AI Integration for Recurly Dunning Management

AI integration connects at three key points in Recurly's dunning system: the webhook stream for real-time payment failure events, the REST API to query subscription and transaction history, and the communications API to personalize and orchestrate outreach. Instead of a one-size-fits-all retry schedule, an AI agent listens for transaction_failed webhooks, instantly analyzes the customer's payment history, subscription value, and decline reason, and then decides the optimal action. This could be an immediate retry with a different gateway, a personalized email drafted via the communications API, or an immediate escalation flag in the customer's Recurly account for a collections specialist.

The core intelligence lies in the agent's decision logic, which uses historical data to predict recovery likelihood. For a high-value subscriber with a single card decline, the agent might trigger a gentle SMS reminder via Twilio (integrated through Recurly's gateway ecosystem). For a pattern of declines across multiple payment methods, it could pause the dunning sequence, update the account with a high_risk_collections tag via the API, and create a task in the collections team's Salesforce queue. This transforms dunning from a reactive, time-based process into a predictive, value-based workflow that prioritizes effort and preserves customer relationships.

Rollout is typically phased, starting with a shadow mode where the AI agent analyzes events and recommends actions without executing them, allowing teams to audit its logic against existing rules. Governance is critical: all agent decisions and the data used (e.g., last_four of card, decline code) must be logged to an audit trail outside Recurly. Final implementation involves setting up secure, scoped API credentials for the agent and defining clear escalation thresholds to ensure complex cases—like potential fraud or contractual disputes—are always routed to human review before any account-altering action is taken.

The integration is triggered by Recurly's failed_payment webhook. This event payload—containing the account_code, invoice_number, failure_code, and historical payment data—is routed to a central Orchestration Agent. This agent's first task is to enrich the context by calling Recurly's API for the full account profile, subscription details, and past dunning history. Simultaneously, it may query a vector store containing past support tickets or CRM notes to understand any recent customer issues.

Based on this enriched context, the Orchestration Agent routes the case to one of three specialized execution agents:

• Predictive Prioritization Agent: Scores the account's likelihood of successful recovery using payment history, subscription value, and engagement signals. High-value, high-probability accounts are fast-tracked.
• Personalized Communication Agent: Drafts and sends multi-channel messages (email, in-app). It uses the failure reason (insufficient_funds vs. card_expired) and customer persona to tailor tone, offer (e.g., partial payment link), and timing.
• Exception & Routing Agent: Handles complex cases—like multiple declines or accounts flagged for fraud—by creating tasks in a collections team queue (e.g., in Salesforce Service Cloud) with a summarized case file and recommended next steps.

All agent decisions and actions are logged back to a dedicated audit object in Recurly via custom fields or notes, creating a transparent trail. The system is designed for gradual rollout: initially, agents operate in a human-in-the-loop mode where recommendations are presented to a collections manager for approval. Over time, as confidence thresholds are met for specific failure patterns (like expired_card), those workflows can be fully automated, while complex exceptions remain routed for human review.

A production integration connects to Recurly's webhook and REST API endpoints, primarily listening for failed_payment_notification and past_due_invoice events. The AI agent, acting as a middleware service, processes these payloads—containing anonymized account identifiers, invoice amounts, and decline reasons—without storing full PCI data. It enriches this context by securely querying Recurly's accounts, subscriptions, and payment_methods APIs to build a risk profile, then executes logic within a secure, isolated runtime environment.

Governance is enforced through role-based access controls (RBAC) on the AI system, ensuring only authorized RevOps or finance users can adjust dunning rules or review AI-generated communication drafts. Every AI action—such as sending a personalized email via Recurly's communication API, scheduling a retry, or escalating an account—is logged to an immutable audit trail with the reasoning context. This creates a clear lineage from a payment failure to the AI's recommended action, which is critical for compliance and operational reviews.

A phased rollout is recommended. Start in a monitor-only phase, where the AI analyzes dunning events and proposes actions in a dashboard without executing them, allowing teams to validate its logic against historical recovery rates. Then, progress to a hybrid phase, where the AI automates low-risk, high-volume retries (e.g., first-time declines for small amounts) but routes complex cases or high-value accounts to human collections for review. Finally, a fully automated phase can be implemented, with continuous evaluation against key metrics like recovery rate and customer satisfaction scores to detect model drift or unintended consequences.