FINANCE
Bank Feed Anomaly Detection with Escalation
Scans the day's bank and Stripe transactions against a rolling baseline in BigQuery, flags outliers like unusual large debits or duplicate charges, and alerts finance on Slack.
How it runs
The automated pipeline, trigger to output.
- TriggerSchedule fires after prior business day closes
- ActionPull day's Stripe and Postgres transactionsPostgres
- ActionLoad rolling baseline statistics from BigQueryBigQuery
- LogicScore transactions and branch by anomaly severity
- ActionPage on-call for high-severity anomaliesPagerDuty
- OutputPost all flagged anomalies to SlackSlack
What it does
Runs statistical anomaly checks over each day's transactions. It compares today's debits, credits, and merchant patterns against a 90-day rolling baseline stored in BigQuery, then flags outliers: abnormally large transfers, duplicate-looking charges, or accounts whose net movement breaks their historical range. Low-severity flags go to Slack for review; high-severity ones page on-call.
When to use it
When manual eyeballing of statements misses fraud, double-billing, or misposted entries until reconciliation. Use it as a daily early-warning layer that surfaces only genuine outliers instead of noise.
How it works
- 1A schedule triggers after the prior business day closes.
- 2Pull the day's Stripe payouts and charges plus posted bank transactions from Postgres.
- 3Query the BigQuery baseline (rolling means, standard deviations, known recurring vendors).
- 4Score each transaction; branch by severity threshold.
- 5For high-severity anomalies, raise a PagerDuty incident for the on-call analyst.
- 6Post all flagged items with context and z-scores to the finance Slack channel.
Set it up
What you configure once, before turning it on.
- 1Connect StripeCustomers, subscriptions, payments.
- 2Connect PostgresAny Postgres URL — query, write, migrate.
- 3Connect BigQueryDatasets, queries, schemas.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Connect PagerDutyIncidents, on-call, escalations.
- 6Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 7Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 8Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Finance workflows
Month-End Uncoded Spend Chaser via Snowflake to Slack
On a month-end schedule, queries Snowflake for unaccrued or uncoded expenses, groups them by department owner.
Receipt Upload OCR Policy Check with Manager Escalation
When an employee drops a receipt into a Drive folder, it extracts the line items, checks them against expense policy.
Weekly Proration Anomaly Audit to Notion
Each week it aggregates all flagged proration discrepancies from Snowflake, scores them against anomaly thresholds, and publishes a finance-ready audit page in Notion with totals.
Accrual Chase Board in Monday with Per-Owner Tasks
On a schedule it reads open uncoded expenses from Snowflake and creates or updates a Monday item per department owner.
Detect Mid-Cycle Plan Change Mischarges and Queue Credit Memos
Listens for Stripe subscription plan changes, recomputes the correct prorated amount.
Draft and Send Proration Over-Billing Correction Emails
For each confirmed over-billing credit, an agent drafts a clear, customer-specific apology email explaining the proration error and the credit applied.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.