Infrastructure for Collections & AR Optimization

Collections optimization requires documented collection policies — escalation procedures, communication timing standards, write-off thresholds, and client relationship protocols that override standard collection actions. In professional services, AR collection is documented at a basic level (dunning letter sequences, aging thresholds) but client-specific exceptions — 'don't call the CFO directly, go through the partner' — live in account managers' heads. At L2, standard collection procedures are documented enough for the ML model to learn baseline patterns, but exception handling requires human judgment.

Payment delay prediction requires systematic capture of invoice aging, payment timestamps, and communication history. At L3, PSA and ERP systems automatically log invoice issuance dates, payment receipt dates, and aging buckets. Communication history (emails, calls) is captured through structured logging in CRM or AR systems. This systematic capture enables the ML model to learn reliable payment behavior patterns across clients, rather than relying on manually maintained aging spreadsheets.

Collections prediction requires a consistent schema linking invoices, clients, payment history, and communication records. At L3, the PSA/ERP financial data model provides standardized invoice aging buckets, client identifiers, and payment records that the ML model can consume as structured features. The consistent schema across clients and time periods enables the model to compute meaningful payment behavior features — days-to-pay distribution, seasonal patterns, dispute frequency.

Collections optimization requires the ML model to query current invoice aging, client payment history, communication logs, and financial health signals programmatically to generate daily priority scores and outreach recommendations. At L3, API access to PSA, ERP, and CRM enables automated daily scoring without human data compilation. AR staff receive actionable priority queues rather than sorting aging reports manually.

Payment prediction models must update as client payment behavior evolves — a client that reliably paid on time for two years then changed CFOs may require a recalibrated risk score. At L3, event-triggered maintenance retrains or recalibrates the model when significant client changes are detected (ownership change, credit downgrade) and refreshes AR aging data continuously through month-end close cycles, keeping DSO forecasts accurate.

Collections optimization primarily integrates ERP (invoice aging, payment history) with an AR workflow system for routing collection actions. At L2, point-to-point integration between ERP and the collections tool is sufficient for payment delay prediction — the model operates on financial data from a single system of record. CRM integration for communication history enriches predictions but the core capability functions with financial data alone, consistent with the professional services baseline.