End Close

Finance teams at payments companies reconcile large batched payouts against individual transactions by hand, and this gets harder as transaction volume grows. A Head of Finance quoted on the site describes reconciling batched payments against individual transactions becoming a real manual lift for both their finance team and their customers. The alternatives described are building reconciliation in-house, which the site says takes quarters of engineering and requires a growing team of analysts as volume scales, or using legacy vendors, which it describes as glorified spreadsheets built around monthly, balance-level reconciliation that only detect exceptions without resolving them.

End Close ingests transaction data from sources like Stripe, Snowflake, BigQuery, Postgres, Databricks, bank feeds, PDF processor reports, CSV and API, then parses and normalizes it before matching. It runs automated matching described as exact, fuzzy and many-to-many, with tolerances for FX, rounding and other real-world messiness, and claims 99.999% of transactions are automated. AI agents resolve straightforward exceptions on their own with a human-review trail, and every action taken by a person or an agent is logged in a permanent, auditable trail. The product also offers real-time per-account balances, natural-language queries over payments data, compliance report exports, and direct export into GL systems such as NetSuite, QuickBooks, Xero and Sage.