Infrastructure for Predictive Collections & AR Optimization

Predictive collections ML requires documented credit policies, collection escalation procedures, and customer-specific billing terms to be findable and current. The AI needs to know that Customer A has net-60 terms with a dispute resolution path before it can score payment likelihood accurately. GAAP documentation and SOX controls provide a baseline, but collection strategy rules—when to call vs. email, when to hold shipments—must be explicitly documented at L3 so the model can apply consistent tactics.

Payment likelihood scoring depends on systematic capture of payment history, communication touchpoints, and dispute resolutions linked to specific invoices. ERP auto-captures transaction data, and TMS feeds shipment billing, providing the structured payment behavior data the ML model needs. Template-driven capture ensures fields like days-to-pay and late-frequency are consistently populated, enabling the model to compute DSO trends and bad debt risk scores per customer.

AR optimization requires consistent schema across customer master data (billing terms, credit limits), invoice records (amount, aging bucket, dispute flag), and communication history. The ML model needs to join these fields reliably to compute payment probability. Finance data's structured GL and customer master provide this foundation, enabling queries like 'all open invoices >45 days for customers with prior disputes' that drive collection priority rankings.

The collections model must query AR aging from ERP, pull communication logs, and write priority rankings back to the collections workflow. API access to ERP and CRM systems enables this without manual export. Mid-market logistics ERP systems increasingly offer APIs, allowing the AI to retrieve open invoice data and customer credit profiles in near-real-time rather than waiting for scheduled batch exports that delay proactive outreach.

Customer payment behavior and credit risk signals change with economic conditions and customer financial health. Event-triggered updates—when a customer's credit score changes, when a new dispute is filed, when payment terms are renegotiated—keep the model's inputs current. Finance's regulatory discipline ensures active AR data is refreshed daily, supporting the model's need for current aging and open invoice status to generate accurate collection forecasts.

AR optimization requires connecting ERP (invoice and payment data), CRM or communication logs (customer interaction history), and the collections workflow tool (output of priority rankings and tactic recommendations). API-based connections between these systems allow the model to assemble a complete customer collections profile without manual data transfer. TMS-ERP integration already supports shipment-to-invoice flows, extending this to collections workflow is the required step.