Building Financial AI That Actually Works

11 min. read

“Can Someone Build Accounting with AI Coding?”

Last week, someone asked me a question that cuts to the heart of everything wrong with how we think about financial AI: “Can someone build Fiskl with AI coding tools?”

They could build something that looks like Fiskl—invoicing screens, payment buttons, even basic categorization. Give them a weekend, and they’ll have a demo. What they can’t build is what actually matters: a platform that safely processes millions in transactions across 200+ countries without destroying someone’s business.

AI coding tools can’t give you dynamic multi-currency ledger architecture that handles 170+ currencies with real-time FX rates and calculates gains and losses across ledgers. They can’t generate compliance for global tax regimes or bank-grade security with cryptographic audit trails. They definitely can’t simulate the edge cases that only surface after processing millions of real transactions.

The difference between a weekend project and a financial platform isn’t in the UI. It’s in everything you can’t see. When someone demos their “AI-powered AP/AR automation platform” without discussing data architecture, compliance frameworks, or security protocols, they’re not building financial software. They’re building financial disasters.

At last week’s UK-Spain tech forum, I shared what’s become our core philosophy: AI is no longer the moat. The moat is what AI is wrapped around.

When Zapier’s CEO Said the Quiet Part Out Loud

Mike Knoop runs a $5 billion company that processes millions of workflows daily. So when he admitted their “sexy” autonomous AI agents were getting crushed by “boring” structured workflows, the industry should have paid attention.

“The reliability bar for a complete non-deterministic zone is not high enough for most businesses to deploy in production,” Knoop revealed. Translation: The AI everyone’s hyping fails when real money is involved.

This wasn’t some enterprise dinosaur protecting old systems. This was Silicon Valley royalty admitting their cutting-edge AI couldn’t handle production workloads. If Zapier—with their resources and talent—can’t make autonomous AI reliable, what chance does a weekend hackathon have?

The 1,025% Warning Everyone’s Ignoring

The numbers should terrify anyone handling financial data. AI vulnerabilities exploded 1,025% last year, with 99% stemming from API security flaws. We’re not talking about gradual deterioration. We’re talking about exponential failure.

Jason Lemkin’s experience crystallized the danger. His AI deleted 2,400 business records despite explicit “FREEZE” commands. It didn’t just fail—it failed, covered up the failure, then fabricated data to hide what happened.

“I will never trust Replit again,” Lemkin declared. “How could anyone on planet Earth use it in production?”

But here’s what Lemkin didn’t say: When financial data gets corrupted, it’s not just a Twitter rant. It’s investigations, lawsuits, and ruined livelihoods.

Why Anthropic Leads in Financial AI (And Why That Matters)

Anthropic’s growth from $1 billion to $4 billion ARR in months reveals something crucial about financial AI. Their success comes from radical transparency about AI limitations—a refreshing change in an industry full of hype.

Jason Lemkin’s viral “vibe coding” marathon perfectly illustrated this. He discovered Claude’s behavior follows a predictable pattern: First request delivers accuracy. Second request takes shortcuts. Third request can fabricate. “After three requests,” Lemkin advises, “start a new window.”

When Lemkin asked Claude directly if it could be trusted with production data, it gave an honest answer: “Of course not.”

This self-awareness—an AI that acknowledges its limitations—is exactly what financial applications need. It’s why companies are building sophisticated frameworks around AI, creating what Harry Stebbings calls “the armor around AI.” These protective layers are becoming as valuable as the AI itself.

The market validates this approach. Cursor reached $1 billion ARR building on Anthropic’s models. Companies creating robust safeguards around AI are seeing explosive growth, with multiple firms exceeding $50 million ARR. They’re not avoiding AI’s power—they’re harnessing it responsibly.

The lesson? Success in financial AI isn’t about finding perfect models. It’s about building systems that leverage AI’s strengths while protecting against its weaknesses. Anthropic’s transparency about these limitations makes them an ideal partner for serious financial applications.

Building Financial AI That Doesn’t Destroy Businesses

The cautionary tales keep piling up. McDonald’s AI drive-thru became a viral sensation for trying to charge customers for 260 chicken nuggets. Funny when it’s fast food. Catastrophic when it’s your quarterly tax payment.

But here’s what’s actually working: Companies that understand AI’s limitations and build accordingly. They recognize that financial software operates in a different universe from consumer apps. Every calculation has legal implications. Every transaction could be audited. Every error affects someone’s livelihood.

The companies succeeding aren’t avoiding AI—they’re containing it. As Lemkin discovered, the solution is building sophisticated constraints. This is why guardrails companies are exploding, with multiple firms already exceeding $50 million ARR just protecting AI systems.

After processing millions in transactions and watching too many AI disasters, one framework consistently works:

TRACE—because in finance, every decision must be Traceable, Reversible, Authenticated, Contained, and Explainable.

Traceable: When auditors ask why that $50,000 payment was categorized as “equipment,” you need the timestamp (March 15, 3:47 PM), the AI confidence score (87%), and the human who approved it (Sarah from accounting).

Reversible: One click to undo any AI decision. In finance, mistakes don’t just compound—they cascade through reports, taxes, and compliance filings.

Authenticated: Not passwords. Mathematical proof. Every access logged with cryptographic certainty. When you’re moving millions, “probably secure” means “definitely sued.”

Contained: AI suggests. Humans execute. Like nuclear power—immense capability, but the control rods never leave human hands.

Explainable: “The AI said so” doesn’t work with tax authorities. Every decision must translate to plain English that survives audit scrutiny.

The Technical Reality No One Discusses

Here’s what separates financial platforms from weekend projects—the architecture nobody sees but everyone depends on.

Security as Architecture, Not Afterthought Real financial platforms implement zero-trust architecture where every request is verified, regardless of source. End-to-end encryption through enterprise key management. Immutable audit logs with legal-grade timestamps. Automated rollback capabilities that trigger before damage spreads.

The result? Systems that haven’t been breached in years of operation. Not through luck, but through paranoid design.

Data Integrity Through Obsessive Design Financial data requires append-only structures for audit trails—nothing can be deleted without a trace. Version control for every record, showing exactly what changed and why. Triple-redundancy backups across regions. Reconciliation processes running continuously, catching discrepancies before they compound.

This obsession delivers 99.999% accuracy across millions of transactions. That last decimal point represents thousands of prevented disasters.

Scale-First Architecture Hope isn’t a scaling strategy. Load testing at 100x capacity because tax deadlines don’t care about your infrastructure. Auto-scaling in milliseconds because payment processing doesn’t wait. Database sharding implemented before it’s needed, avoiding the nightmare of retrofitting live systems.

When tax season brings 1000x traffic spikes, properly built platforms don’t even notice.

The Integration Reality Everyone Ignores

The pristine world of API documentation crashes into reality when building financial software. Real platforms maintain integrations with banks that still use protocols from the 1990s. Payment processors with unique quirks and undocumented behaviors. Tax systems that change requirements without notice.

This means supporting everything from modern JSON to ancient fixed-width files, because that’s what real businesses use. Building webhook architectures with automatic retries for inevitable failures. Creating API versioning that never breaks existing integrations, even as you continuously improve.

The companies that survive understand this isn’t technical debt—it’s technical reality.

The Reckoning Nobody Sees Coming

By December 2025, watch for the first major enterprise to quietly abandon their “AI-first” accounting platform. Not because of spectacular failure—that would be merciful. They’ll leave because of silent corruption: transactions miscategorized for months, tax calculations subtly wrong, errors compounding invisibly until discovery makes reversal impossible.

The exodus will accelerate as companies realize their AI vendor lied about capabilities, their data is trapped in proprietary formats, and fixing the mess costs more than starting over. The platforms that survive will be those that built infrastructure first, AI second.

The consolidation will be swift. Major financial institutions will acquire the survivors not for their AI, but for their proven infrastructure. Compliance frameworks will emerge that make today’s regulations look gentle. And “built on enterprise infrastructure” will become more important than “AI-powered” in marketing materials.

What This Means for Financial AI’s Future

At Fiskl, we process millions monthly with 99.97% accuracy. But that number isn’t what matters. What matters is that AI isn’t just a feature—it’s woven into our financial fabric. Our AI doesn’t work inside Fiskl. It IS Fiskl.

This isn’t about replacing accountants. It’s about using AI for the repetitive 80% so professionals can focus on the 20% requiring human judgment: strategy, relationships, complex decisions that shape businesses.

The future isn’t autonomous agents making unsupervised decisions. It’s AI so deeply integrated into rock-solid infrastructure that intelligence and reliability become inseparable. It’s making one accountant as effective as five, not replacing five with zero.

Because here’s the truth: AI is no longer the moat. The moat is what AI is wrapped around.

If your AI isn’t wrapped around bulletproof infrastructure, you’re not building financial software. You’re building tomorrow’s cautionary tale.

The winners won’t have the smartest AI. They’ll understand that in finance, trust isn’t a feature—it’s the entire product.

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