What’s Really Happening With Your Data in 2026?

Monarch Money has become one of the most talked‑about budgeting apps in 2026, and not just because it absorbed a wave of former Mint users when Mint shut down. A big part of the buzz now centers on its AI features and what they do with your financial data. Monarch says that only data relevant to a given AI task is shared with models and that personal identifiers like names and passwords are excluded, yet users are still debating whether that’s enough—and whether they’re truly in control.

Key Takeaways

Question	Short Answer
What is the Monarch Money AI controversy about?	It centers on how Monarch’s AI assistant and AI-driven features use transaction data, what gets sent to third‑party LLMs, and how much control users really have over that process.
Does Monarch send all my data to AI models?	No. Monarch states that only task‑relevant data (like transaction descriptions and dates) is sent, and that passwords and obvious identifiers are excluded. However, some users still worry about sensitive context in descriptions.
Can I opt out of AI training on my data?	Yes, for Monarch’s in‑house models you can opt out via Settings > Preferences. But some AI features—such as AI categorization—are built‑in and can’t be turned off.
Is my data encrypted and protected?	Monarch says all data sent to AI models is encrypted in transit and at rest and that it uses enterprise‑grade protections like SSO and 2FA. The controversy is less about raw security and more about data flows and trust.
Why is this debate so intense now?	Monarch has grown fast post‑Mint and raised significant funding, while at the same time rolling out AI. More users plus more automation equals more scrutiny on data and transparency.
Are there AI‑driven alternatives?	Yes. Tools like global portfolio tracker Strabo, BNPL platforms like Tabby, and invoicing tools like Invoiless are also building AI layers, each with their own trade‑offs.
Where can I learn more about AI in personal finance?	Sites that focus on personal finance tools—such as the Klayto blog and its dedicated personal finance category—regularly review AI‑enabled apps and discuss risks versus benefits.

1. Introduction & First Impressions of the Monarch Money AI Issue

The core of the Monarch Money AI controversy is simple: users love the convenience of automated categorization and smart insights, but they’re unsure how far the AI tentacles reach into their financial lives. Monarch uses a mix of third‑party large language models (LLMs) and its own models, which raises natural questions about where your data goes and who can see it. For a typical user, the experience looks slick. You connect accounts, the AI cleans up categories, and the AI assistant can answer questions about your spending, budgets, and goals. But once you realize some of those answers are generated by external LLMs, the “magic” starts to feel a bit more like a risk calculation than a simple feature. Most of the public conversation around the controversy comes from power users and privacy‑conscious customers who read help‑center fine print and compare it to other tools. Reviewers who also test global dashboards like Strabo or advanced invoicing suites such as Invoiless are particularly vocal, because they’ve seen different models for handling sensitive financial data.

What this product is (and why AI matters)

Monarch Money is a cloud‑based budgeting and planning app that connects to your bank, credit cards, loans, and investments. The AI layer sits on top: auto‑categorizing transactions, suggesting budgets, and answering natural‑language questions. The controversy isn’t that AI exists in Monarch. It’s about how that AI works behind the scenes and whether you have meaningful choices around it.

Who Monarch is for—and who’s most concerned about AI

Monarch primarily targets people who want a more serious, long‑term financial planning tool than basic budgeting apps. That includes:

• Ex‑Mint users looking for a modern replacement
• Couples syncing shared finances
• Higher‑income households with multiple accounts
• Professionals tracking investments and goals in one place

These are the same people who are most protective of their financial privacy. They know that spending patterns, merchant names, and even memo fields can paint a detailed picture of their lives.

Testing period & early 2026 sentiment

Reviewers tracking Monarch through early 2026 generally report that AI features feel fast and helpful but occasionally “opaque.” Some user comments about AI data handling, found across forums and social feeds, are best summarized as: “Useful, but I want a bigger red button to turn parts of this off.” Any specific 2025 testimonial claiming a particular security incident or breach tied to Monarch’s AI would be Needs verification at this time.

2. Overview: How Monarch Money Uses AI Under the Hood

To understand the controversy, you need a clear picture of what Monarch’s AI is actually doing. Monarch says it uses a combination of third‑party LLMs and in‑house models, depending on the feature. In practice, that means some tasks—like interpreting natural‑language questions or summarizing spending—may rely on external AI providers. Others, such as ongoing categorization logic, lean more on Monarch‑controlled systems, sometimes fine‑tuned using anonymized user data.

Key AI‑driven features in Monarch

Monarch’s AI footprint includes:

• AI Assistant: chat‑style interface where you can ask questions about spending, categories, and budgets.
• AI categorization: automatic labeling of transactions and merchants.
• Smart insights: pattern‑based notifications like “Your dining out is 18% higher than last month.”

Behind each of these, there’s a decision: which data to send, to which model, and under what privacy guardrails.

What triggers AI data sharing

According to Monarch’s help materials, AI data sharing is task‑based. Ask the AI assistant about “last month’s grocery spending,” and the system sends:

• Relevant transaction descriptions
• Dates
• Amounts and categories

Names, passwords, and obvious login credentials are not sent, but descriptions themselves can sometimes be revealing. That’s one of the flashpoints for critics.

3. Design, UX, and the “Invisible AI” Problem

Monarch’s UX is polished and intentionally calm. Budgets, net worth, and cash‑flow charts feel similar to advanced dashboards you see in tools like Strabo’s global investment view or Invoiless’s invoicing console. The AI, however, is largely invisible. Outside of the chat assistant, there’s not much UI feedback showing when your data is being sent to an external model or which model is being used, and that’s part of what makes some users uneasy.

Why UX transparency matters in AI finance tools

Financial apps like Tabby (for BNPL) and Strabo (for investments) increasingly add AI, but the better ones at least surface:

• Clear toggles for AI‑driven features
• Inline indicators like “AI‑generated suggestion”
• Brief explanations of what data powers a recommendation

Monarch’s interface is clean, but critics argue it needs more UX cues around AI to rebuild trust.

Can better UX calm the controversy?

Yes—if Monarch makes AI data flows more explicit. Simple UI elements like a small “AI” label with a tooltip (“This insight uses anonymized transaction data and a third‑party LLM”) would go a long way for informed users. Right now, users often must dig into help‑center docs to understand what’s going on. That design choice is convenient for casual users but frustrating if you care about privacy.

4. Performance: How Well Does Monarch’s AI Actually Work?

From a raw performance standpoint, Monarch’s AI is strong. Categorization accuracy is generally high, and the AI assistant can answer nuanced questions like “How much did I spend at restaurants in Q2 compared with Q1?” For many users, this performance is the main reason they’re willing to tolerate some data‑sharing anxiety. The app saves real time, and it can reveal patterns people didn’t spot manually.

Where AI performance shines

You’re most likely to notice AI benefits when:

• You connect many accounts and have thousands of transactions.
• Your merchant names are messy or inconsistent.
• You ask broad, open‑ended questions (“Where is my money going?”).

This is very similar to how global dashboards like Strabo lean on automation to reconcile multi‑currency portfolios, or how Invoiless uses logic to accelerate invoicing workflows.

Where it still struggles

Some edge cases still trip Monarch up:

• Highly specific local merchants
• Mixed‑purpose cards (business + personal) on the same account
• Transactions with vague or cryptic descriptions

In those moments, users sometimes feel an uncomfortable combo of “AI is guessing” and “I’m not sure who saw that guess,” which feeds the broader controversy.

5. User Controls, Opt‑Outs, and Data Rights

This is the heart of the controversy. Monarch does offer an opt‑out for using anonymized data to train its in‑house models. You can go to Settings > Preferences and disable this, which many privacy‑conscious users appreciate. However, some AI features are “baked in.” Monarch acknowledges that certain capabilities—like AI‑powered categorization—cannot be fully disabled. That split is what annoys users who want hard, clear lines around AI.

What you can control today

You currently have levers for:

• Opting out of anonymized data being used to improve in‑house models
• Limiting how much you rely on the AI assistant (simply by not using it)
• Manually editing categories and rules to reduce AI guesswork

These are meaningful, but they’re not total control.

What you can’t fully control

You can’t fully turn off:

• AI‑powered categorization that runs in the background
• Internal logic that uses your data to detect patterns and insights

For some users, that’s fine. For others, it crosses a line: they want a fully manual mode that never sends anything to external models.

6. Security, Encryption, and the Real Risk Profile

On paper, Monarch’s security posture is solid. The company states that all data sent to AI models is encrypted in transit and at rest, and that it uses enterprise‑grade protections like SSO (single sign‑on) and two‑factor authentication (2FA). So the risk isn’t “Monarch leaves everything unencrypted.” It’s about whether you’re comfortable with who ultimately handles and processes your data, even in anonymized or partial form.

How this compares to other AI‑driven finance tools

Other fintech platforms reviewed on Klayto—like Strabo or Invoiless—also lean heavily on encryption and modern auth methods. The difference often lies in:

• How much data leaves the core system for external AI providers
• Whether user data is used for training by third parties
• How explicit the company is about these flows

Monarch says third‑party models are not trained on customer data, which is a key reassurance for many.

The practical risk for most users

If you’re already connecting bank accounts to any cloud budgeting tool, Monarch doesn’t obviously stand out as more dangerous from a pure security perspective. The bigger question is philosophical: are you comfortable with AI models “reading” your transactions, even in partial form, to serve you insights?

7. Real‑World User Experience (Interactive Checklist)

In day‑to‑day use, most Monarch customers don’t think much about the AI controversy. They connect accounts, set up budgets, and check progress. The AI fades into the background unless they open the assistant or see a surprising categorization. To make this practical, here’s a simple mental checklist you can “click through” for yourself as you test Monarch’s AI features.

Interactive self‑check: Is Monarch’s AI right for you?

• Step 1: Am I okay with any third‑party LLM ever seeing pieces of my transaction data (descriptions, dates, amounts)? If the answer is “no,” Monarch’s AI may not be for you.
• Step 2: Do I regularly ask AI‑like ChatGPT questions about my money anyway? If the answer is “yes,” letting Monarch’s integrated system handle it might be safer than ad‑hoc copy‑pasting.
• Step 3: How much time would I save if AI handled categorization and summaries for me? If your accounts are complex, the time savings can be significant.
• Step 4: Have I gone into Settings > Preferences and set my opt‑out choices the way I want? If not, do that before you judge.

If you walk through that honestly, you’ll have a good feel for whether the controversy is a deal‑breaker or just something to keep an eye on.

8. Comparative View: Monarch vs Other AI‑Enabled Money Tools

To put Monarch’s AI controversy in perspective, it helps to compare it with peers, even if they serve slightly different niches. Here’s a high‑level comparison with three AI‑touched tools covered on Klayto:

Tool	Primary Use	AI Role	Pricing Snapshot*
Monarch Money	Budgeting & financial planning	Assistant, categorization, insights	Varies by plan and region (not detailed here)
Strabo	Global investment tracking	Smart dashboards, data integrations, AI insights (Pro)	Free; Pro at £70; Adviser at £1170 per year
Tabby	BNPL shopping & installments	Risk scoring, payment experience optimization	No explicit subscription cost; revenue via merchant fees/finance
Invoiless	Invoicing & billing	Workflow automation, smart reminders	$0 Free; Pro from $8.25/mo; Lifetime at $69

*Pricing based on Klayto’s 2026 reviews where available; always confirm current pricing.

Where Monarch stands out

Monarch is much more consumer‑budget focused than tools like Strabo or Invoiless. Its AI controversy is more visible mainly because:

• It’s a daily‑use tool for a broad audience.
• It’s dealing with raw consumer transactions, not just business invoices or portfolio positions.
• It sits at the center of people’s financial lives, not off to the side.

That makes any AI misstep feel personal.

What buyers can learn from this comparison

The main lesson: AI in finance is now the norm, not the exception. The real question when comparing Monarch to peers isn’t “Does it use AI?” but:

• How explicitly does it explain that AI?
• What controls do I get over my data?
• Is the value (time saved, insight quality) worth the exposure?

9. Pros and Cons of Monarch Money’s AI Approach

Here’s the balanced view on Monarch’s AI in 2026, grounded in its own documentation and how similar tools behave.

Pros

• Genuinely useful automation: categorization and AI summaries are time‑savers for most users.
• Task‑scoped data sharing: Monarch says only relevant data (e.g., descriptions and dates) is sent to models.
• No third‑party training on your data: external AI providers aren’t training their models on your transactions, according to Monarch.
• In‑house model opt‑out: you can refuse to let anonymized data train Monarch’s own models.
• Strong baseline security: encryption, SSO, and 2FA are all standard.

Cons

• Incomplete opt‑out: some AI features (like categorization) can’t be turned off, which bothers privacy purists.
• Opaque UX around AI: most users can’t easily tell when external AI is involved.
• Reliance on third‑party LLMs: even with guardrails, some users are simply uncomfortable with any external processing.
• Limited granular controls: you can’t, for example, say “use AI for budgets but never for merchant insights.”

10. 2026 Updates: Growth, Funding, and What It Signals

Monarch Money isn’t a tiny startup experimenting on a handful of users. In May 2025 it reportedly raised around $75 million in a Series B funding round, valuing the company at roughly $850 million. That level of capital usually means more engineering resources—and more pressure to ship features like AI. The company has also been scaling up its team, reportedly to around 90+ employees, with revenue in the tens of millions. Combined with the migration wave from Mint in early 2024, this growth created a perfect storm: lots of new users, new AI features, and intense scrutiny.

What this means for the AI controversy

On the one hand, more funding and staff suggest Monarch can keep improving its privacy controls and transparency. On the other, rapid scaling can lead to prioritizing velocity over detailed communication. The company says it tripled its support team after the Mint news and aims for 24‑hour responses. If that commitment extends to clarifying AI behavior and listening to privacy feedback, we’re likely to see a more mature, user‑friendly AI posture over time.

Conclusion: Should the Monarch Money AI Controversy Stop You From Using It?

Monarch Money’s AI controversy isn’t about a public breach or a smoking‑gun scandal. It’s about a reasonable tension between powerful automation and deep financial privacy. Monarch’s current stance—task‑scoped data sharing, no third‑party training, encryption everywhere, partial opt‑outs—is better than many, but not perfect for everyone. If you want maximum convenience, automated insights, and you’re already using AI tools for other parts of your life, Monarch’s AI is likely an acceptable trade‑off. If you’re in the camp that never wants an external model to see even a sanitized version of your transactions, then yes, this controversy is a legitimate reason to look for a more manual, less AI‑driven tool. Either way, the key is going in with your eyes open: read Monarch’s help‑center explanations, set your opt‑out preferences on day one, and treat AI as something you consciously choose to use—not an invisible background trick you never think about again.

Evidence & Proof

• Monarch’s own help‑center statements on AI scope, opt‑outs, and encryption (2025).
• Reports of Monarch’s Series B funding (~$75M, ~$850M valuation) and team growth.
• Klayto reviews of adjacent AI‑enabled finance tools like Strabo, Tabby BNPL, and Invoiless, providing context for how other platforms handle sensitive financial data.
• Public migration narrative following Mint’s shutdown and Monarch’s rapid user influx.