Building Secure FinTech with Spec-Driven Development

Financial software challenges extend far beyond typical application development. Regulatory compliance starts with the obvious frameworks—GDPR for privacy, PCI DSS for payment processing, SOC 2 for data security—then extends into industry-specific regulations like Dodd-Frank, MiFID II, or Basel III depending on your market. Each framework demands extensive documentation, complete audit trails, and specific technical controls. The penalties for failure aren't warnings. Fines can exceed millions of dollars. Regulators can revoke your ability to operate entirely.

Transaction integrity introduces constraints that don't exist in typical web applications. Social media feeds can tolerate eventual consistency—updates appearing after a few seconds causes no harm. Financial transactions operate under different rules. They must be atomic, consistent, isolated, and durable. A race condition in payment processing doesn't create a bad user experience; it leads to double-charges, failed settlements, or regulatory violations. The distinction between a pending transaction and an authorized one carries legal and financial weight.

Security requirements exist in a different league entirely. Your system isn't just a potential target—it's a prime target. Defense-in-depth architecture becomes mandatory: encryption at rest and in transit, secure key management, real-time fraud detection, continuous threat monitoring. Unlike typical SaaS products where you might patch a security hole and move forward, financial systems require breach reporting to regulators and potentially millions of customers. The reputational damage compounds the regulatory penalties.

Auditability isn't optional. Every financial transaction must maintain a complete, immutable audit trail. When regulators request information—and they will—they might demand transaction histories spanning seven years or more. This capability can't be retrofitted. Your data model must support comprehensive audit trails from day one, capturing every state change, every decision point, every external API call in immutable logs.

High availability takes on new meaning when downtime directly costs money. A typical web application celebrating 99.9% uptime accepts over eight hours of downtime per year. Financial systems demand 99.99%+ uptime, requiring graceful degradation, automatic failover, and comprehensive disaster recovery. Your customers don't care that AWS experienced an outage—they care that their payment didn't process.

Financial workflows are fundamentally complex state machines. Transactions don't simply succeed or fail. They progress through states: pending, authorized, captured, settled. Each state transition has specific validation rules. What happens when authorization succeeds but capture fails? When a customer disputes an already-settled charge? These aren't hypothetical edge cases—they occur daily, and your system must handle them correctly every time.

Modern FinTech doesn't exist in isolation. Your stack integrates with payment processors like Stripe and Adyen, banking APIs like Plaid and Finicity, fraud detection services like Sift and Forter, compliance services like ComplyAdvantage. Each integration brings specific data formats, error handling requirements, rate limits, and operational quirks. A single failed webhook from a payment processor cascades into customer support nightmares when your system doesn't handle it correctly.

Spec-driven development for FinTech formalizes exactly the requirements regulators demand, but does so in ways that improve code quality rather than just lengthening compliance checklists.

Financial data models follow a fundamental principle: make illegal states unrepresentable. Rather than hoping developers remember to validate positive transaction amounts or valid currency codes, we encode constraints directly in the type system. FinTech projects begin with comprehensive type definitions representing your financial domain with precision. This work typically spans a week or more before any implementation code gets written.

Every field in transaction types becomes a compiler-enforced requirement. IP addresses for fraud detection become mandatory, not optional. Idempotency keys that prevent duplicate charges can't be skipped. Audit trail fields aren't extras that might be forgotten—they're structural requirements. PCI compliance gets enforced through data structure design: storing only last four digits, expiration dates, and tokenized references rather than full card numbers. This isn't a guideline—it's enforced by how the data itself is structured.

Financial workflows are state machines that demand formal specifications. Transactions progress through carefully choreographed state sequences where only certain transitions are valid. We make these rules explicit. A pending transaction can only become authorized or failed—it can't jump directly to settled. Authorized transactions can be captured or fail, but can't be refunded. Only captured or settled transactions permit refunds. Failed and refunded states are terminal. These rules become the foundation for all transaction processing logic. When LLMs generate code for state transitions, they're constrained by formal specifications that prevent invalid state changes.

API design embeds compliance requirements directly into contracts. Idempotency keys aren't buried in documentation developers might not read—they're required fields in request types. IP addresses and user agents needed for fraud detection aren't optional extras—they must be provided or code won't compile. Compliance transforms from a checklist into a type system guarantee.

Event sourcing provides immense power for financial systems, though it's often misunderstood. The core insight is straightforward: instead of storing current state, store every state change as an immutable event. For financial applications, this provides something invaluable—complete, tamper-proof audit trails that regulators value highly.

Consider an auditor's perspective. They don't just want to know account balance today. They want to see how it got there: every deposit, every withdrawal, every fee, traced through time. Event sourcing delivers this automatically. Account balances can be reconstructed at any historical point by replaying events. Double-entry bookkeeping receives natural support. Disaster recovery improves—corrupted read models can be rebuilt by replaying the event stream.

Double-entry bookkeeping, a practice surviving since 15th century Venice, remains relevant because it's mathematically sound. Every transaction creates at least two ledger entries summing to zero. Financial reconciliation becomes trivial. Balance drift—the nightmare of systems built on direct updates—becomes impossible.

Idempotency isn't optional for financial APIs. Network failures create inevitable scenarios: customers submit payments, networks drop, they retry. Or your API returns an error but the charge actually processed. Without proper handling, you create duplicate charges, angry customers, and regulatory violations. The pattern is straightforward: clients provide idempotency keys with each request, and results get cached. If the same key arrives twice, return the cached result instead of executing the operation again.

Fraud detection moves FinTech from defensive to proactive. You're not just protecting against system failures—you're protecting against adversaries actively trying to steal money. Real-time fraud detection requires ML pipelines analyzing transactions in milliseconds, flagging suspicious activity before charges complete. Transaction velocity, geographic anomalies, device fingerprinting, behavioral patterns—these detection rules get specified upfront as part of your fraud policy, then implemented with LLM assistance. You're not asking AI to invent fraud detection. You're asking it to implement your specific, well-defined fraud rules in code.

Financial systems must ensure atomicity: operations complete entirely or don't happen at all. You can't have transactions that charge customer cards but fail to record charges in your database, or vice versa.

Atomic transaction processing begins with idempotency checking. If a request has already been processed, return the existing result immediately. Transaction records get created in the database within a transaction block (the database kind, not the payment kind), ensuring both the transaction record and audit event are created atomically. The payment processor gets called outside the database transaction because external API calls can take seconds or time out, and holding database locks that long creates problems. If the processor call succeeds, update the transaction to authorized. If it fails, update it to failed. Either way, you have a complete record of what happened.

A fundamental rule of financial programming: never use floating-point numbers for money. This seems obvious but gets violated constantly, usually with disastrous results. Decimal numbers like 0.1 can't be represented exactly in binary floating-point. Arithmetic accumulates rounding errors. The solution is simple: always use integers representing the smallest currency unit. For USD, that's cents. For JPY, it's yen (which has no decimal subdivision). All math happens in integers, which is exact. Conversion to decimal only happens when displaying to users.

Database schemas should be the last line of defense, encoding compliance requirements at the structural level so application code bugs can't violate critical constraints. Databases can do more than store data—they can enforce invariants that protect your business.

Spec-driven development with LLM assistance lets you build FinTech products three to four times faster than traditional development while maintaining regulatory rigor. Clients typically launch MVPs in eight to twelve weeks instead of six to nine months. They pass compliance audits—PCI DSS, SOC 2—on first attempt. Production bugs related to money handling drop 60-80%. Documentation time decreases 70% because specifications become documentation.

Built-in compliance emerges naturally when specifications force you to document data flows, state transitions, and security controls upfront. You're creating compliance documentation as you build. When auditors ask how you prevent duplicate charges, you point to idempotency specifications and implementation.

Hiring becomes easier with comprehensive type definitions and specifications. New developers onboard in days instead of weeks. Types serve as living documentation that stays current automatically.

Confidence in making changes comes from TypeScript's compiler and LLM-assisted refactoring making evolution of financial systems safe. Adding a new transaction status means changing an enum and letting the type system flag every location needing updates.