Artificial Intelligence for Accounts Receivable and Predictive Collections Course

Introduction

Effective collection management requires more than monitoring overdue balances. Finance teams must understand customer payment behaviour, identify emerging risks, and prioritise collection activities while maintaining professional customer relationships.

This five-day programme provides corporate finance and collection professionals with a practical approach to applying artificial intelligence and data analytics to accounts receivable management. Participants will explore Aging Report analysis, customer risk classification, predictive collections, and collection prioritisation. The programme also covers personalised collection communications, automated follow-up workflows, and AI-supported management reporting, with an emphasis on reliable data, sound judgement, and appropriate human oversight.

Objectives

By the end of this programme, participants will be able to:

• Interpret Aging Reports and identify overdue balances requiring attention.
• Classify customers according to payment behaviour and the likelihood of delayed payment.
• Use predictive insights to identify customers at risk of payment difficulties or potential default.
• Prioritise collection calls and follow-up activities based on risk, outstanding value, and recovery potential.
• Analyse customer data to identify payment patterns and the underlying causes of delayed settlement.
• Develop personalised collection messages and structured, automated follow-up workflows.
• Prepare AI-supported collection reports that communicate performance, risks, and recommended actions to management.

Day 1 – Aging Report Analysis and Customer Segmentation

• Understanding the structure and purpose of Accounts Receivable Aging Reports.
• Reviewing data accuracy, payment terms, and outstanding balances.
• Analysing aging buckets, overdue exposure, and changes over time.
• Evaluating customer payment history and recurring late-payment behaviour.
• Segmenting customers by payment performance, outstanding value, and collection risk.
• Applying Aging Report analysis to a practical customer portfolio.

Day 2 – Predictive Collections and Early Risk Identification

• Understanding predictive collections and their role in proactive receivables management.
• Identifying the customer and transaction data needed for payment-risk analysis.
• Recognising early warning indicators of payment delays and potential default.
• Applying a simple, explainable approach to customer risk scoring.
• Interpreting AI-supported predictions and checking their reliability against customer history.
• Selecting early intervention actions for customers showing signs of increased risk.

Day 3 – Collection Prioritisation and Payment Delay Analysis

• Establishing collection priorities using outstanding amounts, aging, risk, and recovery potential.
• Developing daily collection call lists and targeted follow-up plans.
• Analysing payment delays arising from disputes, documentation gaps, approval processes, and financial difficulties.
• Identifying recurring causes through customer records and collection notes.
• Matching collection actions to customer circumstances and risk categories.
• Using AI-supported insights to recommend next actions and appropriate escalation.

Day 4 – Personalised Collection Communication and Workflow Automation

• Preparing professional collection emails, payment reminders, and call scripts using AI.
• Personalising communications according to customer history, overdue status, and previous commitments.
• Structuring pre-due reminders, overdue follow-ups, and escalation messages.
• Designing automated follow-up workflows with clear triggers and exception handling.
• Tracking promises to pay and responding to missed payment commitments.
• Applying confidentiality safeguards, human approval, and accuracy checks to AI-generated communications.

Day 5 – Collection Performance Analysis and AI-Supported Reporting

• Using AI to identify customer payment patterns and trends in overdue receivables.
• Monitoring collection indicators, including Days Sales Outstanding, overdue balances, and promise-to-pay fulfilment.
• Designing clear dashboards for aging, customer risk, and collection performance.
• Producing AI-supported management summaries grounded in verified financial data.
• Presenting priority accounts, collection risks, and recommended management actions.
• Completing an integrated case study covering portfolio analysis, prioritisation, communication, and reporting.