Oracle Fusion Cloud ERP and AvidXchange integration

What moves between them

The main flow moves invoices and purchase orders from Oracle Fusion into AvidXchange. ml-connector polls Oracle Fusion REST API on your schedule (typically every 5-15 minutes, filtered by LastUpdateDate), retrieves unpaid invoices and associated POs, and pushes them to AvidXchange. After AvidXchange processes invoices through approval workflows and executes payments, ml-connector reads the completed payment records and their associated GL details, then reconciles those payments back into Oracle Fusion GL accounts on the same polling schedule. Vendor master data can be synchronized in both directions to keep coding tables aligned.

How ml-connector handles it

ml-connector manages credentials encrypted for both Oracle Fusion and AvidXchange. On the Oracle side, it stores the multi-tenant pod URL per customer and refreshes OAuth 2.0 bearer tokens when a call returns 401 or expires. On the AvidXchange side, it handles Partner Portal OAuth 2.0 credentials. Because both systems operate on a pull-only model, ml-connector polls both REST APIs on a configurable schedule (typically aligned with your AP cycle). It filters Oracle Fusion invoices by LastUpdateDate to avoid redundant fetches, maps AvidXchange GL dimensions to Oracle GL accounts and cost centers, and tracks payment status to prevent duplicate postings. Every record carries a full audit trail, and if a downstream GL posting fails, the transaction can be replayed.

A real-world example

A mid-market manufacturing company runs Oracle Fusion Cloud ERP for procurement, finance, and GL. They use AvidXchange to automate accounts payable coding and approval. Before the integration, AP staff manually exported unpaid invoices from Oracle, entered them into AvidXchange, then waited for approval and execution before re-entering payment status back into Oracle. With Oracle Fusion and AvidXchange connected, each invoice flows automatically from Oracle into AvidXchange's approval queue, payments are executed in AvidXchange, and completion status is read back into Oracle GL on your AP schedule. The manual re-entry step is eliminated, approval times drop, and reconciliation happens automatically.

What you can do

• •Poll Oracle Fusion on a schedule to retrieve unpaid invoices and purchase orders, then push them into AvidXchange for approval workflow and payment execution.
• •Read completed payment records and GL data from AvidXchange and reconcile them back into Oracle Fusion GL accounts without manual re-entry.
• •Handle Oracle Fusion's OAuth 2.0 authentication and multi-tenant pod URL model, plus AvidXchange's Partner Portal credentials and API gateway.
• •Keep vendor master data and GL account coding tables synchronized between Oracle Fusion and AvidXchange in both directions.
• •Track payment status and prevent duplicate GL postings with a full audit trail and the ability to replay failed transactions.

Questions

Which direction does data flow between Oracle Fusion and AvidXchange?

Invoices and purchase orders move from Oracle Fusion to AvidXchange for approval and payment execution. Payment status and GL data are read from AvidXchange and reconciled back into Oracle Fusion. Vendor master data and GL accounts can be synchronized in both directions to keep coding tables aligned.

How does the integration handle Oracle Fusion's lack of webhooks and multi-tenant pod URLs?

ml-connector accepts the full pod URL per customer, since Oracle Fusion publishes no shared base address and is multi-tenant by pod. Because both systems support polling only, ml-connector polls Oracle Fusion REST API on your AP schedule, filtering by LastUpdateDate to avoid redundant fetches, and reads AvidXchange payment status on the same schedule.

What happens if AvidXchange processing takes longer than the polling interval?

ml-connector tracks the status of each payment transaction and prevents duplicate GL postings by checking payment state before writing to Oracle. If a payment is still in progress when the next polling cycle runs, ml-connector skips it and checks again at the next interval. All transaction state is logged in the audit trail.