Karsten Wenzlaff, Advisor
August 26th, 2025
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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August 13, 2026 | NCFA Insight | Artificial Intelligence And Data, Competition And Market Structure, Public Sector Policy And Industrial Strategy

On August 10, 2026, Meta published The Future Is For Everyone, Mark Zuckerberg's wide sweeping proposal for how superintelligence should fit into society.
The central idea is personal empowerment. Zuckerberg argues that advanced AI should give individuals more ability to create, learn, build businesses, improve their health and pursue their own goals rather than placing most of that intelligence under the control of governments, large institutions or a handful of AI companies.
Meta's vision imagines personal agents working continuously on a user's behalf, small teams building companies that once required much larger organizations, personalized tutors, faster scientific discovery and powerful creative tools available to billions of people.
Meta wants AI capability spread widely, while the compute, models, release decisions and government relationships needed to provide it remain concentrated among a handful of organizations.
Mark Zuckerberg, Founder and CEO, Meta:
“The defining questions of our age are who will have access to superintelligence and what will we direct it towards.”
One of Zuckerberg's strongest economic arguments is that AI's biggest contribution could come from helping people invent things rather than simply automating today's jobs.
Meta expects individuals to become capable of doing work that currently requires larger teams, more capital or specialized expertise. Zuckerberg predicts more small businesses, more experimentation and potentially more employment as people use AI to create products, services and jobs that don't exist today.
That is a different vision from a future where AI mainly replaces knowledge work. Meta argues that if personal agents increase people's capabilities quickly enough, workers can adapt and new demand can grow alongside automation.
For founders, that could change the economics of starting a company. Product development, research, design, marketing and operations could require fewer people and less initial capital. Small firms could reach meaningful scale much earlier.
Financial services will feel the same pressure. Meta already has AI that can plan work, connect with email and calendars and continue tasks after the user leaves. As agents gain access to financial information and connected services, permissions and accountability become part of the operating model, especially when an agent can act rather than simply advise.
The more unusual part of Zuckerberg's argument is about safety.
He rejects the idea that one centrally controlled superintelligence can be aligned to a single set of values that works for everyone. People disagree about politics, economics, culture and what makes a good life.
Meta's answer is to distribute powerful AI widely enough that people, businesses, governments and competing AI systems check one another.
It is essentially a balance of power argument. One person with vastly better legal, financial or cybersecurity intelligence could gain an enormous advantage. If many people have access to comparable capabilities, Meta argues that power becomes harder to monopolize. (There’s some irony here. Zuckerberg built his fortune by controlling access to data, distribution and network effects that others couldn’t easily replicate.)
That philosophy also influences Meta's approach to alignment. Personal agents should primarily help users pursue their own goals within legal and safety boundaries rather than enforce one company's view of what those goals should be.
Meta says it plans to build a private mode where even Meta can't access a user's information, and it intends to resume releasing some open models. It is also giving its independent board authority to approve safety criteria for model releases rather than leaving those decisions entirely with Zuckerberg or management.
Meta's existing algorithmic products are already under legal scrutiny, including a federal trial involving 29 U.S. states over alleged harm to children. Meta denies the allegations. A company asking people to trust far more capable personal agents will have to show that user empowerment, privacy and safety work in practice. Algorithmic accountability is already moving into the courts as AI and automated systems take on a larger role in people's lives.
Zuckerberg's decentralization argument has limits.
He wants individuals to have broad access to powerful AI, but he also argues that the United States and its allies should retain leadership in advanced models, silicon and infrastructure. Meta supports continued restrictions on exports of leading chips to geopolitical rivals and wants U.S. policy to make it easier to build data centres and energy capacity.
He also proposes closer cooperation between frontier AI labs and government. Rather than waiting until an advanced model is finished, Meta wants labs to share intermediate model checkpoints and technical staff so governments can identify cybersecurity and other security risks earlier.
The result still leaves considerable power with governments, frontier labs and the companies that control advanced compute. Individuals would gain far more capability. Governments would receive earlier access for security purposes. Independent boards would get more authority over release standards. Frontier labs would still control development of the most capable models.
Meta's vision is therefore decentralized at the user level while retaining substantial institutional coordination at the frontier.
Meta expects capital spending of US$130 billion to US$145 billion in 2026 and spent US$31.08 billion in the second quarter alone. It is investing in models, data centres, energy, networking, its own chips and outside accelerators while trying to deliver AI across products already used by billions of people.
If personal superintelligence is going to be free or affordable at global scale, someone still has to pay for the compute..
Meta wants superintelligence broadly distributed, but scarce compute still has to be allocated. Its answer is a dynamic auction for additional capacity, which means the vision of AI for everyone could still produce tiers of access based partly on what users can afford. (conflict?)
The business model hasn't been proven. Meta's second quarter free cash flow fell to US$784 million as infrastructure spending accelerated, even while its core advertising business remained highly profitable.
Meta is making these commitments under real pressure. Its infrastructure spending has climbed rapidly, the company is still building the compute capacity and custom chips needed to compete at the frontier, and its existing platforms face growing legal scrutiny.
The scale of the investment also reinforces a central tension in Zuckerberg's vision. Meta wants personal AI to give individuals more power, but only a small number of companies can currently finance the systems needed to provide it.
Meta's vision has clear upside for Canada.
Canadian entrepreneurs, researchers and smaller businesses could gain access to capabilities they would never be able to finance themselves. If AI lowers the cost of creating companies, learning new skills and developing new products, a smaller economy can participate without matching U.S. frontier model spending dollar for dollar.
Canada is already debating how to keep more domestic intellectual property, capital and compute capacity while using global AI platforms. The country's AI sovereignty debate is partly about preserving enough domestic capability to avoid becoming only a customer of technology developed and controlled elsewhere.
A recent pro-human AI initiative backed by researchers, business and labour groups also argues for human agency, limits on concentrated power and accountability for AI companies. Zuckerberg reaches some similar principles from a very different starting point.
Canada needs enough choice, competition, data control and domestic capability for its companies and citizens to use increasingly powerful AI on their own terms.
Zuckerberg's bet is that superintelligence can give individuals more power to learn, invent, work and build. Meta has the reach and financial capacity to put that idea in front of billions of people. The cost of doing so is already putting heavy pressure on cash flow.Whether users ultimately gain more control will depend on who controls the models, data, compute and rules behind their personal AI.
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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August 12, 2026 | NCFA Market Activity | Open Banking Open Finance And Data Sharing, Payments Infrastructure And Money Movement, Cross Border Payments And FX

On August 12, 2026, Boston-headquartered U.S. payments company Flywire expanded its partnership with Stockholm-headquartered Swedish Pay by Bank provider Trustly to offer Pay by Bank payments in Canada and the United States.
The Canadian launch is notable because two foreign payment companies are commercializing a Pay by Bank experience here before Canada has a fully operational regulated consumer-driven banking system with payment initiation. Flywire and Trustly are improving how an existing bank payment is authenticated and started rather than waiting for Canada's future open banking payment rails.
Flywire's Canadian payment instructions describe a simple flow. The payer selects Online Bank Transfer in CAD, a Trustly window opens, and the payer signs into their bank. Payment details are filled in automatically and the bank can request additional verification before the payment is authorized.
Flywire says the money is normally debited within 24 hours and delivered to the institution in about five business days. Only personal bank accounts are currently supported. Business and corporate accounts aren't.
The timing tells us the product isn't an instant payment and it isn't using Canada's Real Time Rail. Flywire's own developer documentation identifies its Canadian EFT authorization as a pre-authorized debit agreement (PAD).
Trustly adds the digital layer around that debit, using bank authentication and account data to make the payment easier to initiate inside Flywire.
Alex Gonthier, Founder and CEO, Trustly:
“Pay by Bank is a term that is too general. Generic essentially. What we really mean is Open Banking-based or -enabled payments over bank rails.”
That description fits the Canadian launch particularly well. The bank rail isn't new. The authentication, data and payment experience around it are what Trustly changes.
Flywire specializes in payments where the amount, currency or accounts receivable process can be more complicated than an ordinary checkout. It currently supports more than 5,100 clients across education, healthcare, travel and B2B markets, with payments available in more than 140 currencies across 240 countries and territories.
Its platform processed US$11.4 billion in payment volume during the first quarter of 2026 alone.
Canadian payment options work differently. Flywire lets customers use Interac e-Transfer for payments up to C$3,000, and those payments can arrive quickly. Online bill payment can handle larger amounts, but the payer has to leave Flywire, find Flywire as a biller in online banking and complete the payment there.
Trustly keeps more of that process inside Flywire. The payer signs into their bank and approves the payment without having to set up a biller or copy payment details.
That can be useful for larger payments such as tuition. Flywire hasn't published one Canada-wide Trustly limit, so the amount a payer can send depends partly on the limits set by their bank.
The terminology needs care because Trustly calls the product an open banking payment while Canada's regulated consumer-driven banking framework is still being implemented.
The federal framework is designed to replace credential sharing with regulated API-based financial data access. Payment initiation, often called write access, belongs to a later phase. As of August 2026, the government is still working on the implementation rules for Canada’s consumer-driven banking framework.
Flywire's Canadian Trustly instructions, by contrast, currently ask the payer to sign into online banking through the Trustly experience. Public documentation doesn't establish that every participating Canadian financial institution is connected through a direct API.
It shows that commercial Pay by Bank services can develop before Canada's regulated framework reaches payment initiation.
The longer-term opportunity gets more interesting when the payment experience and settlement infrastructure improve together. Canada's financial infrastructure is opening to new participants, while the Real Time Rail is intended to bring instant, data-rich clearing and settlement. Flywire and Trustly are showing what fintechs can build above the infrastructure available today.
Flywire and Trustly aren't waiting for Canadian open banking payments to become fully operational. They're bringing an international Pay by Bank model into Canada by adding bank authentication and a cleaner payment experience to PAD infrastructure that already works. For Canadian fintechs, that shows an example of how much product innovation can happen above the payment rail before the rail itself changes.
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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