Karsten Wenzlaff, Advisor
August 26th, 2025
Karsten Wenzlaff is a Crowdfunding and Fintech Expert from Germany. He is Secretary-General of Digit[...]
Category Archives: Venture Funding and Building
Canadian VC Is Growing Again, But Fewer Companies Are Getting Funded
August 7, 2026 | NCFA Insight | Capital Markets And Market Infrastructure, SME Finance And Business Banking, Public Sector Policy And Industrial Strategy
Canadian VC Growth Masks A Thinner Funding Pipeline
Canadian venture capital reached $2.69 billion across 250 deals in the first half of 2026, according to the CVCA's latest market data. Capital invested rose 17% from H1 2025, the first year-over-year increase in first-half dollars since 2021.
Deal count moved the other way. It fell 8.8% from 274 to 250, marking a fifth consecutive first-half decline, while average financing size increased from $8.4 million to $11.38 million.
Canada is putting more venture capital to work without funding more companies. Larger rounds are lifting the national total while seed financing continues to weaken.
More Money Is Concentrating In Larger Rounds
| H1 2026 | Capital | Deals | Avg. Deal | YoY |
|---|---|---|---|---|
| Total VC | $2.69B | 250 | $11.38M | Capital +17%; deals -8.8% |
| Seed | $285M | 82 | ~$3.5M | Capital -31%; deals -13% |
| Early Stage | $1.18B | 68 | ~$17.4M | Capital +29%; deals essentially flat |
| Later Stage | $984M | 18 | $54.67M | Capital +23%; eight fewer deals |
Sixteen rounds of $50 million or more absorbed $1.57 billion, or 59% of all capital invested. Five financings above $100 million alone accounted for $807 million, equal to 30% of the national total.
Most transactions were much smaller. Deals below $25 million represented 85% of disclosed financings but received only 32% of the money. Another 155 rounds closed below $5 million and collectively attracted $221 million.
The same pressure is visible on the fund side. Canadian VC fundraising became more concentrated in 2025, leaving more capital in fewer hands and raising the bar for companies trying to get into institutional portfolios.
Seed Financing Is Still Moving Backward
Seed is the clearest warning in the report. Investment fell 31% to $285 million across 82 deals, while transaction count declined 13%. Pre-seed added $52 million across 56 financings, with an average round below $1 million.
Early stage looks healthier at $1.18 billion, up 29%, but the number of financings barely changed. More capital went into roughly the same number of companies, pushing the average early-stage round to about $17.4 million.
Later-stage financing is even more concentrated. The $984 million invested was spread across only 18 transactions, the lowest first-half deal count in CVCA's series. The average round reached $54.67 million.
For founders, companies with traction and scale can still attract large rounds, while the market for the first few million dollars is getting tighter.
Some of that friction is structural. Smaller Canadian financings can carry disproportionately high compliance costs because many legal, disclosure and regulatory costs do not get proportionally cheaper as the raise gets smaller. Ontario's decision to join Canada's securities passport should reduce some duplication, but it does not by itself solve the economics of small-company financing.
There is also a financing-fit problem. Merchant Growth founder David Gens argues that many smaller businesses are asset light and cash-flow driven, while traditional lending still relies heavily on assets that can be pledged as collateral. After nearly $1.5 billion deployed to about 15,000 Canadian small businesses, his point is practical: access to capital and access to financing that fits the business are not the same thing.
Those problems compound. A company may need grants, founder capital, crowdfunding, angel money, debt and venture financing at different points in its growth. Canada's small-business capital access gap is therefore less about finding one missing source of money than making it easier for companies to move from one financing stage to the next.
If fewer businesses get financed near the bottom, fewer can build the traction needed to compete for the larger rounds that are keeping Canada's headline VC numbers up.
Fintech Shows What It Takes To Raise At Scale
Financial technology supplied four of the larger disclosed rounds in the first half. KOHO raised $130 million, nesto $107 million, Float $85.4 million and Relay $68.8 million. Together they represent almost $392 million in financing.
KOHO has been building toward banking scale, adding credit products and pursuing a Schedule 1 bank licence. Its $130 million Series E was one of the largest disclosed Canadian VC financings in H1.
Float has been expanding its SME finance platform across business accounts, spend management and working-capital products. Its $85.4 million H1 financing followed earlier equity financing and continued expansion into business banking and credit.
Relay has been scaling its SMB finance platform, raising US$50 million in its Series B as it expanded banking and cash-flow tools for small businesses. The CVCA records its H1 2026 financing at $68.8 million in Canadian-dollar terms.
These companies already have products, customers and operating histories. Their ability to attract larger rounds shows where capital is still available, while the weaker seed numbers show how much harder it may be for the next group to reach that point.
Foreign Capital Still Matters At The Top
U.S. investors participated in 28.0% of Canadian VC transactions in H1, up from 25.9% in 2025. European participation reached 10.8%, the highest share in the six-year series, while 66.4% of transactions were financed exclusively by Canadian investors.
The money also remains geographically concentrated. Ontario, Quebec and British Columbia accounted for 91% of capital and 80% of transactions. Toronto led with $879.7 million across 64 deals, followed by Montreal with $619 million across 50.
See: What Canada Can Learn From The SEC Small Business Forum
For founders that reach scale, Canada remains connected to large domestic and international pools of capital. For investors, the concern is whether enough new companies (read: Canada's farm team) are being financed underneath them to keep producing attractive later-stage opportunities.
The Headline Recovery Hides A Thinner Pipeline
H1 2026 looks better than H1 2025 if the measure is dollars invested. It looks weaker if the measure is how many companies received venture financing, and weaker again at seed.
For founders, proof of traction and financing readiness carry more weight in a selective market. For investors, larger rounds remain available, but a shrinking seed base can become a sourcing problem several years down the road.
Canada needs capital at both ends. Proven companies need enough money to scale, while younger companies need financing that fits where they are today and gives them a realistic way to reach the next stage. The CVCA numbers show stronger deployment at the top of the market, but they don't show yet at the mid way point of 2026 that the pipeline feeding it is getting healthier.
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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Decade Raises US$85M To Build AI Wealth On Open Finance
August 7, 2026 | NCFA Market Activity | Wealth Investing And Trading, Artificial Intelligence And Data, Open Banking Open Finance And Data Sharing
AI Wealth Management On Brazil’s Open Finance Rails
On August 4, 2026, Brazilian wealthtech Decade emerged from stealth with an US$85 million seed round backed by Greenoaks, Benchmark and Diffusion. Decade describes it as the largest seed round ever raised by a Latin American startup.
The size of the financing gets attention, but the operating model is more interesting. Decade is building a wealth-management service that can connect investment and financial data held across different institutions, analyze the client's wider financial position with AI and pair that technology with human advisers.
Clients do not have to transfer their investments to Decade first. They can connect accounts through Brazil's Open Finance system, add statements or other assets and build a consolidated view that can include investments, pensions, international holdings and real estate. Decade currently charges R$200 per month (approx $54 CAD) on an annual plan for its Decade Intelligence service and is admitting customers gradually.
Brazil's Open Investment Rails Change The Wealth Model
The regulatory infrastructure underneath Decade is important. Brazil expanded Open Finance into investments through Open Investment, allowing customers to authorize the sharing of investment information held across participating financial institutions.
The Central Bank of Brazil specifically identified faster consolidation of investments held with different custodians as one of the benefits. Today the data that can be shared includes stocks, ETFs, investment funds, government bonds, debentures and several types of fixed-income securities.
That creates a different competitive starting point for wealthtech. A new adviser can potentially understand more of a client's portfolio without first winning custody of every asset. The client relationship, data layer and asset custody no longer have to all be tied to the same institution.
Decade's privacy policy shows how that works in practice. Customer-authorized Open Finance data is received through Iniciador Instituição de Pagamento Ltda., a payment institution authorized by the Central Bank and participating in Open Finance. Decade can then consolidate and analyze the information inside its own service without becoming the custodian of those investments.
AI Works Across The Financial Picture
Decade's product combines continuous monitoring with human advice. The company says its AI can monitor markets and portfolios around the clock, identify high fees and tax inefficiencies, review spending and cash flow, run investment simulations and retain context from previous client interactions.
Each client can also work with a human adviser. Decade positions the AI as the analytical layer and the adviser as the source of judgment, explanation and support around decisions.
That makes the model more substantial than a robo-adviser that simply recommends a portfolio. The platform is trying to work across investments, spending, taxes, financial goals and assets held elsewhere while keeping a person in the advisory relationship.
The approach also extends a direction already visible in AI-supported wealth management. Technology can expand the amount of portfolio analysis and monitoring available to advisers without removing the human adviser from the service.
The US$85M Bet Still Needs Client Evidence
The founders bring unusually relevant experience. CEO Vitor Olivier was an early Nubank engineer who later became its CTO, while Head of AI Felipe Meneses founded financial-services AI company Hyperplane before Nubank acquired it. Decade says Olivier previously worked in wealth management at BTG Pactual.
But the financing is well ahead of the operating proof. Access remains limited, and Decade has not disclosed client numbers, assets advised, retention, portfolio outcomes or how much of the service customers use through AI versus human advisers.
See: RBC Invests In d1g1t To Scale Canadian Wealthtech
The regulatory perimeter is also still developing. Decade's website states that Decade Asset Management Ltda. is in the process of obtaining CVM accreditation and joining ANBIMA's third-party asset-management codes. The current service therefore should not be described as Decade taking custody of or independently managing client assets.
The larger opportunity is clearer. Brazil's regulators have already created a way for investment data to travel with the customer. Decade is testing whether a new wealth company can use that access to compete for advice and financial intelligence before it competes for custody.
Talking Point
If Open Investment lets customers bring a consolidated view of their portfolios to a new adviser without moving the underlying assets, does custody remain the main competitive advantage in wealth management or does intelligence around the assets become more 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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Coding Agents Get Cheaper As Meta Joins A Faster AI Race
August 7, 2026 | NCFA Market Activity | Artificial Intelligence And Data, Cybersecurity And Fraud, Risk Compliance And Regtech
Coding Agents Get Cheaper And More Competitive
On August 5, 2026, Meta released Muse Code in beta, a terminal-based coding agent that can plan changes, write code, validate results and divide larger jobs among parallel sub-agents across software repositories.
The timing of the announcement is more interesting than the launch alone. In less than three weeks, Moonshot released the 2.8-trillion-parameter Kimi K3 as an open-weight model built for reasoning and long-horizon coding, OpenAI cut the price of GPT-5.6 Luna by 80%, DeepSeek launched V4-Flash at $0.14 per million input tokens and Alibaba unveiled the 2.4-trillion-parameter Qwen3.8-Max.
Coding agents are taking on more of the engineering job while the models underneath them are getting cheaper and easier to mix and match.
For fintechs and financial institutions, that makes the buying decision less about which model tops a benchmark and more about what useful work gets completed, at what cost and under which controls.
Three Weeks Changed The Cost And Model Landscape
| Date | Development | Key Data | What Changed |
|---|---|---|---|
| Jul. 17 | Kimi K3 | 2.8T parameters; 1M-token context; open weights | Moonshot added a very large open-weight option designed for advanced reasoning and long-horizon coding. |
| Jul. 30 | OpenAI GPT-5.6 Luna | Input cut from $1.00 to $0.20/M; output from $6.00 to $1.20/M | OpenAI cut Luna pricing 80% as business customers pushed harder on AI costs and lower-cost competition intensified. |
| Aug. 3 | DeepSeek V4-Flash | $0.14/M input; $0.28/M output; about $0.03 average benchmark-test cost | DeepSeek pushed the price floor sharply lower, although its overall intelligence score remains below the strongest frontier models. |
| Aug. 3 | Qwen3.8-Max | 2.4T parameters; 1M-token context; 95B parameters active per request | Alibaba expanded the open-weight option for large-context and agentic workloads while using a mixture-of-experts design to reduce compute requirements per request. |
| Aug. 5 | Meta Muse Code | $1.25/M input; $4.25/M output | Meta entered long-running agentic coding with parallel sub-agents and a persistent activity log. |
The price cuts do not mean every engineering job is suddenly cheap. Agentic work can consume substantially more compute because agents inspect repositories, call tools, run tests, retry failed work and sometimes launch other agents.
Cursor has already changed its pricing around that reality. It says a difficult agent request can consume an order of magnitude more tokens than a simple request, such as a syntax question. In June, Cursor also introduced a $120 monthly Premium team seat with five times the included usage of its $40 Standard seat and added stronger spend alerts for administrators.
That makes cost per million tokens a weak buying metric on its own. A fintech should care more about the cost of a completed engineering task after model calls, retries, testing and developer review.
The Agent And The Model Are Starting To Separate
Another important change is that choosing a coding environment no longer always means committing to one model provider.
Qwen Code is an open-source terminal agent that supports OpenAI-, Anthropic- and Gemini-compatible APIs, Alibaba Cloud, other providers and bring-your-own API keys. GitHub Copilot and Cursor also offer access to multiple models inside their development environments.
That creates two buying decisions. Which agent should work with the codebase, and which model should do the reasoning underneath it. A financial firm could use one managed development interface while selecting different models for cost, capability or internal risk requirements.
Open-weight does not mean free. The firm still has to pay to run the model or provide the computing infrastructure, monitoring and security needed to operate it itself. Commercial terms are also evolving. Alibaba is reportedly preparing revenue-sharing requirements for some large commercial users of Qwen3.8-Max, following a similar approach used by Moonshot for Kimi K3.
Which Coding Agent Fits Which Financial Firm?
Codex and Claude Code are already competing for larger repository-level assignments. Meta now joins a field where workflow, model choice, governance and billing can matter as much as raw coding performance.
| Platform | Current Cost | Model Choice | Enterprise Difference | Best Fit |
|---|---|---|---|---|
| GitHub Copilot | Business $19/user/mo; Enterprise $39 | Broad model catalogue | Cloud agent, code review, access and budget controls, governance, IP indemnity and pooled AI credits | Banks and fintechs already standardized on GitHub and Microsoft development workflows |
| Claude Code | Pay-as-you-go for Team and Enterprise through Anthropic Console | Anthropic models | Filesystem and network sandboxing; Enterprise adds SSO, SCIM, fine-grained permissions and audit logs | Complex delegated work where containment and access controls carry more weight |
| OpenAI Codex | Included with ChatGPT Business at $20/user/mo annually; extra usage is token-linked through credits | OpenAI models | Business workspace controls, budgeting, SAML SSO, MFA and no training on business data by default | Teams already using OpenAI across engineering and business workflows |
| Cursor | Teams Standard $40/user/mo; Premium $120 | Multiple frontier and first-party models | AI-native editor, cloud agents, usage pools and spend controls | Engineering-led fintechs willing to make AI central to the development environment |
| Amazon Q Developer | Pro $19/user/mo | AWS-managed models | IDE and CLI agents, IAM Identity Center support, admin dashboards, application transformation and IP indemnity | AWS-heavy financial firms |
| Gemini Code Assist | Standard about $19/user/mo annually; Enterprise about $45 | Google models | Agent mode and Gemini CLI; Enterprise adds private code customization and higher agent usage | Google Cloud development environments |
| Meta Muse Code | $1.25/M input; $4.25/M output in beta | Muse Spark 1.2 | Large-repository work, parallel sub-agents and persistent task history | Worth testing, but enterprise maturity is still unproven during beta |
| Qwen Code | Open-source agent; provider or infrastructure costs vary | Multi-provider and bring-your-own-model | Separates the coding interface from the model provider and supports sub-agents | Firms prioritizing portability, lower-cost inference or more control over the model layer |
There isn't a universal winner (yet?).
GitHub Copilot has the cleanest operational fit where GitHub already anchors development.
Amazon Q and Gemini Code Assist benefit from existing cloud relationships.
Claude Code and Codex are stronger candidates where teams want to hand over larger engineering assignments.
See: AI Agents Enter Governed Financial Workflows
Cursor asks firms to make AI more central to the development environment.
Muse Code is too new to put in the same enterprise-maturity category. Its price and multi-agent design are competitive.
Meta still has to show how it performs inside real development teams and which administrative controls follow the beta.
Qwen Code offers a different choice. Firms can keep the coding interface more portable and compete the model layer separately, which becomes more valuable when model prices can fall 80% in a matter of weeks.
For Financial Firms, Access Is Part Of The Product
A coding agent becomes more useful as it gains permission to read repositories, edit files, run commands, call tools and execute tests. Those permissions also increase the consequences of an incorrect instruction, compromised dependency or bad code change.
Anthropic says sandboxing reduced Claude Code permission prompts by 84% in its internal use by giving the agent defined filesystem and network boundaries rather than asking a developer to approve every action.
For financial firms, security and oversight need to be part of the comparison alongside coding quality. That includes who can access the agent, what repositories it can reach, what activity gets logged, how data is handled, whether code is used to train models, what networks it can connect to and how spending is controlled.
The market is changing quickly. Open-weight models are pushing down prices, coding tools can increasingly work with more than one model, and agents are taking on larger jobs that make simple token-price comparisons less useful.
Banks and fintechs should be prudent and practical. How much usable engineering work did the agent complete, what did it cost, how often did a developer need to step in and did the work stay within the firm’s security and approval rules?
Talking Point
As coding agents and models become easier to mix and match, should financial firms standardize on one managed platform or keep the agent, model and infrastructure layers separate so each can compete on capability, cost and control?
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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HarborLine Builds A Marketplace For Portfolio-Backed Loans
August 6, 2026 | NCFA Market Activity | Wealth Investing And Trading, Banking And Credit, Capital Markets And Market Infrastructure
Marketplace Lending For Investment Portfolios
On August 6, 2026, U.S. fintech HarborLine introduced its securities-backed lending marketplace, a platform designed to help qualifying investors access loans against their investment portfolios by coordinating banks, advisers and brokerage custodians through one digital workflow.
Investors can seek borrowing capacity without selling eligible securities, while HarborLine handles portfolio eligibility checks, lender matching, loan documentation, collateral pledges and ongoing monitoring.
The company is trying to open a lending process that has traditionally been concentrated inside private banks, large brokerages and wealth firms. Its platform connects investors and advisers with banks and brokerage custodians, then coordinates the portfolio review, loan application, lender matching, collateral pledge and ongoing monitoring.
HarborLine doesn't lend the money, hold the investments or provide investment advice. Banks make the credit decisions, while custodians continue to hold the pledged assets.
HarborLine is trying to make portfolio-backed borrowing available without requiring the investor, bank, adviser and custodian to manage the process separately.
How HarborLine Turns Investments Into Borrowing Capacity
This isn't a public marketplace where fund managers list portfolios for lenders to browse. The borrower is generally an investor who owns an eligible brokerage account, either directly or through an adviser.
The investor connects the account so HarborLine can review which investments qualify as collateral and how much borrowing each one can support. Liquid, diversified securities may support more credit than concentrated positions or assets a bank considers difficult to sell.
HarborLine says it packages each verified application in a standardized format and distributes it to matched bank partners. Each bank reviews the file and decides whether to make an offer on its own terms.
If the borrower accepts, HarborLine coordinates the documents and collateral pledge with the brokerage custodian. The investments remain in the account, but the bank receives a security interest over them.
The platform also tracks eligible collateral, available credit and loan-to-value headroom after funding. If the portfolio falls far enough, the borrower may need to repay part of the balance or add more assets.
Banks Lend While Advisers Keep The Client
Securities-backed credit has traditionally been easier to obtain through private banks, large brokerages and wealth firms that already control the investment account, lending channel and customer relationship.
Wealthsimple’s portfolio line of credit is an example of that integrated model. Eligible clients can borrow through the same platform that holds their investments and manages the account experience.
HarborLine separates those roles. A bank can provide the capital without owning the brokerage relationship. The custodian keeps the assets. The adviser continues serving the client. HarborLine manages the information and handoffs between them.
That could help regional banks and independent advisers offer securities-backed lending without building the full operating process themselves. It could also give qualifying investors another route beyond a private bank or vertically integrated wealth platform.
Strong coordination is needed in making a four-party transaction feel as straightforward as borrowing from the firm that already holds the customer’s investments.
Bank Participation Will Decide Whether HarborLine Works
HarborLine explains the workflow, but it hasn't name participating banks or custodians yet. Those disclosures will determine whether HarborLine is operating as a true multi-lender marketplace or is still assembling the network needed to support one.
Borrowers need enough lender choice to improve access or terms. Banks need qualified applications and reliable collateral information. Custodians need a practical way to place, monitor and release pledges.
See: APX Powers Embedded Crypto Loans For Netcoins Canada
The technology can reduce paperwork and coordinate the process, but it cannot remove the lending risk.
HarborLine states that it isn't a bank, broker-dealer, investment adviser or custodian. That keeps the credit, custody and investment decisions with the regulated firms involved while positioning HarborLine as the operating layer between them.
Talking Point
Can HarborLine widen access to portfolio-backed credit, or will investors still prefer wealth platforms that already hold their assets and control the full lending experience?
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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Google Cloud Expands Open Banking Account Verification
August 6, 2026 | NCFA Market Activity | Open Banking Open Finance And Data Sharing, Identity Privacy And Data Governance, SME Finance And Business Banking
Bank Data Moves Deeper Into Business Onboarding
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.
Bank Data Replaces A Manual Onboarding Step
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.
Open Banking Will Usually Arrive Inside Other Software
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.
The Platform Owns The Customer. Yapily Runs The Connection
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.
Coverage Quality Will Decide Whether The Workflow Works
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.
See: Canada Open Banking and Consumer Driven Banking Rules
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.
Talking Point
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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AI Spending, Board Oversight And Investor Disclosure
Aug 6, 2026 | NCFA Insight | Artificial Intelligence And Data, Risk Compliance And Regtech, Capital Markets And Market Infrastructure
What Boards And Investors Need To See As AI Costs Rise
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.
Lawsuits Follow The Gap Between AI Plans And Results
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.
Boards Need The Numbers Behind The AI Plan
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.
Financial Firms Carry More Than Spending Risk
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.
Canada Already Has A Route To Better AI Disclosure
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.
Talking Point
When an AI plan changes spending, capacity or revenue, what should the board see before investors hear the same growth story again?
Frequently Asked Questions About AI Spending And Board Oversight
When does AI spending become a board issue?
AI spending becomes a board issue when it is material to strategy, capital commitments, margins, capacity, customer outcomes or regulated operations.
Do the current AI lawsuits prove companies misled investors?
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.
What AI information should a board receive?
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.
Does Canada have a special AI disclosure rule for public companies?
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.
Why is AI oversight harder for banks and fintechs?
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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The Next Open Banking Battle Is Decision Intelligence
August 6, 2026 | NCFA Insight | Open Banking Open Finance And Data Sharing, Artificial Intelligence And Data, Competition And Market Structure
Who Captures the Value Created Above Financial Data Access
Canada’s open banking framework will help customers move financial data between approved providers. The larger commercial opportunity is what firms can do with it: improve credit decisions, spot fraud earlier, give useful financial guidance and respond faster when customers need help.
Secure access is essential, but access alone won’t determine which firms create the most value.
On July 31, 2026, Scotiabank introduced three knowledge agents through Scotia Intelligence. More than 71,000 employees have access to the bank’s AI capabilities, which had generated 14 million actions since launching in March 2026.
The agents help employees find approved information on delivery standards, procurement, travel and expenses. They aren’t open banking products. They show how a large bank is learning to govern information, connect it to AI systems and use those systems inside daily workflows.
Banks shouldn’t judge AI only by model accuracy. The stronger measure is whether it improves a real financial decision, such as approving credit, detecting fraud or helping a customer act sooner.
Canada’s Rules Stop At The Data Layer
Canada’s proposed Consumer-Driven Banking Regulations establish requirements for accreditation, consent, security, liability, technical standards, complaints and Bank of Canada oversight. The framework is intended to replace screen scraping with regulated API access.
NCFA’s Canada open banking rules guide tracks the remaining work across participation, authentication, consent records, security and implementation.
Participating institutions must share prescribed account, balance, transaction and product information when a customer requests it. They generally don’t have to share data that has been commercially enhanced to increase its usefulness or value.
A customer may transfer transaction history to another provider while leaving behind the bank’s internal risk scores, fraud patterns, behavioural classifications and service recommendations. The records can move with permission. Much of the interpretation may stay behind.
That gives firms room to compete through better models and products. It also means data portability may do less to weaken an incumbent bank’s advantage than many open banking advocates expect.
Credit, Fraud And Cash Flow Will Show What The Data Is Worth
An API connection has little value on its own. The information becomes commercially useful when it improves an outcome that a customer or business can measure.
Cash-flow underwriting could give lenders a more current view of whether a borrower can repay. Incoming revenue, recurring expenses, balance patterns and payment obligations may reveal more than a static credit file.
That could help small businesses, gig workers and borrowers with limited credit histories. It could also produce faster denials, intrusive classifications or pricing that customers don’t understand. More data does not automatically produce a better result.
Fraud systems could review activity across connected accounts instead of assessing one transaction in isolation. A small-business platform could warn about an approaching cash shortfall. A lender could use verified income and spending patterns to assess eligibility or limits. A financial app could identify expensive debt, idle cash or recurring charges that warrant action.
NCFA’s open banking commercialization roadmap identifies cash-flow tools, embedded credit, onboarding and treasury services as potential businesses built above data access.
For a broader view of the companies, regulation, infrastructure and opportunities developing around financial data access, explore NCFA's Open Banking market intelligence.
Did the decision reduce fraud losses, improve credit performance, help a customer avoid a fee or resolve a problem sooner? A more accurate model matters only when it produces a better result.
Data Portability May Still Leave Banks In Control
Open banking is expected to reduce the advantage banks receive from holding customer account data. It doesn’t give every participant the same ability to use that data.
Large banks already have long account histories, established distribution, compliance teams, capital and multiple products. They can combine outside data with information held across deposits, cards, loans, investments and customer-service interactions.
Scotiabank’s deployment shows how quickly an incumbent can build governed AI workflows before shared financial data becomes widely available. Its announcement doesn't unveil any details about how the knowledge agents may connect or interact with consumer-driven banking, so any future use remains unconfirmed.
Fintechs have a different opportunity. A specialist may understand one customer group or financial decision better than a universal bank. It can concentrate on cash-flow underwriting, fraud detection, financial guidance or small-business treasury without carrying the same legacy systems.
Infrastructure providers may supply the data normalization, identity, consent, categorization and scoring tools used by both sides. A fintech could develop the intelligence, a bank could provide the balance sheet and distribution, and an infrastructure company could connect the workflow.
That arrangement may create strong suppliers without changing who owns the customer relationship.
Competition perspective is that Canada should measure whether independent providers gain durable customer relationships, whether switching produces better products and whether smaller firms can compete without becoming feature suppliers to incumbent banks.
Customers Need Rights Over How Their Data Is Used
Permission to access financial data should not become unlimited permission to infer, classify and act.
A customer may share transaction history so an app can prepare a budget. The same records might also be used to infer financial stress, employment stability, likely life events or willingness to accept a higher price. Those uses carry different consequences.
The stakes and liability rise when an inference affects credit, account access, fraud controls, pricing or financial guidance.
A statistically strong model can still produce unwanted results that rely on incomplete data, reproduce unequal outcomes or optimize for the institution rather than the customer. Fraud systems can block legitimate transactions. Cash-flow models can misread irregular income. Personalized recommendations can become disguised sales prompts.
Banks and fintechs need clear purpose limits, explanations for consequential outcomes, ways to correct inaccurate information and human review where the impact justifies it. They also need to establish who is accountable when data, scoring and distribution come from different providers.
This differs from the authorization problem reviewed in AI payments consent and liability. That issue concerns whether an AI agent can legally authorize a payment. Decision intelligence concerns how permissioned data is interpreted and how those interpretations affect the options, prices and outcomes presented to a customer.
Canada is building open banking around secure access and customer control. The next competitive issue is whether banks and fintechs can turn shared data into better decisions without making those decisions harder to understand or challenge.
The firms that succeed will need to prove that their decisions are commercially better and that customers receive a fair share of the benefit. Otherwise, financial data may become more portable while market power remains largely where it is today.
Talking Point
If customers can move their financial records but not the intelligence created from them, how much competitive power does open banking really transfer?
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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