Automated Collections for Xero

An effective AI integration for Xero Collections connects at two primary surfaces: the Contacts API for customer segmentation and communication history, and the Invoices API for real-time status on AccountsReceivable, Overdue, and Paid invoices. The AI system acts as an orchestration layer that polls Xero for aging reports, analyzes customer payment patterns and risk (using data like TotalOwed, AverageDaysToPay), and then executes a prioritized, multi-channel outreach strategy. This is not a replacement for Xero's built-in invoice reminders but an intelligent overlay that decides which customer to contact, when, how (email, SMS), and with what message based on learned effectiveness.

Implementation typically involves a serverless function or microservice that uses Xero's webhooks (e.g., InvoiceUpdated, ContactUpdated) to trigger collection logic in near real-time. For example, when an invoice status changes to Overdue, the AI agent can evaluate the contact's history: if they have a good record, it might send a gentle reminder via Xero's email function; if they are a high-risk account, it could escalate to a personalized email drafted by an LLM and log a task in Xero for a human collector. All outbound communication and status changes are written back to Xero as Notes on the contact or invoice, maintaining a complete audit trail within the platform's native interface.

Rollout should be phased, starting with a pilot on a segment of low-risk, overdue invoices to tune the AI's decision logic and message templates. Governance is critical: establish clear rules for escalation thresholds and ensure a human-in-the-loop for any communication that deviates from pre-approved templates. This approach reduces manual dunning work from hours to minutes, improves cash flow by prioritizing high-value delinquencies, and keeps all collection activity centralized within Xero's system of record. For a deeper dive on the technical patterns for building such agents, see our guide on AI Agent Builder and Workflow Platforms.

The integration architecture is built on Xero's Contacts API and Invoices API, with webhooks for real-time event triggers. The core data flow begins when an invoice's due date passes, triggering a webhook to an AI orchestration service. This service pulls the invoice details and the full contact history for the customer, including payment patterns and communication logs. An AI agent then evaluates the AR aging report data, the customer's risk segment, and the invoice amount to determine the optimal collection action—such as sending a reminder email, scheduling a call, or escalating to a human collector. The agent drafts a personalized message using Xero's contact details and logs the proposed action back to the contact notes or a custom field via the API, ensuring all activity is recorded within the platform's audit trail.

For implementation, the AI system typically runs as a cloud service that authenticates via OAuth 2.0 to the Xero tenant. Key objects involved are the Contact (for customer profile and history), Invoice (for status, due date, amount), and Payment (for tracking partial payments). The agent's decision logic can be configured with rules (e.g., "first reminder after 7 days") and enhanced with ML models trained on historical collection outcomes. Actions are executed through Xero's Email endpoints or integrated third-party communication channels like Twilio for SMS. A reconciliation job runs nightly to sync statuses, ensuring the AI's view of "collections priority" matches the live AR ledger in Xero.

Rollout should follow a phased approach, starting with a pilot on a subset of low-risk, overdue invoices to tune the agent's tone and timing. Governance is critical: all AI-generated communications should be reviewed by a collector for the first 30 days, and a human-in-the-loop approval step should remain for any escalation action (e.g., payment plans, write-offs). The integration includes a dashboard for finance managers to monitor the AI's hit rate (reminders that led to payment), override its recommendations, and audit the complete interaction log stored in Xero. This design reduces manual follow-up from hours to minutes per week while keeping the finance team in control of customer relationships and compliance.

A production-grade AI integration for Xero collections operates through a secure middleware layer, not directly inside Xero. This layer uses Xero's OAuth 2.0 API with scoped permissions (typically accounting.transactions, accounting.contacts.read, accounting.contacts.write) to fetch invoice and contact data. All AI processing—such as risk scoring customers, drafting email reminders, and scheduling follow-ups—occurs in this governed environment. Communication logs (e.g., 'AI drafted reminder for Invoice INV-001') are written back to Xero as notes on the contact or invoice, creating a complete, auditable trail within the platform's native audit log.

Rollout follows a phased, risk-managed approach: Phase 1 (Monitor & Learn): The AI system runs in a read-only 'shadow mode' for 30 days, analyzing your AR aging report and customer payment history to build risk profiles and suggest collection actions without taking any. Phase 2 (Assisted Workflow): AI-generated tasks (prioritized collection list, draft emails) are pushed to a human-in-the-loop queue in your team's existing task manager (e.g., Asana, Slack). Collectors review, edit, and manually send communications, with all outcomes fed back to train the AI. Phase 3 (Conditional Automation): For low-risk, low-value invoices (e.g., under $500, 30 days overdue), the system can be configured to automatically send pre-approved reminder sequences, escalating to a human only if a payment promise is broken or a dispute is logged in Xero.

Governance is enforced through configurable business rules: you define which customer segments, invoice age brackets, and dollar amounts are eligible for automated actions. All AI-generated content is tagged and versioned, allowing for prompt tuning and compliance review. Before any automated email is sent, it can be routed through an approval step or a secondary LLM for policy compliance checking. This architecture ensures the AI acts as a force-multiplier for your collections team while maintaining strict control, aligning with financial controls and data privacy requirements inherent to accounting platforms.