You’ve probably lived this sprint-from-hell before.
Product wants “self-serve revenue dashboards.” Sales wants “real-time MRR.” Finance wants “clean numbers by Monday.” You, naturally, get the request at 4:45 p.m. on Friday… and somehow it always ends with yet another CSV export from your app and a forest of spreadsheets on someone’s shared drive.
The irony: your product already emits almost everything finance needs. It’s just trapped in logs, databases, and SaaS tools that were never designed to be a single source of truth.
This post is about fixing that from the engineering side. Not by becoming an accountant, but by treating finance as another downstream consumer of your event stream—and wiring your app into the finance stack in a way that’s repeatable, testable, and boring enough to trust at month-end close.
Why your finance team is drowning in exports (and why you should care)
Most engineering teams underestimate how much time finance spends wrestling with data instead of analyzing it.
According to Harvard Business Review Analytic Services research, the top challenges for finance teams around data are preparing, reconciling, and accessing high volumes of information – not doing sexy machine learning on top of it. A recent Deloitte finance trends survey finds that most finance departments are experimenting with AI, but relatively few feel they’re getting clear, measurable value from those investments yet.
If you zoom into your own company, “finance cares about our data” usually shows up as:
● Endless “one-off” exports for special analyses
● Ad hoc filters (“exclude refunds but include credits if…”) buried in someone’s Excel
● Conflicting numbers between dashboards, billing, and the general ledger
That last one is scary. When there isn’t a single source of truth for financial data, organizations end up with inconsistent metrics in board decks, investor updates, and sometimes even regulatory filings, which is exactly the kind of risk your CFO loses sleep over.
From the engineering side, the root problem is simple: the data model your app uses to ship features is rarely the model finance needs to close the books. You don’t need to smash those worlds together—but you do need a deliberate bridge between them.
Think in events: design a finance-ready stream instead of another export
The easiest way to stop shipping spreadsheets is to stop thinking in “reports” and start thinking in events.
Step 1: Define the events finance actually cares about
Instead of dumping your internal models, design a small “finance event schema” that other services (and finance tools) can rely on. For most SaaS products, you’ll want events like:
● customer_created
● subscription_started, subscription_upgraded, subscription_cancelled
● invoice_issued, invoice_paid, invoice_voided
● usage_recorded (for metered billing)
● refund_issued, credit_applied
Each event should be:
● Immutable – if something changes, emit a new event; don’t “edit” the history.
● Idempotent – include stable IDs so consumers can deduplicate.
● Time-stamped – both “event time” (when it happened) and “ingestion time” (when you emitted it).
A minimal JSON-ish shape might look like:
{
"event_id": "evt_123",
"event_type": "invoice_paid",
"occurred_at": "2025-11-18T14:03:00Z",
" ingested_at": "2025-11-18T14:03:05Z",
"customer_id": "cus_456",
"currency": "USD",
"amount_minor": 19900,
"invoice_id": "inv_789",
"metadata": {
"plan_code": "pro_monthly",
"source": "billing_service"
}
}
The goal isn’t perfection; it’s consistency. Once you’ve got a stable schema, you can keep your API and UI free to evolve, swap billing providers with less pain, and feed finance tools, BI, and AI from the same stream.
If you’re building this on top of HTTP APIs, you’ll quickly appreciate solid pagination and reliability patterns; guides like API pagination best practices are surprisingly relevant when you start backfilling historical events from your existing database.
Step 2: Make “finance events” a first-class citizen in your architecture
There are a few patterns that work well in practice:
● Outbox pattern – write domain changes and outgoing events in the same transaction, then have a worker publish from the outbox to Kafka/SNS/etc. This keeps your finance stream consistent with your primary DB.
● Dedicated “finance-events” service – other services call it with domain events; it validates, normalizes, and publishes them.
● Change Data Capture (CDC) – for legacy systems, capture changes from the DB transaction log and transform into finance events downstream.
This doesn’t have to be hyper-enterprise. Even a small Node or Python service that reads from a queue, validates against a JSON Schema, and writes to a durable log gives finance something way better than “March_exports_final_v7.xlsx”.
Wiring into the finance stack: from events to ledgers, with AI in the loop
Once you’ve got a clean event stream, you need somewhere for it to land.
Finance typically lives in a constellation of tools: ERP, general ledger, billing, and now AI-driven analysis platforms. Modern guidance on “single source of truth” emphasizes a centralized, trusted repository where all critical data is integrated and stored so every team works off the same numbers; a practical example is this guide to financial data automation that frames the general ledger as the backbone for consistent reporting.
Here’s a pragmatic way to wire your app into that world.
- Normalize and enrich before finance sees it Create a small “finance ingestion” service that:
Consumes raw events from your stream.
Validates them (schema, required fields, allowed currencies).
Enriches them with:
○ Customer metadata (segment, region)
○ Product metadata (plan family, packaging version)
○ FX rates if you bill in multiple currencies
Think of this as the adapter between product land and the general ledger. This layer is a good candidate for a separate codebase with its own tests, especially around currency handling and edge cases like partial refunds.
- Land events in a read-friendly store From your ingestion service, write into: ● A time-series or columnar store for analytics (e.g., BigQuery, ClickHouse, or your warehouse of choice).
● A staging table or queue that’s designed to feed the ledger/ERP.
Finance will use your warehouse for ad hoc analysis and dashboards. Ledger/ERP will care about debits, credits, and close calendars. The glue between the two is your mapping logic—how events become journal entries.
A practical mapping might look like:
● subscription_started → deferred revenue credit, cash/debit AR
● invoice_paid → cash debit, AR credit
● refund_issued → revenue reversal plus cash/AR adjustment
This is where collaboration with finance matters most. Surveys like the Deloitte finance trends report highlight both the pressure on finance teams to “do more with less” and the central role of data in that shift. You don’t need to design the chart of accounts yourself—but you do need to expose enough dimensions (product, region, channel) that finance can map events to the right accounts.
- Plug into AI-enabled finance tools without bespoke glue every time Instead of building your own forecasting or anomaly detection, you can stream finance-ready data into specialized tools.
For example, you might send summarized events and ledger mappings into an AI-powered finance platform such as an AI bookkeeping platform for finance teams that can help turn those events into live cash projections, variance analysis, and scenario models finance can use without looping you in for every new report.
The key engineering move is to treat “finance destinations” as pluggable sinks. Once your ingestion service exposes a standard “fact table” of events, adding a new sink (warehouse, BI, AI platform) is incremental work, not a fresh cycle of custom exports.
If you’re building this in Node, posts like logging and monitoring in Node.js are worth a read, so you can observe exactly what’s flowing into those sinks—finance will love that you can prove what happened when.
Guardrails: make your finance integration boring, observable, and auditable
Finance systems don’t just need features; they need evidence. When numbers don’t tie, someone’s going to ask “what changed, and when?”
A few engineering practices go a long way.
- Treat finance pipelines like production-critical code Set up: ● Proper CI for your ingestion and mapping services.
● Property-based tests for currency, rounding, and edge cases.
● Contract tests around your event schema so changes are explicit.
If you front your ingestion layer with an HTTP API, it’s easier to lean on an API gateway than to reinvent auth, rate limits, and observability; a practical guide on choosing the right API gateway walks through the trade-offs in more detail than most vendor docs.
- Build first-class observability around finance events You want to answer questions like: ● “How many invoice_paid events did we emit yesterday by region?”
● “Are we dropping events from any source service?”
● “Did the FX enrichment fail for a subset of currencies?”
Practical tips:
● Attach a finance_stream_version to every event so you know what mapping logic was in play.
● Emit structured logs with event_type, amount_minor, and customer_id for every step in the pipeline.
● Create dashboards for business metrics (bookings, billings, churn) that are directly computed from the event stream; these become your smoke tests for data issues.
External research on finance data quality makes the same point in more formal language: if the ledger is the single source of truth for financial results, data quality issues there don’t just break dashboards—they can derail audits and regulatory reporting.
- Make replays and backfills a core feature Something will go wrong: ● A worker dies and misses a batch.
● A schema change drops a field you need.
● A billing provider sends delayed events.
Plan for:
● Replayable streams – events stored durably so you can reprocess with new code.
● Idempotent consumers – no duplicate ledger entries when you replay.
● Backfill jobs – scripts that can regenerate events from the source-of-truth DB for specific time windows.
This isn’t just resilience; it’s how you earn the right to refactor your finance integration without finance panicking that historical reports will silently change.
Wrapping it up: helping finance without becoming finance
You don’t need to memorize GAAP or redesign the entire chart of accounts to make life better for your finance team.
What you do need is to:
● Model a small set of clear, immutable finance events.
● Give those events a home in your architecture that’s reliable and observable.
● Land them in places that finance and AI tools can actually use, without fresh glue every time.
● Treat the whole pipeline with the same seriousness you give to billing or authentication.
Do that, and “can you send over an export?” slowly turns into “we pulled it from the finance stack; it’s already up to date.”
You get fewer surprise requests, finance gets numbers they trust, and everyone spends more time on the work they’re actually good at—instead of juggling spreadsheets that should’ve been events all along.
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