Kraken is rolling out a new set of AI-powered financial tools inside its mobile app, aiming to shift the experience from manual trading workflows to goal-based investing guidance. The update is built around users setting financial objectives and preferences up front—so the app can tailor recommendations and the on-screen experience around what they are trying to achieve.
In its announcement, Kraken described the core system as “financial intelligence” that monitors markets, spots investment opportunities, and suggests trades. Importantly, it positions the technology as decision support rather than fully automated execution: every recommendation must be reviewed and approved by the user before any transaction is placed.
Key takeaways
- Kraken’s mobile update uses AI to align trading recommendations with user-defined goals like home purchases, retirement savings, and emergency funds.
- The “financial intelligence” layer recommends trades and updates, but Kraken says it does not execute transactions autonomously.
- According to CNBC, Kraken also incorporates user risk tolerance, funding preferences, and financial profile to generate reviewable portfolio suggestions.
- Kraken’s launch follows broader moves across crypto by exchanges and fintech firms adding AI agents and conversational workflows, including tools that still require user approval.
Goal-based investing with user control
Kraken’s approach starts with how users set expectations. Rather than forcing customers to navigate complex trading screens, the redesigned app prompts them to specify financial goals and preferences. From there, Kraken says the system tailors the interface and recommendations to those objectives.
Examples Kraken highlighted include buying a home, saving for retirement, and building an emergency fund. The emphasis on goals matters because it potentially reframes investing from “choose an asset and execute” to “define an outcome and get guidance that fits it,” which could be especially relevant for less experienced users who struggle to translate long-term needs into day-to-day trading decisions.
Kraken also clarified the operational model of its AI tooling. While it continuously monitors markets and identifies opportunities, it requires a user’s explicit approval before any trade is submitted. That distinction—recommendation versus execution—may help address a key concern with AI in finance: reducing the risk of unintended trades while still offering more timely, personalized guidance than static forms or generic alerts.
How the app generates suggestions
CNBC reported that Kraken’s app uses information from its market monitoring system alongside user-specific inputs such as risk tolerance, funding preferences, and financial profile to create a suggested portfolio. The user is then able to review and adjust the proposed allocation before investing.
After a user invests, the app shifts from one-time advice to ongoing support. It provides personalized portfolio updates and additional investment suggestions tailored to what the user holds, again with the expectation that the user remains in control of whether to proceed with any action.
In an interview with CNBC, Kraken chief data officer Kamo Asatryan framed the purpose of the technology as narrowing the gap between retail users and the exchange’s most active traders. He said the system is designed to deliver everyday investors “the same market awareness” as high-frequency traders—using continuous monitoring and opportunity identification paired with recommendations that can be expressed in plain language.
“[T]here’s an opportunity for everyday people to become high-frequency traders and do so using plain English,” Asatryan said, according to CNBC.
Kraken joins a wider push for AI agents in crypto
Kraken’s rollout lands as exchanges and fintech platforms increasingly explore AI-driven interfaces that let users analyze markets, manage portfolios, and interact via conversation-like flows. The broader theme across the industry is not just automation, but “agentic” assistance—systems that can interpret a user’s intent, prepare actions, and streamline the steps between analysis and execution.
Earlier this year, OKX launched a beta marketplace where AI agents can transact autonomously, complete onchain tasks, and build blockchain-based reputations. In the same timeframe, Coinbase introduced a tool described as enabling AI agents to make payments and trade cryptocurrencies on behalf of users using its x402 payments protocol.
Activity reports suggest adoption is already taking shape. Chainalysis reported last month that agentic payment activity on Coinbase’s Base network surpassed 100 million transactions. While Chainalysis said transaction growth has stabilized, it also noted that higher-value transfers have become more common—an indicator that usage may be maturing beyond initial small-scale experiments, even if the overall pace of transactions is less explosive than earlier periods.
Other platforms are also leaning into conversational investing and AI-assisted trading workflows. On Friday, Revolut launched an upgrade to its Revolut X exchange that allows customers to connect AI assistants—including Claude, Gemini, Cursor, and OpenClaw—to analyze markets, backtest trading strategies, and place orders through natural-language prompts. Like Kraken’s model, Revolut’s approach requires users to review and approve every trade before execution.
The key question: when does “help” become automation?
What differentiates Kraken’s update from the most autonomous agent narratives is the explicit requirement for user approval. That control layer may be a practical compromise: it can preserve trust and reduce operational risk, while still giving users a more personalized and potentially faster way to evaluate opportunities.
At the same time, the industry is clearly moving toward workflows where users express intent in simpler terms and software translates that intent into trading and portfolio actions. For investors and traders, the near-term watch items are likely to be transparency and usability: how recommendations are explained, how risk factors are reflected, and how consistently the app updates guidance after portfolio changes.
For builders, the broader implication is that AI agents in finance are increasingly being packaged as user-facing product layers rather than back-office experiments—often paired with human approval gates that keep execution responsibility with the customer.
As Kraken begins deploying these tools in its mobile app, the next phase will likely reveal how quickly users adopt goal-based investing workflows, and whether the guidance becomes meaningfully better aligned with individual outcomes over time. Readers should also watch for additional details on how Kraken communicates recommendation logic and manages edge cases where market conditions or user preferences conflict.
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