Karsten Wenzlaff, Advisor
August 26th, 2025
August 10, 2026 | NCFA Insight | Regulation And Policy, Competition And Market Structure, Public Sector Policy And Industrial Strategy

On August 10, 2026, ClearScore, Modulr, Teya, Urban Jungle and Zilch became the first firms regulated solely by the UK's Financial Conduct Authority to join its Scale-up Unit. These aren't startups testing whether a product works. They already operate across credit, payments, SME finance and insurance, and several are adding products, distribution channels or new markets. The FCA wants closer contact during that stage of growth, when regulatory questions and operational complexity can multiply quickly.
The Scale-up Unit gives participating firms a dedicated regulatory contact. It can help:
The FCA also wants feedback when its own policies or supervisory processes create unintended barriers to growth or competition. That makes the programme a two-way channel where companies get earlier regulatory access, while the FCA gets evidence from companies dealing with expansion in real time.
The unit sits alongside Innovation Pathways, the Pre-Application Support Service and Early and High Growth Oversight, giving the FCA several points of contact from early product development through commercial scale. Its innovation services have supported more than 1,000 firms.
The FCA's 2026 innovation framework already connects sandboxes, regulatory guidance, AI testing and growth support.
The Scale-up Unit fills more of the space after authorization, when successful firms start becoming harder to supervise and harder to change.
ClearScore reported £144.7 million in 2025 revenue, up 37%, and more than 25 million users globally. Acquisitions of Aro Finance and Acre Platforms have taken it further into embedded finance, mortgages and financial product distribution, while AI is becoming more central to the platform. That means more than scaling a credit-score app. ClearScore now has to manage credit broking, mortgages, customer data, embedded distribution and AI across a larger financial marketplace.
Modulr processes more than 200 million transactions and over £180 billion in annualised payment value for more than 6,000 businesses. Its infrastructure supports payroll, supplier payments, collections and other finance operations where outages or control failures can affect many customers at once. Its scale problem is therefore operational as much as commercial. More volume and wider market reach increase the importance of resilience, financial crime controls and oversight of critical payment infrastructure.
Teya's 2026 product release extends well beyond card acceptance. Its offering now brings together payments, a business account, team cards, e-commerce tools, savings features and Teya AI. One merchant relationship can therefore span payments, cash management, spending and business data. That creates more regulatory dependencies inside a product experience designed to feel simple to the customer.
Urban Jungle says it has helped more than 300,000 UK customers and now distributes insurance both directly and through white-label partners. Its August partnership with IKEA puts Urban Jungle-powered home insurance into a much larger retail customer journey. That tests whether product design, pricing, claims and customer outcomes remain consistent when insurance is distributed through another brand rather than only through Urban Jungle's own channels.
Zilch passed 5.5 million registered customers and $200 million in annual revenue before agreeing to acquire Lithuania's Fjord Bank. Subject to regulatory approval, the deal would give Zilch a European banking licence and a base for expansion across the region. Zilch is now bringing consumer credit, payments, AI and cross-border banking into the same business. That is a very different regulatory footprint from the one it had when it entered the FCA's Regulatory Sandbox earlier in its development.
The Scale-up Unit arrives with evidence from the FCA's own supervision. Between July 2025 and March 2026, its Early and High Growth Oversight pilot worked with 15 firms across asset management, wealth management and payments to see whether governance, risk management and controls were keeping pace with growth.
The FCA found that stronger firms invested early in boards, risk management, compliance resources and management information. Weaker examples included governance that had fallen behind business growth, too much responsibility concentrated in a few people and insufficient independent challenge.
The reality is commercial growth can arrive faster than the systems needed to govern it. The regulator's answer is earlier engagement rather than waiting for those gaps to surface through an application, incident or supervisory problem.
The FCA isn't promising easier rules. Participation doesn't lower regulatory standards, guarantee approvals or amount to an endorsement of the firms involved.
What it is offering is earlier access to the regulator when a company is changing quickly. That could help commercially if firms can resolve regulatory questions before they delay product launches, permissions or expansion plans.
It also gives policymakers a closer view of where regulation itself creates unnecessary friction. That question is already live in Canada. Canada's competition and growth debate has increasingly focused on faster approvals, proportionate oversight and whether regulation helps challengers reach scale rather than protecting established market structures.
ClearScore, Modulr, Teya, Urban Jungle and Zilch will provide the first real test of whether that approach can deliver both: faster regulatory navigation and controls that keep pace with expansion.
Ontario's decision to join the securities passport tackles another part of the same problem where regulatory duplication can consume legal budgets, management time and capital without producing proportionately better outcomes. The FCA model goes a step further by asking whether regulators should actively help successful firms navigate the next layer of complexity.
Should regulatory support extend beyond startup experimentation and authorization to help established fintechs navigate the complexity that comes with rapid 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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August 6, 2026 | NCFA Market Activity | Open Banking Open Finance And Data Sharing, Identity Privacy And Data Governance, SME Finance And Business Banking

On August 6, 2026, Yapily expanded bank account verification for Google Cloud customers across 11 European markets.
Businesses in Germany, France, the Netherlands, Spain, Portugal, Lithuania, Italy, Belgium, Austria, Ireland and Sweden can now confirm a bank account during onboarding through a direct bank connection instead of submitting documents for manual review.
The optional service builds on an account-verification arrangement first announced in 2025. Google can now use one Yapily connection across the selected markets rather than maintaining separate document checks in each country.
Businesses can verify a bank account without uploading documents and waiting for someone to review them.
Business account verification often requires a bank statement or another document showing the account holder’s name and banking details. Staff then review the document and compare it with the information submitted during onboarding.
Yapily replaces that exchange with permissioned information received from the customer’s bank. The company says Google Cloud customers can confirm a business bank account and receive a result in minutes.
The service can return account-holder and identity information, account numbers and other bank-sourced data from consumer, business and corporate accounts. The information available and the authentication process may still vary by bank and market.
The immediate benefit is lower administrative cost. Faster verification can reduce manual review and limit document errors. It may also keep a qualified customer from abandoning onboarding while a bank statement waits for approval.
Banks provide the underlying APIs, but most businesses will not connect separately to every institution. They will usually reach open banking through an infrastructure provider or software that has already built the connection into a business task.
Yapily connects to banks and handles differences in authentication, consent, data formats and market coverage. Google places the verification step inside its own onboarding process. The customer sees a faster way to confirm an account, not a separate open banking product.
The same structure can support verification, lending, accounting, payments and treasury workflows. A business may use open banking without choosing an open banking provider or even seeing its name.
Direct bank data inside SME finance software is already taking a similar route in Canada. Adoption grows when bank connectivity is built into software businesses already use.
Google controls the onboarding experience and the customer relationship. Yapily supplies the bank connectivity and verification infrastructure behind it.
That split can work for both sides. Google removes a manual step without building bank connections market by market. Yapily gains enterprise volume and proof that its network can support a large international customer.
A similar division of roles appears where Jack Henry embeds Google Cloud technology inside bank operations. Google provides the underlying infrastructure, while the company closest to the financial institution controls the workflow and customer relationship.
The Yapily arrangement shows where commercial power may settle as open banking becomes embedded. The infrastructure provider can be essential while the software platform controls distribution, product placement and the customer experience.
There is no public evidence that the Google-Yapily arrangement is exclusive or restricts competing providers. The issue is whether specialist infrastructure firms can retain pricing power when their services sit behind much larger platforms.
One Yapily integration simplifies Google’s side of the process. It does not, however, make European bank connectivity uniform.
Verification still depends on the quality of each bank’s API, the information it returns and the authentication process available in that market. Some institutions or account types may not support every field needed to complete the check.
Yapily says it connects to more than 2,000 banks across 19 European markets. Its documentation describes the broader Validate service as being in private beta. The Google Cloud rollout shows a live enterprise use, but it does not mean every applicant will receive the same experience across every bank.
Enterprise customers will care less about the size of a country list than the percentage of applicants who complete verification without falling back to manual review. Successful verification rates, exception handling and bank coverage will determine how much time the workflow saves in practice.
Google Cloud’s rollout shows where open banking is heading. The bank connection disappears into onboarding, while the customer experiences one less document request.
As open banking disappears into business software, how much of the value will remain with the infrastructure provider behind the connection?
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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Aug 6, 2026 | NCFA Insight | Artificial Intelligence And Data, Risk Compliance And Regtech, Capital Markets And Market Infrastructure

On July 29, 2026, a shareholder filed a Rackspace securities complaint alleging that the cloud company failed to explain how its AI plans were affecting capacity, spending and revenue. The complaint says Rackspace reaffirmed its 2026 guidance in May, then cut expected annual revenue by US$150 million in July. It also alleges that resources moved away from the more profitable Private Cloud business while margins absorbed restructuring and AI investment.
Those claims haven't been proven, and the court hasn't decided whether Rackspace or its directors did anything wrong. The filing still raises a useful question. Once an AI plan changes how a company spends, allocates computing capacity or describes future results, the board needs a clear view of the economics behind it. Investors may need that view too.
Rackspace isn't an isolated case. Recent complaints against Oracle, Microsoft, ZoomInfo and Upstart use different facts, but each asks whether the company story kept pace with what was happening inside the business.
An Oracle shareholder complaint alleges that the company understated the financing pressure created by its AI infrastructure build. Oracle later projected US$50 billion of capital spending for fiscal 2026, US$15 billion above its September 2025 projection, while reporting more than US$10 billion of negative free cash flow. The complaint focuses on whether investors received enough information about the scale, financing and cash impact.
A Microsoft securities complaint focuses on a different pressure point. The plaintiffs allege that Microsoft overstated Copilot adoption and didn't adequately explain that AI products were competing with Azure customers for computing capacity. Microsoft reported US$72.4 billion of capital spending in the first half of its fiscal year, almost as much as it spent in the prior full year. The unresolved issue is whether product demand, available capacity and investor disclosure remained aligned as the build accelerated.
ZoomInfo adds the risk of AI weakening the business that funds the transition. Its June 2026 complaint alleges that customers were using internal AI tools and moving away from seat-based subscriptions toward consumption pricing. The plaintiffs argue that management failed to explain how AI was changing demand for the existing model.
None of these cases proves misconduct. Shareholder complaints present company events through the plaintiff's theory, and a falling share price does not establish that earlier disclosure was misleading. The filings are interesting because they show where disputes are forming. Investors are asking what was spent, what reached customers, what revenue followed and what the rest of the business gave up.
A board cannot judge an AI strategy from product demos or spending totals alone. It needs to know what the money produced, such as more computing capacity, products in market, paying users, lower costs, higher revenue or better service.
Usage numbers can hide as much as they reveal. An enabled account may never use the product. An active user may not pay. Even paid adoption says little about retention, margins or the cost of serving that customer.
Savings claims need the same scrutiny. AI may reduce work in one team while increasing cloud costs, review time or customer complaints elsewhere. Early pilots do not need to make money immediately, but management should know what would justify further investment and what would cause it to pull back.
Boards also need to see what the AI plan is displacing. Computing capacity assigned to one product cannot serve another workload. Engineers moved to a new platform are no longer maintaining something else. A sales team promoting an AI add-on may spend less time selling the core product. Those choices may be reasonable, but the trade-offs should be clear before a profitable business starts carrying an open-ended investment.
Directors do not need to become model engineers but they do need enough operating information to test whether the plan is working. That includes supplier commitments, capacity constraints, effects on established products and a clear explanation when results fall short.
The SEC Investor Advisory Committee's AI disclosure recommendation follows the same logic. It calls on issuers to define what they mean by AI, explain how the board oversees it and disclose material effects on operations and customers. It also argues that companies can provide much of this information through existing disclosure requirements. The recommendation comes from an SEC advisory committee. It is not an SEC rule.
For banks and fintechs, weak AI performance can reach customers before it appears in an earnings release. A model may change who receives credit, how a transaction is flagged or what recommendation reaches an investor. It can also create more manual review, complaints and losses when performance moves in the wrong direction.
The Upstart securities complaint brings that issue into automated lending. Plaintiffs allege that a model update reacted too strongly to negative economic signals, reducing loan approvals and conversions while affecting revenue and guidance. The filing shows why boards need model performance connected to approval rates, customer outcomes and financial forecasts.
That connection becomes harder when a firm depends on an outside cloud, model or data provider. A vendor change can alter cost or performance. An outage can interrupt a regulated process. Concentration can leave the company without a workable alternative. NCFA's analysis of feedback loops behind AI failures shows how model output, human responses and operating data can reinforce an error before the full effect is visible.
The Financial Stability Board's 2026 consultation proposes 12 practices covering governance, the AI lifecycle, cyber risk and outside providers. It is not a binding international standard. In Canada, OSFI's Guideline E-23 on model risk takes effect on May 1, 2027 for federally regulated financial institutions. It expects clear ownership, model inventories, monitoring and communication to senior management and boards.
AI is already moving into governed financial workflows. Board reporting has to keep pace. Spending and adoption belong beside model exceptions, overrides, complaints and losses. Otherwise, financial results may arrive after the operating warning signs.
Canadian boards do not need an AI-specific statute before asking these questions. Under the Canada Business Corporations Act, directors of federal corporations must act honestly and in good faith and exercise the care, diligence and skill of a reasonably prudent person.
Canadian continuous-disclosure requirements separately require reporting issuers to publish financial statements, management's discussion and analysis, material-change reports and other prescribed information. The exact obligation depends on the issuer and the facts.
AI is already appearing in Canadian filings. The Ontario Securities Commission reviewed 225 companies in the S&P/TSX Composite and found that 72 issuers mentioned AI in 2024 annual management discussion and analysis. That is 32% of the sample. The OSC described the work as a proof of concept and did not assess whether any issuer's disclosure was adequate.
Simply mentioning AI more often will not make disclosure more useful. Investors need to know how much the company is spending, what is already in use, how customers are responding and what has changed since the last report. When AI affects capacity, margins, revenue or a regulated customer decision, a generic risk paragraph is not enough.
Canada may get more immediate value from clearer reporting on AI costs, live deployment, board oversight and business results. A separate AI disclosure rule is not the only option. Existing board duties and continuous-disclosure requirements already give companies a reason to make sure their public statements match what management is seeing inside the business.
Poor AI performance is not automatically a governance failure or securities violation. A board can approve a reasonable investment that does not work. Litigation can also overstate what directors could have known at the time. The difficult question is whether the company’s internal numbers had changed while its public story stayed the same.
When an AI plan changes spending, capacity or revenue, what should the board see before investors hear the same growth story again?
AI spending becomes a board issue when it is material to strategy, capital commitments, margins, capacity, customer outcomes or regulated operations.
No. The complaints contain allegations that have not been proven, and courts have not decided the merits. They identify the spending, adoption, capacity and business-model questions investors are asking.
The board should see enough financial, operating, customer and model-performance information to challenge the investment and recognize when results depart from the approved plan.
Canada does not have a single AI-specific securities disclosure rule for public issuers. Existing corporate duties and securities requirements can still apply when AI costs, risks or operating effects become material.
An AI model can affect credit, fraud controls, suitability, customer service and complaints before its full financial effect appears in company results.
This article is provided for informational purposes and does not constitute investment, financial or legal advice. Lawsuits discussed contain allegations that have not been proven in court. Recommendations, consultations and regulatory requirements may change.
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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On August 3, 2026, nine U.S. senators asked the CFTC to consider restricting wildfire prediction markets that pay out based on how long a fire lasts, how much damage it causes or how far it spreads. Their letter to the CFTC argues that these contracts may conflict with public safety, emergency response and market integrity.
That raises a larger question. Should prediction markets be allowed to trade on disasters at all?
The evidence suggests they can, but only within much tighter limits than ordinary event markets. Prediction markets may combine information and react quickly to new public facts. That value starts to break down when traders can influence the result, hold protected information or profit as the damage grows.
The issue comes down to four practical tests:
The regulatory line is still being drawn. The CFTC is reviewing prediction markets, but its current proposal does not treat disasters as a separate category. Regulators now have to decide where legitimate forecasting ends and unacceptable incentives begin.
The same tension extends beyond wildfires. NCFA unveiled the boundary between informed trading and unfair advantage in When Does A Smart Prediction Become Insider Trading?
Click each item to expand
Nine senators asked the CFTC whether wildfire contracts serve the public interest and whether the agency plans guidance, enforcement or restrictions.
The request is not a prohibition. It places disaster contracts directly inside the CFTC’s wider debate over which events should be open for trading.
Polymarket opened a market on how many acres the Palisades Fire would burn before it was fully contained.
The market used a clear public source, which helps. The harder issue is the payoff itself. Traders made money by correctly predicting how much land an active disaster would consume.
The argument for prediction markets is not imaginary. A well run market can combine information from many participants and update faster than a survey or committee.
That creates a possible public use. A wildfire market could draw attention to new weather, acreage or containment data. It still has to prove that the price adds something useful beyond the official information already available.
Prediction markets often benefit when informed people trade. That logic starts to break when the information comes from a public duty, a private contract or direct control over the event.
Good research is one thing. Trading on protected emergency information is another. A trader who can affect the fire belongs in an even more serious category.
Polymarket now bars users from trading on stolen confidential information, illegal tips or outcomes they can influence.
Those controls are useful. They do not settle the listing question. Surveillance usually acts after trading starts, while a badly designed contract may create a risk that should not enter the market in the first place.
The CFTC is working through how event contracts fit with market integrity, public interest and responsible innovation.
That leaves an open decision. A wildfire contract may still fail a manipulation, unlawful conduct or public interest test, but disaster status alone does not yet settle the issue.
A blanket ban on anything connected to weather or catastrophe would go too far. Insurance, weather derivatives and catastrophe bonds help businesses and investors manage risks they already carry. They are not the same as a retail trader buying a position because a fire may destroy more property.
The better test starts with the contract itself.
Contracts that pay more as deaths, destruction or suffering increase should generally not be allowed. Regulators do not need proof of arson before deciding that the incentive itself creates too much risk.
Contracts that insiders or participants can influence need strict exclusions. Fire crews, government officials, contractors and others with protected information or operating control should not be allowed to trade.
Markets built on public data may have a stronger case when the outcome is neutral, the rules are clear and the platform can explain what useful decision the market supports.
Position limits, trading pauses and conflict checks can reduce risk, but they cannot rescue a contract whose basic payoff rewards greater harm.
Market prices shouldn’t be presented as emergency forecasts unless the platform can prove they are reliable and add something useful beyond established fire forecasts.
Some people will still want a full ban. Some platforms will argue the rules go too far. The practical acid test should be: protect public safety without blocking every market that may produce useful information.
No Canadian wildfire prediction market has been identified. That doesn’t mean Canada should wait for one before deciding how the rules would apply.
A single contract could fall across provincial derivatives law, gaming rules, consumer protection and market integrity. The most valuable information may also be with provincial agencies, municipalities, utilities, insurers and private response firms before it reaches the public.
Canadian regulators should decide now whether contracts tied to deaths, destruction or an active emergency should be allowed at all. They also need clear rules on who can trade, how protected emergency information is handled and what happens when an offshore platform lists a market on a Canadian disaster.
The first case will be easier to manage if the rules are already clear. Waiting for a public controversy would leave regulators reacting after the risk has arrived.
Do you agree the evidence supports this answer?
Click Agree or Disagree. Your vote is recorded anonymously and aggregate results are tracked.

The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem providing education, market intelligence, industry stewardship, networking and funding opportunities to thousands of members. NCFA works with industry, government, partners and affiliates to support a competitive and innovative fintech and funding sector in Canada. Join Canada’s Fintech and Funding Community or learn more at NCFA Canada.
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Aug 3, 2026 | Artificial Intelligence And Data, Banking And Credit, Risk Compliance And Regtech

The financial technology industry is changing as users expect quick responses, services tailored to their needs and smooth digital interactions. Artificial intelligence in CRM software is helping these companies improve how they interact with users - this technology is able to analyze data, automate interactions and provide detailed information about user requirements. When businesses combine management tools with artificial intelligence, they are able to create communication strategies that are more efficient plus build more stable relationships with users.
Fintech companies manage large quantities of information from transactions, account activity and digital interactions. The best CRM software helps these organizations organize and evaluate this information - identifying patterns. Businesses are able to use automated systems instead of manual reviews to understand user preferences but also create experiences that are more relevant.
Advanced platforms allow financial service providers to create profiles that show communication history, financial behaviors and service preferences - this information is useful for teams to provide specific support and make better decisions. When businesses have a clear understanding of their users, they are able to offer services that match individual requirements.
Personalization is a significant part of engagement because users expect services that match their specific situations. Solutions using artificial intelligence are able to analyze interactions as well as recommend products or services based on data - this allows companies to move away from general messages and provide communication that is more useful.
Systems are also able to help businesses predict what a user needs before a problem occurs. As an example, a platform is able to identify changes in behavior so that financial teams provide information at the correct time - this method is proactive and increases satisfaction.
Support is a primary area where artificial intelligence is changing how companies interact with users. Automated chat tools, intelligent response systems or the integration of data allow companies to provide assistance more quickly - these technologies are able to answer frequent questions so that support teams are able to focus on more difficult concerns.
Platforms also give representatives access to important information during a conversation - this reduces the need for users to repeat their details and allows employees to provide solutions that are more effective. A support process that is efficient is able to improve trust and strengthen long term relationships.
Fintech companies require accurate information to make decisions about products next to marketing. AI CRM provides analytics that help businesses understand trends and evaluate strategies - these details allow organizations to identify areas for improvement and change their services based on how users behave.
Selecting the most appropriate software requires an evaluation of features like automation plus data analysis. Businesses are in need of solutions that handle financial data securely. Artificial intelligence is able to help companies make informed decisions and improve their general strategies for engagement.
Automation is a useful tool for businesses that want to be more efficient and maintain consistent communication. Platforms are able to automate tasks like follow up messages but also routine notifications - this reduces the amount of administrative work and allows employees to spend more time on activities that require human attention.
Automation is also helpful for maintaining engagement throughout the time a user is with a company. From the initial signup to ongoing support, the systems are able to ensure that users receive communication on time - this consistent interaction helps businesses create experiences that are smoother.
Security is a critical concern because companies manage sensitive financial information. Software is able to assist companies - monitoring interactions, identifying unusual activity and supporting compliance - these features help businesses manage risks while they maintain efficient interactions.
Tools are also able to improve internal visibility - providing records of communications as well as activities - this information is helpful for organizations to remain accountable and respond to regulations. When companies combine management with security features, they are able to create digital experiences that are safer.
Artificial intelligence is changing how fintech companies connect with users - improving personalization and decision making. As digital services expand, businesses that use intelligent solutions are able to understand expectations or provide experiences that are more responsive.
The future of engagement will continue to rely on technologies that combine data analysis with efficient communication - these systems give organizations the ability to build stronger relationships. When companies use these tools, they are able to create experiences for their users that are more reliable and valuable.
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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