Multi-Agent Based Automation of Collections Prioritization and Dunning

By autonomously prioritizing high-risk, high-value accounts and initiating personalized outreach within hours of becoming overdue, this workflow compresses the collection cycle. The system's continuous analysis of payment history and real-time cash needs ensures effort is focused where it has the greatest liquidity impact, typically achieving a 10-20% reduction in DSO within the first operational year.

The workflow's predictive scoring identifies accounts with deteriorating credit signals before they become write-offs, enabling earlier intervention or reserve adjustments. Automated dunning and structured escalation prevent accounts from slipping into delinquency through neglect, directly lowering bad debt expense and reducing the required allowance for doubtful accounts.

Agents handle routine prioritization, data gathering, and initial communication drafting, freeing collectors to focus on complex negotiations and escalated cases. This shifts the team from reactive, high-volume calling to strategic relationship management, often allowing a single team to manage a 30-50% larger portfolio without adding headcount.

Personalized, context-aware communication (e.g., referencing specific invoices, offering payment plans) reduces customer friction compared to generic, batch-driven dunning. This preserves key relationships and can improve retention rates among temporarily strained customers, protecting future revenue streams that are often lost during aggressive, manual collection drives.

The workflow integrates with payment and ERP systems (e.g., SAP, Oracle), automatically matching incoming payments to prioritized invoices and updating account status. This accelerates cash application, reduces lockbox reconciliation effort, and provides real-time visibility into true collected cash versus outstanding AR, closing the liquidity loop faster.

Every prioritization decision, communication sent, and escalation step is logged with the agent's reasoning, creating a defensible audit trail. The system enforces collections policies and regulatory requirements (e.g., FDCPA) uniformly, reducing compliance risk and providing clear reporting for internal audit and management oversight.

This core agent continuously scores each debtor by analyzing ERP payment history, real-time credit bureau signals, current cash forecast needs, and open invoice amounts. It assigns a dynamic collections priority score, routing high-risk, high-value accounts to the top of the workflow queue. This replaces manual spreadsheet sorting and ensures effort is focused where it has the greatest liquidity impact.

Built on frameworks like LangGraph, this component manages the state and execution of the dunning sequence. It triggers personalized email drafts via LLM, schedules SMS reminders, and can initiate secure messaging platform connections based on customer preference and response. It handles retry logic, channel fallback, and compliance logging for all outbound communications.

When payments arrive, this engine automates lockbox reconciliation. It uses OCR and LLMs to extract data from check images and bank files, then applies ML-based matching to link payments to open invoices—even with poor remittance data. It creates a clean exception queue for staff, resolving >90% of payments straight-through and accelerating cash application.

For complex disputes, promised payments, or high-stakes negotiations, the workflow automatically escalates to a human collector. This layer packages the full customer interaction history, risk score, and recommended actions into a case within the CRM (Salesforce, ServiceNow). It ensures seamless handoff, maintains audit trails, and allows collectors to trigger follow-up agentic actions (e.g., payment plan generation) directly from the case.

The control plane for the operation. This observability layer ingests event data from all agents and integrated systems (ERP, CRM, banking APIs) to display real-time metrics: agent effectiveness, promise-to-pay rates, aging bucket shifts, and projected DSO. It enables managers to adjust agent rules, pause sequences, or re-prioritize queues based on live performance against targets.

The foundational data layer. Custom-built connectors and change-data-capture (CDC) streams synchronize debtor master data, invoice status, and payment postings between the collections workflow and core systems like SAP S/4HANA, Oracle Fusion, or NetSuite. Secure API integrations with banking platforms fetch real-time lockbox data and execute payment acknowledgments, creating a closed-loop system.

Primary buyers who fund the build based on the working capital and liquidity ROI. They mandate the reduction in Days Sales Outstanding (DSO) and bad debt expense, and define the policy guardrails (e.g., which customer tiers can receive automated dunning, maximum communication frequency). Their sign-off is required on the business case linking agent logic to cash flow improvement.

The operational owner who defines the prioritization rules, dunning scripts, and escalation paths. They provide the domain knowledge on customer segmentation, payment history analysis, and effective communication tactics. During implementation, they validate the agent's decision logic and approve the human-in-the-loop workflows for high-risk accounts.

Responsible for the production architecture and integration design. They select the orchestration framework (LangGraph, Temporal), design the API connections to ERP (SAP, Oracle) and CRM (Salesforce), and establish the observability and logging pipeline. This role ensures the multi-agent system is scalable, secure, and maintainable within the existing tech stack.

Builds and maintains the data pipelines that feed the agents. They unify customer payment history, credit scores, and real-time AR data from fragmented sources. This team operationalizes the ML models for payment probability scoring and is responsible for model retraining, drift detection, and ensuring the quality of inputs that drive prioritization.

The end-users who are augmented by the system. Their workflow shifts from manual prioritization and drafting to reviewing high-priority exceptions and handling complex negotiations. Their feedback during piloting is critical for tuning agent behavior and communication tone. Change management focuses on transitioning them to a supervisor role.

Governance stakeholders who audit the workflow for regulatory adherence. They review and approve all AI-generated communication templates for fair debt collection practices (e.g., FDCPA). They also mandate audit trails for all agent decisions and require configurable holds on communications for customers in active dispute or bankruptcy proceedings.