Malhotra urged Indian banks to inventory the AI models they use and establish board approved AI governance policies, while arguing that banks cannot afford to avoid the technology because of its risks. The BRICS discussion is less advanced, but it has progressed beyond India's proposal earlier this year.
AI Governance Is Becoming A Bank Operating Responsibility
The RBI has been working toward this point for some time. India's FREE-AI framework was developed around responsible AI use in finance, including governance, explainability, privacy, fairness and risk controls. The central bank has also been expanding its model risk work beyond credit models into other operating areas.
Malhotra's August remarks make the institutional responsibility clearer. A bank cannot govern AI well if it cannot identify which models are running, where they are used or who owns the decisions around them. An inventory creates the starting record. Board approved governance then establishes responsibility above individual technology teams.
That becomes more important as AI spreads beyond analysis into customer service, credit, fraud detection, risk management and other live banking functions. Cybersecurity and operational risk also grow with the number of systems, vendors and data connections involved.
Canada is dealing with more or less the same operating problem. OSFI and GRI's financial AI work found that regulated deployment depends on practical controls around identity, cyber risk, financial crime, third party providers and customer outcomes. More than 170 participants contributed to that work, with 72% reporting AI use at work in financial services.
Financial regulators are asking institutions to prove control over AI that is already being used. For banks, model inventories, ownership, monitoring and escalation are becoming part of normal operating discipline.
BRICS Payment Links Advance From Proposal To Discussion
The payments comments are at an earlier stage.
In January, India proposed that BRICS members consider linking their official digital currencies for cross border trade and tourism payments. NCFA captured that proposal in its January BRICS payments evidence.
Malhotra now says BRICS members are discussing potential links between both fast payment systems and CBDCs. The objective is lower cost cross border payments, but he was equally clear that the work is still at the discussion stage.
There is no shared BRICS CBDC network, agreed settlement architecture or implementation timetable. There is also no basis for treating the discussions as evidence that a common BRICS currency is being created. Official BRICS material has instead focused on reducing the cost of trade and financial transactions and improving payment links between member countries.
The addition of fast payment systems is also important. CBDCs are only one possible route. Connecting existing national payment rails may offer another way to improve cross border transfers without waiting for every participating country to deploy a mature digital currency.
More Capability Means More Governance At Every Layer
Malhotra did not present AI governance and BRICS payment connectivity as a single program, however combined they do expose a common operating issue. Inside a bank, more capable AI requires clearer ownership, controls and auditability. Between countries, more connected payment systems require agreements around access, standards, settlement, legal responsibility and risk.
Interoperability is useful only when participants know who is accountable when something fails. The same is true for AI performing more important financial work.
For Canadian readers, the comparison is timely. Canada's financial infrastructure is becoming more open through new payment system access, PSP supervision and the Real Time Rail, while banks are also deploying more AI. Different technologies are involved, but both require stronger operating controls as access and automation expand.
Talking Point
Financial infrastructure gets harder to govern as it becomes more capable and connected. The RBI's AI expectations and the BRICS payment discussions show that control, accountability and interoperability are becoming operating questions, not side issues for innovation teams.
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
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.
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.
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.
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.
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
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 complaintalleging 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?
Related NCFA Intelligence
Continue through the operating controls, supervisory questions and financial risks most closely connected to material AI deployment.
Canadian operating constraints around AI governance, outside providers, customer outcomes and financial stability.
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)
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.
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)
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.
Improving Customer Data Management
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.
Creating Personalized Customer Experiences
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.
Enhancing Customer Support Operations
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.
Supporting Better Business Decisions
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.
Increasing Automation Across Fintech Services
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.
Strengthening Security And Compliance
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.
Transforming The Future Of Customer Engagement
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
July 30, 2026 | NCFA Market Activity | Wealth Investing And Trading, Artificial Intelligence And Data, Identity Privacy And Data Governance
AI Portfolio Access With Controlled Trade Drafting Ahead
Founded1999
OriginToronto, Canada
FounderEdward Kholodenko
Company StagePlatform Expansion
On July 23, 2026, Questrade published beta documentation for a direct connection between selected Canadian investment accounts and AI agents. The Questrade MCP beta currently supports Claude and Claude Code. With the customer's permission, an agent can read approved account and market data, discuss the portfolio and draft an order for review.
Here is the important boundary. Trading permission is switched on separately, and Questrade requires the customer to approve the order before it is submitted. The agent can help get a trade ready. It cannot trade independently in the account.
What Questrade MCP Can Do
A general chatbot can discuss markets. Questrade MCP can work with the investor's authorized account data. Once connected, the agent can retrieve approved balances, positions, orders and market information, then combine that context with outside research.
Questrade's live documentation describes both read and write access, although "write" doesn't mean autonomous trading. The agent prepares the instruction. The customer sees the symbol, buy or sell direction, order type, quantity, price, estimated cost and commission, then approves it through Questrade. The agent can't skip that review or place, change or cancel the order on its own.
Customers choose the accounts and permissions. They can share data without allowing trade drafting, and they can revoke the connection through Questrade's API settings. There is an important catch.
Questrade says revocation stops future access but does not remove information already shared with the AI provider. Closing the connection and retrieving previously shared data are two different things.
The connection becomes more interesting beside Custom Indexing. Launched in June, the product uses templates, fractional shares and one click rebalancing across as many as 600 securities. Questrade now says an agent can help build a custom index too. A conversation can therefore produce a structured group of proposed orders, with the customer still deciding whether to proceed.
Questrade MCP Beta Arrives As CIRO Sets AI Expectations
The timing is hard to ignore. In March, CIRO guidance gave execution only dealers more room to provide tailored education, alerts and other decision support. The core restriction remains. These dealers cannot recommend a security.
Questrade MCP puts that boundary in front of a real customer. The agent can discuss actual holdings and turn a plain language request into a draft order. Questrade says the analysis, orders and custom indices come from the agent and are not its advice or recommendations. The customer is expected to check the work and approve the result.
The disclaimer doesn't settle every responsibility. Questrade still controls authentication, account permissions, the order screen and execution. CIRO's 2026 compliance report says examiners will ask how dealers use AI and test the controls around it. The IOSCO AI toolkit raises similar questions about accountability, data, monitoring and third party providers. Neither regulator has publicly approved Questrade MCP as a product.
Operators will want to see the working details. When does a prompt become an instruction? When might an answer sound like a recommendation? What happens if the agent uses stale information, mistakes a ticker or drafts the wrong order type? A clean record of the request, response, draft and final approval will be just as important as the chat experience.
How Questrade Compares With AI Trading Platforms
Questrade enters an active race. Interactive Brokers connected accounts to Claude, ChatGPT and Grok in June. Its customers can research markets and prepare instructions for equities, ETFs, options and futures, but they approve every trade. That is the closest public comparison to Questrade's current model.
Robinhood has gone further in the United States. Its Agentic Account gives a third party agent a dedicated budget and allows trades without direct approval each time. Robinhood warns that the agent can make mistakes, customers can lose their entire investment and the customer remains responsible for monitoring the account.
Questrade's version is closer to assisted execution. The agent can see the authorized account and prepare the action, while Questrade keeps the approval gate. It gives up some autonomy in exchange for a visible decision before each order.
The contest isn't limited to AI connectors. Wealthsimple and bank owned brokerages are already competing to become the main Canadian investment account. Questrade is assembling zero commission trading, real time fractional shares, Custom Indexing and agent access while its parent prepares Questbank. We don't yet know whether customers will use these products together or treat them as separate features.
What Makes Questrade Different In Summer 2026
Where it could work A customer can ask about a portfolio, research an idea and prepare an order without copying balances and tickers between several screens. Custom Indexing could make that especially useful for people managing many positions.
Where it could get messy The account data may be current while the outside information is incomplete. An agent can misunderstand an instruction or produce analysis that a customer hears as advice. The approval screen helps before execution, but it can't resolve every question involving data retention, explanation or customer understanding.
Questrade already has regulated execution, a large Canadian customer base and more than C$80 billion in assets under administration. The open question is usage. If customers repeatedly research and prepare orders through an agent, MCP becomes another front door to the brokerage. If they mostly ask for balances, it remains a convenient connector.
Talking Point
When an AI agent can read a portfolio and prepare a trade, which controls keep useful assistance from becoming an unintended order or advice the dealer isn't allowed to give?
NCFA Company Intelligence Snapshot
Questrade
How a Canadian online brokerage expanded from lower cost trading into banking, personalized portfolios and agent accessible investing
Last updated Jul 30, 2026
Company At A Glance
Founded1999 in Toronto by Edward Kholodenko
OwnershipPrivately held Questrade Financial Group
Core BusinessSelf directed brokerage, managed portfolios and related financial services
Regulated EntitiesQuestrade, Inc. is a CIRO investment dealer and CIPF member; Questrade Wealth Management is a registered portfolio manager, investment fund manager and exempt market dealer
AssetsQuestrade reports more than C$80B in assets under administration
Current ProductsSelf directed investing, Questwealth Portfolios, Questrade Pro, Custom Indexing and agent access through MCP
Bank ExpansionQuestbank received federal approval in 2025 and is preparing Canadian deposit and mortgage products
Current MCP AccessSelected beta customers using Claude or Claude Code
Business ModelTrading and options fees, spreads, interest, subscriptions, managed portfolio fees and other financial services
Key CompetitionWealthsimple, Interactive Brokers, bank owned brokerages and other Canadian investing platforms
Milestones
Select a milestone to follow how Questrade built the operating base behind its agent accessible brokerage
1Formation1999
2Zero CommissionsFeb 2025
3Questbank ApprovalNov 2025
4Custom IndexingJun 2026
5Agent AccessJul 2026
Milestone 1
Questrade Opens As A Canadian Online Brokerage (1999)
Edward Kholodenko founded Questrade in Toronto in 1999. The company built its early position by offering self directed investors a lower cost alternative to bank owned brokerages.
Bank BrokeragesPrice and digital access define the early contest
Additional Company Data
Questrade began as an independent non bank online brokerage
The company remains privately held and Canadian owned
Early products included securities and foreign currency investing
Questrade later reduced stock trading prices from $9.95 to $4.95 before reaching zero commissions
NCFA Perspective
The brokerage account created the regulated customer and execution base behind Questrade's later products. Agent access now enters through that same account relationship.
Milestone 2
Questrade Removes Stock And ETF Commissions (Feb 2025)
Questrade removed online trading commissions for Canadian and US listed stocks and ETFs in February 2025, then added real time fractional trading that April.
Company
Questrade, Inc.CIRO investment dealer
Stage
ScaleLower friction for account activity
Capital
PrivatePricing supported by platform economics
Markets
Canadian And US EquitiesStocks, ETFs and options
Customers
Retail And Active TradersLower entry and transaction costs
Competition
Low Cost PlatformsPricing becomes less distinctive across the market
Additional Company Data
Zero commission pricing went live in February 2025
The change covered Canadian and US listed equities and ETFs
Options were priced from $0.75 per contract when the new schedule launched
Questrade reported more than $50 billion in assets under administration at the time
NCFA Perspective
Once base commissions disappear, platforms need other reasons for customers to consolidate assets and remain active. Better tools, portfolio products and connected services become more important.
Milestone 3
Questbank Receives Federal Approval (Nov 2025)
After a six year application process, Questrade Financial Group received approval to begin operating Questbank. The group is preparing deposit and mortgage products on a new core banking platform.
Company
QuestbankFederally regulated bank within Questrade Financial Group
Stage
Market EntryBanking products in preparation
Capital
Group BackedBuilt from an established financial services base
Markets
Canadian BankingDeposits and mortgages are planned
Customers
Existing And New ClientsMillions of group customers were cited by its technology partner
Competition
Banks And FintechsA larger share of the financial relationship becomes available
Additional Company Data
Questbank received federal approval to begin operating in November 2025
Temenos Core SaaS was live for the bank by May 2026
The platform was configured for deposit and mortgage products aligned with OSFI requirements
The banking stack includes Temenos Core, Data Hub and its Extensibility Framework
NCFA Perspective
Banking can connect deposits, lending and investing inside the same group. It also raises the operating and regulatory requirements attached to data, resilience and customer trust.
Milestone 4
Questrade Launches Custom Indexing (Jun 2026)
Custom Indexing lets customers edit a template, own the underlying fractional shares and rebalance as many as 600 securities. Questrade says the product has no management fees or trading commissions, although foreign exchange and product costs can still apply.
Company
QuestradeBrokerage and portfolio tooling in one account
Stage
Product ExpansionPersonalized portfolio construction
Capital
Customer Funded AccountsMinimum investment begins at C$10
Markets
US Listed SecuritiesCanadian securities are planned
Customers
Self Directed Portfolio BuildersTFSA, RRSP, FHSA and cash accounts
Competition
ETFs And Direct IndexingCustomization and account control are the main distinctions
Additional Company Data
The minimum investment is C$10
Each index uses a dedicated TFSA, RRSP, FHSA or cash account
A drift above 10% triggers a notification and prepares the required rebalance orders
There are no portfolio management fees or trading commissions, although foreign exchange costs and ETF fees may apply
NCFA Perspective
Custom Indexing turns a portfolio idea into a coordinated set of orders. MCP gives customers a conversational way to research and prepare that structure.
Milestone 5
Questrade Connects Brokerage Accounts To AI Agents (Jul 2026)
Selected customers can connect Claude or Claude Code to approved Questrade account and market data. Trading permission can be enabled separately so the agent can draft an order for customer approval.
Company
QuestradeBroker controlled MCP connection
Stage
BetaSelected customer access
Capital
UndisclosedNo product revenue or adoption figures published
Markets
Canadian InvestingPortfolio data, market data and trade drafting
Customers
Selected Questrade ClientsClaude and Claude Code supported first
Competition
Agentic BrokeragesInteractive Brokers and Robinhood show two different control models
Additional Company Data
Read access retrieves approved data while write access prepares orders for customer approval
Questrade lists ChatGPT and Cursor support as coming soon
Connection requires Questrade app version 2.0.0 or later and Push Approval
Revoking access stops future sharing but does not remove data already sent to the AI provider
NCFA Perspective
The customer can now begin with a conversation and end with a reviewed order. Adoption and trust will show whether agent access becomes a regular brokerage interface.
Related NCFA Intelligence
Continue into the regulated AI, brokerage competition and banking developments most closely connected to Questrade.
Why controlled delegation, clear permissions and revocation matter when an agent can initiate a financial action.
Frequently Asked Questions About Questrade MCP
What is Questrade MCP?
Questrade MCP is a beta connection between a Questrade account and a supported AI agent. With permission, the agent can use approved account and market data and prepare an order for the customer to review.
Can Questrade MCP place a trade?
It can prepare an order, but Questrade requires the customer to confirm the trade before it goes through. The customer can also connect account data without enabling trading permission.
Which AI tools work with Questrade MCP?
Claude and Claude Code are supported in the current beta. Questrade lists ChatGPT and Cursor as coming soon. Setup requires version 2.0.0 or later of the Questrade app and Push Approval.
Is Questrade MCP the same as Questrade Flows?
No. MCP connects an outside AI agent to approved brokerage data and uses a Questrade confirmation before a trade goes through. Flows are separate conditional trading instructions offered through Questrade Pro.
What happens to shared data after access is revoked?
Revoking access stops the AI agent from receiving more information through Questrade. It does not remove data already shared with the AI provider.
Questrade MCP remains in beta, and supported agents, permissions and features may change. Product claims are attributed to Questrade and regulatory context is identified separately. This content is provided for informational purposes only and does not constitute investment, financial or legal advice.
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
July 29, 2026 | NCFA Insight | Artificial Intelligence And Data, Public Sector Policy And Industrial Strategy, Banking And Credit
AI Jobs, Inflation And Interest Rates
On July 29, 2026, the Bank of Canada released a working staff paper called Monetary Policy in an AI Driven Two Speed Economy raising a difficult possibility. AI could reduce jobs in some industries while the national inflation rate still looks calm.
The authors test this idea using a model with two industries. One adopts AI and needs fewer workers. The other continues operating near its limit. Lower interest rates can encourage spending and support jobs, but the same rate applies across the economy. A cut that helps the first industry can push up prices in the second.
The paper compares two hypothetical cases that produce the same increase in output:
When AI helps people do more work, the model needs a 1.48 percentage point rate cut to restore full employment
When AI takes over tasks previously done by people, the required cut rises to 3.34 points
These figures aren't forecasts or advice for the Bank of Canada. They show that replacing work creates a much larger employment challenge for monetary policy than helping workers become more productive.
The authors put the problem plainly:
"The apparent stability is cancellation, not balance."
Why Automation Requires A Larger Rate Cut
The paper separates two ways AI can affect work. The first is augmentation, where AI helps someone complete an existing job faster. The second is automation, where software or machines take over tasks that people were paid to perform. Companies will often use both in the same business, but the difference is important.
Even the first case reduces the need for labour in the model's short run. That may sound backwards. If employees become more productive, a company can produce the same amount with fewer hours. Prices and customer demand do not adjust immediately, so new orders do not replace those hours quickly enough. Automation has a larger effect because some tasks leave the workforce altogether.
To restore employment, the model lowers rates enough to increase spending. The larger cut needed after automation also sends more demand into the industry already running near capacity, where businesses respond by raising prices rather than producing much more. That is why the 3.34 point result is more than a larger version of the 1.48 point result. It carries a greater inflation cost.
For founders and investors, two AI projects can produce the same increase in output and still create very different businesses. A company that helps employees handle more customers may increase sales, hiring and margins together. A company that removes whole tasks may improve margins while cutting payroll and reducing demand for certain skills. The headline productivity number doesn't tell you which one is happening.
Stable Inflation Can Hide Industry Job Losses
When AI helps workers produce more, costs and prices can fall in the industries using it. A rate cut may then raise spending and prices elsewhere. The national average can look calm because the price changes cancel each other, even while AI exposed industries are losing jobs.
Automation produces a different result. The larger rate cut raises prices in both industries, so headline inflation reveals more of the strain. The comparison is that the same increase in output can create a different employment and inflation problem depending on whether AI supports paid work or replaces it.
Canada won't experience this evenly. Canada's AI productivity test found that adoption is already much higher in finance and insurance than across the business economy as a whole. Employment, wages, prices and AI use by industry may therefore tell policy makers more than one national average.
Retraining Reduces The Pressure On Interest Rates
The model improves when workers can reach industries that still need them. With easier job transfers, the required rate cut falls from 1.48 to 0.44 percentage points when AI helps workers. It falls from 3.34 to 1.05 points when AI replaces tasks. Retraining, recognized credentials, relocation support and faster hiring between industries can reduce the pressure placed on interest rates.
Investment can produce the opposite result. When money flows quickly into companies automating work, financing and equipment can become more expensive for other businesses. In that model scenario, the required rate cut rises from 3.34 to 4.09 percentage points. An AI investment boom can strengthen the companies buying the technology while adding costs for businesses competing for capital, infrastructure and skilled operators.
Interest rate cuts can also preserve jobs that automation has removed from a company's long term staffing needs. That may delay workers from reaching employers that still need them. Lower rates can buy time, but they can't retrain a worker, recognize a credential or help someone qualify for a growing occupation.
That changes what leaders should measure. Operators need to separate productivity gained through higher sales from savings gained through fewer paid hours. Investors should distinguish growth led margins from payroll led margins. Policy makers need industry level data on AI use, job openings, wages and prices early enough to see whether workers are reaching expanding sectors.
Talking Point
When AI raises output, how much comes from serving more customers and how much comes from removing paid work?
Related NCFA Intelligence
Continue through the Canadian policy, business and financial developments most closely connected to AI productivity and employment.
How permissions, reliable data and human review affect the use of AI inside financial firms.
Frequently Asked Questions About AI And Monetary Policy
Is this an official Bank of Canada forecast?
No. The figures come from hypothetical cases in an independent staff paper. They are not forecasts, rate recommendations or statements from the Bank's Governing Council.
Why does automation require a larger rate cut in the model?
Automation removes more paid work than AI that helps employees become productive. Restoring employment therefore requires a stronger increase in spending, which the model produces through a larger interest rate cut.
How can inflation remain stable while industries lose jobs?
AI can lower prices in industries where productivity rises while lower interest rates raise prices elsewhere. Those changes can cancel each other in the national average even as employment falls in certain industries.
What should Canada track as businesses adopt AI?
Industry level AI use, employment, job openings, wages and prices can show whether higher output is coming from growth, fewer paid hours or both. National inflation alone cannot provide that detail.
This article interprets independent Bank of Canada staff research. The paper uses hypothetical model scenarios. It is not an economic forecast, interest rate recommendation or Governing Council position. Information is current to July 29, 2026 and is provided for informational purposes only.
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
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. 
