Fintech Engineering Handbook
5 hours ago
- #fintech-engineering
- #software-patterns
- #financial-systems
- An introduction to fintech software engineering patterns that ensure money systems are trustworthy, with principles of no invented data, no lost data, and no trust.
- Detailed guidance on representing money, covering precision handling (floating-point, arbitrary precision, minor-units, rational numbers), rounding strategies, and currency handling (packing amounts with currency, no cross-currency arithmetic, controlled sets).
- Explanation of FX rates, emphasizing directionality, time relevance (current-time vs. value-date), transactional vs. reference rates, and the absence of a canonical rate.
- Overview of recording money using double-entry bookkeeping, where balances are derived from entries, accounts have types, and entries are immutable.
- Distinction between value time, booking time, and settlement time for transactions, highlighting the importance of recording all relevant timestamps.
- Importance of audit trails for regulatory compliance, capturing what happened, when, who triggered it, and why, with event sourcing as a principled approach.
- Emphasis on immutability in audit trails, with reversals and corrections handled through new compensating entries, and considerations for GDPR compliance.
- Patterns for executing money flows, including invariants enforcement, funds reservation to prevent race conditions, and handling overdrafts intentionally or unintentionally.
- Idempotency as essential in distributed systems to prevent duplicate processing, with strategies like explicit keys and handling errors.
- Full resumability for multi-step money flows, requiring persisted state machines and idempotent steps to recover from crashes.
- Guidelines for consuming external APIs defensively, including validating schemas, expecting failures, storing requests/responses, and aiming for provider redundancy.
- Safe handling of webhooks: not assuming ordering, validity, or delivery; acknowledging fast; persisting raw payloads; and verifying callers.
- Reliable notification patterns like the outbox pattern and change data capture (CDC) to ensure at-least-once delivery without data loss or invention.
- Reconciliation processes to align data across systems, addressing drift through cadence, matching algorithms, and explicit fixes.
- Controls and access management, including segregation of duties, four-eyes approval, least privilege, and audit trails for authorization changes.
- Change trail in software development lifecycle (SDLC) using source control, enforced reviews, and traceable deployments for auditability.
- Testing approaches tailored for money systems: property-based testing, invariant checks, generative idempotency testing, crash/resume injection, round-trip testing, golden testing, backward-compatibility testing, and testing in production.
- Appendices covering domain vocabulary (accounting, money/FX, transactions, payments, trading, custody/crypto, compliance) and resources for further learning.
- End-to-end flow examples: a crypto withdrawal (highlighting idempotency, funds reservation, compliance, broadcasting, finality, reconciliation), a card deposit (focusing on webhook distrust, clearing accounts, chargebacks), and an in-app conversion with cashback (emphasizing currency handling, rounding, and no invented/lost data).