it drifts out of date the moment it is saved.
The dataset behind Alloy Software's recent deals shows how entrenched that last pattern is. Across more than 40 closed-won accounts between 2024 and 2026, spreadsheets, email, and homegrown databases were the single most common system teams were replacing – ahead of any named commercial tool.

Figure 2
Figure 2. Prior systems replaced across 40+ Alloy Software closed-won deals (2024–2026).
A workable sequence follows the order of dependency, not the order of visible output:
The order matters. Teams that start with dashboards before discovery end up with attractive reports built on data nobody trusts. Discovery first, relationships second, reporting last.
For a regulated fintech, three questions filter the market quickly. Does discovery reach cloud and off-network devices? Does the CMDB model relationships rather than store a flat list? Can the data stay on-premises where a security policy or air-gapped requirement demands it? Cost matters, but it rarely decides the outcome on its own.
Table 2. Decision view across five ICT asset and service-management platforms.
| Platform | Discovery reach | CMDB and relationships | Hosting | Indicative cost / fit |
| Alloy Navigator | Agent and agent-less network inventory; off-network audit for field laptops | Integrated CMDB; tickets linked to assets, users, and contracts | On-prem or cloud | ~$1k–$25k/yr; 2–35 IT staff |
| ServiceNow | Agent-less discovery via MID server; broad cloud coverage | Deep, highly configurable CMDB | Cloud-first; limited on-prem | Six-figure programmes; 100+ IT staff |
| Lansweeper | Agent and agent-less scanning; strong network coverage | Asset-centric; lighter service relationships | Cloud or on-prem | Per-asset pricing that has risen sharply; small–mid teams |
| ManageEngine ServiceDesk Plus | Agent and agent-less; discovery add-on | CMDB in higher tiers | On-prem or cloud | Low–mid, per-technician/node; small–mid teams |
| Freshservice | Discovery agent plus probe | Cloud-native CMDB | Cloud only | Per-agent SaaS; no on-prem option |
Costs reflect market positioning, not quotes; verify against current vendor pricing before shortlisting.
Where a firm has outgrown spreadsheets but cannot absorb a six-figure ServiceNow programme, mid-market platforms cover the ground. Alloy Navigator sits in that band: agent and agent-less network inventory, an integrated CMDB that links tickets to assets, users, and contracts, and a choice of on-premises or cloud hosting for healthcare, public-sector, and finance environments with strict data-residency rules. Deal data puts its annual cost between roughly $1,000 for small teams and $25,000 for larger estates, which is why it usually appears against Lansweeper and ManageEngine rather than enterprise suites.
An accurate asset register earns its keep only when it feeds the processes around it. Change management is the clearest example: when every change references the assets and services it touches, the CMDB stays current as a by-product of daily work instead of decaying between audits. Incident response reads the same relationship graph to scope impact, and third-party risk mapping – a specific DORA obligation – draws on the register of vendor-linked assets. Teams that want to go deeper on tying assets to change and incident workflows tend to find that the relationship model, not the raw asset count, is where the resilience value sits.
If a fintech can answer three questions on demand – what do we run, what depends on it, and who owns it – most of DORA Article 8 and the UK mapping requirement is already within reach. If it cannot, no volume of policy documentation closes the gap, because the gap is data, not paperwork. Point automated discovery at the whole estate, including cloud and remote endpoints, and measure how far the result differs from the current spreadsheet. That delta is the honest size of the resilience problem.
The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem that provides education, market intelligence, industry stewardship, networking and funding opportunities and services to thousands of community members and works closely with industry, government, partners and affiliates to create a vibrant and innovative fintech and funding industry in Canada. Decentralized and distributed, NCFA is engaged with global stakeholders and helps incubate projects and investment in fintech, alternative finance, crowdfunding, peer-to-peer finance, payments, digital assets and tokens, artificial intelligence, blockchain, cryptocurrency, regtech, and insurtech sectors. Join Canada's Fintech & Funding Community today FREE! Or become a contributing member and get perks. For more information, please visit: www.ncfacanada.org
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Aug 17, 2026

A mid-size alternative lender in Vilnius pulls company registry filings, marketplace pricing and sanctions lists into its underwriting model every night. None of it is illegal to read. Most of it becomes a liability the moment it is copied, stored and combined with something else. That gap between "publicly visible" and "lawfully processed" is where fintech compliance teams keep losing arguments with their own data science departments.
Public web data – company filings, marketplace listings, court dockets, sanctions databases, social media bios – has become a standard input for credit scoring, fraud detection, KYB and competitive pricing in financial services. In Meta Platforms v. Bright Data, a federal district court held that Bright Data had not breached Meta's terms of service by collecting data from logged-out pages, which was the specific conduct at issue. The ruling turned on Bright Data's particular conduct and its contractual relationship with Meta rather than establishing a general rule for scraping public websites. For a regulated entity, that distinction is not academic. A bank's third-party risk team, an EU DORA auditor or a state attorney general does not care whether the data was "technically public" if the collection method itself created exposure.
hiQ Labs v. LinkedIn is still the reference case for US practitioners, and it is more nuanced than the headlines from 2019 suggest. The Ninth Circuit held twice, first in 2019 and again on remand in 2022, that scraping data from pages open to any visitor does not amount to accessing a computer "without authorization" under the Computer Fraud and Abuse Act. That took the CFAA off the table as a criminal exposure for reading public pages. It did not end the case. hiQ and LinkedIn settled the remaining contract claims in 2022, and hiQ agreed to destroy the data it had already collected and pay damages, because its scraping still violated LinkedIn's user agreement. The lesson for a fintech legal team is specific: CFAA risk and contract risk are two separate questions, and winning on one does not close the other.
On the EU side, the CFAA question barely matters, because GDPR does not distinguish between public and private personal data. Article 4 defines personal data by whether it relates to an identifiable natural person, not by where it was found. A LinkedIn bio, a court filing with a defendant's name, or a marketplace seller profile with a real name attached all fall inside GDPR's scope the moment they are collected, and Article 6 still requires a lawful basis – legitimate interest is workable for adverse-media or fraud screening, but it requires a documented balancing test, not just a note in a Confluence page.
Four use cases account for most of the public-data traffic coming out of fintech data engineering teams. Alternative underwriting pulls e-commerce store metrics, invoice marketplaces and gig-platform ratings to score borrowers who lack conventional credit files – Kabbage and, later, Amex built entire product lines on this. AML and sanctions screening cross-references OFAC, EU and UN lists against onboarding data, refreshed daily because list updates are unscheduled. Competitive pricing intelligence in embedded finance and BNPL tracks merchant-facing rates across marketplaces to benchmark interchange and fee structures. Fraud and adverse-media screening checks court records, press mentions and social profiles as a secondary signal alongside device fingerprinting.
Not all four carry the same regulatory weight. The table below is the one compliance teams actually need before greenlighting a collection project, not a generic "data source" taxonomy.
| Data source | Typical fintech use | Regulatory sensitivity | Main legal basis to check |
| Company registries (Companies House, EDGAR, EU BRIS) | KYB, beneficial ownership checks | Low to medium | Public register rules and applicable data protection law; filings may contain personal data of directors, officers, beneficial owners and other natural persons |
| Sanctions and PEP lists (OFAC, EU, UN) | AML/KYC screening | Low | Government-published, but update frequency and source authenticity matter |
| E-commerce and marketplace pricing | Competitive intelligence, embedded-finance pricing models | Low to medium | Terms of service and contract law; CFAA exposure may be lower for pages accessible without login (per hiQ v. LinkedIn) |
| Public social media profiles | Alternative credit signals, fraud indicators | Medium to high | GDPR/CCPA personal-data rules apply even if the profile is public |
| Court records and litigation databases | Adverse media, fraud investigation | High | Jurisdiction-specific rules on re-use of judicial data (varies widely, e.g. France's Article 33) |
The engineering choices matter as much as the legal analysis, because a regulator or a bank's third-party risk assessor will ask for logs, not intentions. A defensible pipeline has five properties, and they map to concrete infrastructure decisions rather than policy statements.

Figure 1
Figure 1. Each control maps to an artifact a third-party risk assessor can actually inspect. The first four are described below; request logging is the fifth, and the one the practical takeaway returns to.
Reading a site's robots.txt crawl-delay directive and setting concurrency accordingly is a five-minute engineering task that changes the legal character of the whole program. A crawler hitting a company registry at 200 requests per second looks like a denial-of-service test to the target's security team, regardless of what the data is used for afterward. Most production fintech scrapers we've reviewed cap at 1 request per 2-4 seconds per domain, which keeps CPU load on the target negligible and avoids the WAF triggers that generate abuse complaints in the first place.
This is the point where proxy infrastructure choice stops being a procurement decision and starts being a compliance decision. Rotating through residential or datacenter IPs to maintain a stable success rate against rate limits is standard engineering practice. Rotating IPs specifically to re-access a source after being blocked for a terms-of-service violation is the fact pattern that turned hiQ's win on CFAA into a loss on contract claims. The distinction sounds semantic until an opposing counsel reconstructs your request logs during discovery.
Filtering personal identifiers (names, emails, phone numbers, biometric-adjacent fields) before the data lands in a warehouse is materially cheaper than filtering it after ten analysts have already queried the raw table. A regex-and-NER pass at the collection layer, logged with a timestamp and a rule version, is the artifact a DPO can actually show an auditor.
GDPR's storage limitation principle (Article 5(1)(e)) and most US state privacy laws expect a defined retention period. "We keep everything indefinitely for model retraining" is the single most common finding in the DPIAs we've read for alt-data underwriting programs, and it is usually fixable with a 90-180 day rolling window plus a documented exception process for flagged accounts.
Proxy and scraping infrastructure choice affects three things a compliance file will ask about: whether the vendor itself runs KYC on IP sourcing, whether the billing model matches your actual usage pattern (per-IP monthly vs. per-GB bandwidth), and whether the vendor's own terms indicate the network is ethically sourced rather than built from compromised devices.
| Provider | Billing model | Entry price | Where it fits a fintech workload |
| Proxys.io | Per dedicated IP / month | From $1.40/mo (individual IPv4), $0.13/mo (IPv6) | Steady, low-volume monitoring jobs (registry checks, sanctions list refresh) where a fixed, auditable IP per data feed is easier to log than rotating bandwidth pools |
| Decodo (formerly Smartproxy) | Per GB, tiered | $2.00-$3.75/GB depending on volume | Mid-volume scraping across many source domains where bandwidth, not IP count, is the cost driver |
| Oxylabs | Per GB, sales-assisted | Roughly $8/GB at entry tier, KYC required before provisioning | Enterprises that want a vendor-side KYC record as part of their own third-party risk file |
| Bright Data | Per GB (PAYG or committed) | $8.40/GB PAYG residential, down to ~$3/GB committed; datacenter from ~$0.90/GB | Large, multi-region collection programs where volume discounts offset the higher entry rate |
The billing model split matters more than the headline price. A sanctions-list refresh job that hits the same twelve government sources every night at a predictable, low volume is a poor fit for per-GB bandwidth pricing – you're paying for a metric (data transferred) that has almost nothing to do with your actual constraint, which is IP reputation and consistency of access over time. Vendors like Proxys.io bill per dedicated IP per month, which lines up better with that access pattern and makes cost forecasting for a fixed set of monitored sources straightforward. A marketplace-pricing crawl that touches thousands of product pages across dozens of domains is the opposite case: bandwidth is the real cost driver, and a per-GB model from Decodo, Oxylabs or Bright Data scales more predictably with that workload. Enterprises already running Oxylabs' or Bright Data's own KYC process may lean on that as one input to their own vendor risk assessment, though it doesn't substitute for one.

Figure 2
Figure 2. The two variables that move cost are how many domains a run touches and how much data it moves, not the headline price per unit. Per-IP and per-GB rates are quoted in different units and cannot be compared directly.
None of these vendors, including the ones with published ethics or KYC pages, remove the fintech's own obligation to define a lawful basis, log what was collected, and honor retention limits. The proxy layer solves an availability and reliability problem – consistent access to public pages without disproportionate load on the source – not a data protection problem.
Three signals usually mean a proxy or scraping setup needs to change, independent of price. First, a rising block rate on sources with unchanged rate limits – that's an IP-reputation problem the vendor's pool has accumulated, not something a compliance policy fixes. Second, the compliance team asking for source-level access logs the engineering stack can't currently produce – that's a signal the collection layer needs structured logging before it needs a new vendor. Third, a shift in workload shape, for example moving from a handful of steady, low-volume registry checks to broad multi-domain marketplace crawling, which usually means the per-IP pricing that worked for the first case stops making sense for the second.
A fintech data program built on public web sources holds up under regulatory review when three things are documented before the first request is ever sent: the lawful basis for each data category (not a blanket justification), the technical controls that keep collection proportionate to the source (rate limits, minimization, retention), and a request log detailed enough to reconstruct what was collected and why if a regulator or a counterparty's third-party risk team asks. The infrastructure vendor is a smaller decision than most procurement processes treat it as – it changes reliability and cost, not the underlying legal analysis.
The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem that provides education, market intelligence, industry stewardship, networking and funding opportunities and services to thousands of community members and works closely with industry, government, partners and affiliates to create a vibrant and innovative fintech and funding industry in Canada. Decentralized and distributed, NCFA is engaged with global stakeholders and helps incubate projects and investment in fintech, alternative finance, crowdfunding, peer-to-peer finance, payments, digital assets and tokens, artificial intelligence, blockchain, cryptocurrency, regtech, and insurtech sectors. Join Canada's Fintech & Funding Community today FREE! Or become a contributing member and get perks. For more information, please visit: www.ncfacanada.org
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Aug 13, 2026

Artificial intelligence (AI) is rapidly becoming part of the day-to-day operations of Canadian financial advice firms. From automating administrative tasks and analyzing client portfolios to supporting investment research and improving client communications, AI has the potential to make advisors more efficient and help firms deliver a better client experience.
But as adoption accelerates, governance isn't always keeping pace.
Many firms are experimenting with AI tools before establishing clear policies around how those tools should be used, monitored, and supervised. That creates significant risk in an industry where investment advice is built on trust, accountability, and regulatory compliance.
Using AI without proper governance is a bit like prescribing medication without understanding the side effects or drug interactions. The technology may offer benefits, but without safeguards, oversight, and a clear understanding of the risks, unintended consequences can quickly outweigh the advantages.
For Canadian financial advisors, governance shouldn't be viewed as unnecessary bureaucracy. It's an essential part of responsible innovation.
Canada's financial regulatory environment already places significant responsibilities on advisors, and those obligations don't disappear simply because AI enters the picture. The Canadian Investment Regulatory Organization (CIRO), together with provincial securities regulators such as the Ontario Securities Commission (OSC) and the Canadian Securities Administrators (CSA), have made it clear that existing regulatory obligations continue to apply whenever technology influences regulated activities. Firms remain responsible for ensuring investor protection, fair dealing, appropriate supervision, cybersecurity, privacy, and sound governance, regardless of whether decisions are supported by artificial intelligence.
AI governance is no longer simply a future consideration. CIRO's 2026 Compliance Report identifies artificial intelligence and emerging technologies as areas of supervisory focus, signalling that firms should expect regulators to examine how AI systems are being used, what controls are in place, and whether appropriate oversight exists. The message is clear: firms remain accountable for the outcomes produced by the technology they choose to implement.
At its core, Canadian financial advisors continue to operate under well-established regulatory obligations. For most registered firms, this includes complying with Know Your Client (KYC), Know Your Product (KYP), and suitability requirements under the Client Focused Reforms. In certain advisory relationships, such as discretionary portfolio management, a fiduciary duty may also apply. Regardless of the business model, advisors are expected to understand the rationale behind every recommendation they provide and be able to explain why it is appropriate for each client. That expectation becomes much more challenging if an AI system produces recommendations that advisors cannot clearly explain, let alone defend or stress test.
Explainability is only one piece of the governance puzzle. Firms must also consider data privacy, cybersecurity, recordkeeping, model bias, third-party vendor oversight, and ongoing monitoring of AI systems. Regulators expect firms to demonstrate not only that technology delivers operational benefits, but also that associated risks are identified, documented, and actively managed.
History provides plenty of reasons for this scrutiny. AI systems used in other industries, such as HR, have produced biased hiring decisions, inaccurate healthcare recommendations, and flawed credit assessments due to inadequate oversight or unintended algorithmic behaviour. Financial advice firms cannot assume similar issues won't emerge within investment or wealth management applications.
Another emerging consideration is AI-generated investment commentary. Recent guidance from the CSA and CIRO reinforces that securities laws apply regardless of how investment recommendations are delivered. Whether commentary comes from a financial advisor, an online platform, or an AI-powered tool, firms remain responsible for ensuring communications comply with applicable registration, disclosure, and investor protection requirements. AI cannot be used to distance a firm from its regulatory responsibilities; introducing it does not reduce those responsibilities. If anything, it increases the need for governance.
Strong AI governance starts long before a new tool is deployed. Rather than allowing employees to independently adopt AI solutions across different departments, firms should first define exactly where AI will be used and where human expertise must remain central to the decision-making process. Administrative automation, document summarization, workflow management, and research support may represent lower-risk applications than suitability assessments, portfolio recommendations, or investment decisions that directly affect clients. Establishing clear use cases helps prevent AI from gradually expanding into areas where the risks may outweigh the benefits.
Governance also requires clear accountability. Every AI-enabled process should have an identified owner who is responsible for monitoring performance, addressing concerns, and escalating issues when necessary. Responsibility cannot rest with the software itself. Human accountability remains essential.
Transparency should be another guiding principle. Clients deserve to understand when AI contributes to services they receive, particularly if it influences recommendations, communications, or financial planning outputs. Transparency builds trust while helping clients better understand how technology supports, rather than replaces, professional judgment.
Bias testing is equally important because AI models learn from historical data, which can contain unintended biases. If left unchecked, algorithms may produce outcomes that disadvantage certain investor groups or reinforce patterns that conflict with principles of fairness and equal access. Regular testing allows firms to identify and correct these issues before they affect clients. The objective isn't simply to deploy AI; it's to deploy AI responsibly.
Creating governance policies is only the first step. Maintaining them requires ongoing operational discipline. There are some daily practices that could help firms in this aspect:
Proper documentation: Every meaningful AI-assisted recommendation or decision should be properly documented. Firms should be able to demonstrate how information was generated, how it was reviewed, and how the final recommendation was reached. Comprehensive documentation not only supports internal quality control but also prepares firms for future regulatory reviews.
Continuous monitoring: AI systems are not static. Performance can change over time as market conditions evolve, new data becomes available, or models begin exhibiting algorithmic drift. Regular reviews help ensure systems continue operating as intended while identifying unexpected behaviours before they become larger problems. Many firms may benefit from conducting quarterly governance reviews that assess model performance, review exceptions, evaluate client outcomes, and confirm compliance with internal policies.
Employee education: This should also remain a priority. Advisors need to understand both the strengths and limitations of AI. Training should focus not only on how to use new tools but also on recognizing situations where human judgment should override automated recommendations.
AI should not be treated as a set-and-go replacement for professional expertise. It should be used responsibly as a tool that enhances decision-making and quality investment advice while preserving the experience, judgment, and accountability that clients expect from trusted financial advisors.
AI will undoubtedly reshape financial advice in Canada, but technology alone won't determine which firms succeed. Governance will. Organizations should establish clear policies, maintain transparency, monitor performance, and preserve meaningful human oversight while using AI. Without adequate governance, firms may expose themselves to compliance failures, reputational damage, and increased regulatory scrutiny.
As AI capabilities continue to expand, firms should regularly ask themselves one important question: Could we clearly explain every AI-assisted recommendation to a client and, if necessary, to a regulator? If the answer is yes, governance is likely supporting innovation. If the answer is no, governance deserves attention before AI adoption moves any further.
Ultimately, responsible AI is not a roadblock to the adoption of innovation. It's about ensuring innovation strengthens the quality, integrity, and trust that define professional financial advice.
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Nadeem Kassam, Marnoa Private Wealth Counsel
Nadeem Kassam, CFA®, MBA
Chief Investment Strategist, Chief Operating Officer & Portfolio Manager at Marnoa Private Wealth Counsel
Nadeem is a Chief Investment Strategist and Portfolio Manager with 20+ years' experience across major global banks, including senior-level roles at RBC, Raymond James, CIBC, Deutsche Bank, and Citigroup. At Marnoa, he leads investment strategy and portfolio management with a focus on North American equities and is a frequent commentator in the media, including regular appearances on BNN Bloomberg.
The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem that provides education, market intelligence, industry stewardship, networking and funding opportunities and services to thousands of community members and works closely with industry, government, partners and affiliates to create a vibrant and innovative fintech and funding industry in Canada. Decentralized and distributed, NCFA is engaged with global stakeholders and helps incubate projects and investment in fintech, alternative finance, crowdfunding, peer-to-peer finance, payments, digital assets and tokens, artificial intelligence, blockchain, cryptocurrency, regtech, and insurtech sectors. Join Canada's Fintech & Funding Community today FREE! Or become a contributing member and get perks. For more information, please visit: www.ncfacanada.org
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August 13, 2026 | NCFA Resource | Risk Compliance And Regtech, Artificial Intelligence And Data, Regulation And Policy

On August 6, 2026, the UK Financial Conduct Authority launched the FCA Handbook API, giving firms, developers and RegTech providers direct access to structured Handbook data. The free service lets software retrieve current rules, guidance, technical standards and glossary content for use inside compliance and regulatory change systems.
The practical value is real. Firms no longer have to rely only on website searches, monthly downloads or manually maintained rule libraries when they want FCA source material inside their own systems. The API creates a direct route from the Handbook into software that tracks obligations, maps rules to business activities or supports AI assisted compliance work.
The API provides structured access to the FCA Handbook, Technical Standards and Glossary. Users need a free Handbook account, and the FCA says the data can be used in firms’ own applications or through third party technology providers.
The FCA identifies several practical uses:
AI can help retrieve, classify and compare regulatory information, but the quality of the output still depends on the source material it receives. A direct FCA data feed reduces one common problem which is compliance tools working from copied, stale or inconsistently maintained rule text.
NCFA has already identified this problem in AI powered regulatory reporting. The opportunity isn't simply to add AI to compliance work. Systems need reliable regulatory inputs, clear controls and a way to trace outputs back to the underlying rule or guidance.
The API can also reduce manual work around regulatory updates. Firms can connect Handbook content to internal rule inventories, product governance, control libraries or change management processes rather than repeatedly checking individual pages for updates.
There are some practical access conditions. Users cannot work with the API directly through the Handbook website. They need a compatible external application such as Postman or RapidAPI, or another system built to use the interface. Protected endpoints are also subject to rate limits.
The clearest users are compliance teams, legal teams, RegTech providers, financial institutions and fintechs that need FCA rules inside operational systems.
Large firms with internal technology teams can connect the data to their own compliance architecture and tailor how Handbook content is matched to business lines, products or controls.
Smaller firms may get more value indirectly through RegTech providers that use the API to improve rule monitoring, change alerts, obligation management or policy tools.
Developers and AI teams also gain a cleaner source for regulated workflows. For example, a compliance assistant could retrieve relevant Handbook content, compare current and future text, or help staff identify which internal policies may need review after a rule update.
That doesn't make the API a compliance decision engine. A system can retrieve the rule accurately and still reach a poor conclusion about how it applies to a particular firm, product or client situation. Human review, legal interpretation and internal accountability remain necessary.
The main strength is source quality. The API automatically draws from the latest Handbook rather than requiring firms or vendors to maintain their own copy of the rulebook. That can improve consistency and reduce the delay between a Handbook update and its appearance inside a compliance system.
It is also useful that the FCA has made the service available without a separate licence fee. Firms can choose whether to connect directly or use a technology provider, which lowers the barrier for developers and RegTech companies testing new compliance tools.
The API is not a complete regulatory archive. It does not provide historic Handbook versions. Requests for past dates return an error, although current and future versions are available through the API. Firms that need a full historical record will still need the Handbook website, archive tools or their own retained records.
The API also does not cover every piece of FCA information. The FCA Handbook contains rules, guidance and standards, while other FCA publications, supervisory communications, consultations, speeches and notices remain outside that core source. Compliance systems therefore still need broader regulatory monitoring.
Direct access to current regulatory text improves the input, but it does not guarantee accurate interpretation. Firms using AI for compliance should still test outputs, keep records, control permissions and make it clear when a person needs to review the result. The IOSCO AI Supervisory Toolkit provides useful additional guidance on governance, oversight, data quality and control expectations for AI in regulated financial environments.
The FCA Handbook API is most useful when treated as authoritative source infrastructure. It can make regulatory information easier for software to retrieve and keep current, while firms remain responsible for deciding what the rules mean for their own operations.
FCA Handbook API Launch (use cases for compliance, RegTech and AI)
FCA Handbook API FAQ (access, current data, limits and usage requirements)
FCA Handbook API (API access and developer entry point)
FCA Handbook (current rules, guidance and technical standards)
AI Powered Regulatory Reporting (regulatory data, automation and AI opportunity)
IOSCO AI Supervisory Toolkit For Capital Markets (AI governance, controls and oversight)
The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem that provides education, market intelligence, industry stewardship, networking and funding opportunities and services to thousands of community members and works closely with industry, government, partners and affiliates to create a vibrant and innovative fintech and funding industry in Canada. Decentralized and distributed, NCFA is engaged with global stakeholders and helps incubate projects and investment in fintech, alternative finance, crowdfunding, peer-to-peer finance, payments, digital assets and tokens, artificial intelligence, blockchain, cryptocurrency, regtech, and insurtech sectors. Join Canada's Fintech & Funding Community today FREE! Or become a contributing member and get perks. For more information, please visit: [www.ncfacanada.org](http://www.ncfacanada.org)
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