AI-Powered Collections & AR Management

Overview

A B2B services company had $2.3M in overdue receivables. Their collections process was a finance coordinator manually calling through a list sorted by amount, with no intelligence about who was likely to pay, what communication would work best, or when to escalate. We built an AI collections system that predicts payment behavior, prioritizes the follow-up queue, auto-sends personalized reminders at optimal times, and escalates strategically.

The Challenge

Collections is a delicate balance — you need to be persistent enough to get paid but professional enough to keep the customer relationship. Different customers respond to different approaches (email vs. phone, gentle reminder vs. formal notice). The AI needed to learn individual payment patterns, predict which invoices were at risk before they went overdue, and adapt communication style per customer.

Our Approach

We built a payment prediction model using historical invoice data, customer payment patterns, and external signals (company financial health, seasonality). Each overdue invoice gets a probability-to-pay score that determines urgency and approach. The AI sends automated reminder sequences with personalized messaging — tone and channel adapted per customer based on what's worked before. The collections dashboard shows the finance team exactly where to focus manual effort for maximum recovery.

Key Features

• Invoice-level payment probability scoring
• Automated personalized reminder sequences
• Channel optimization per customer (email, SMS, call)
• Pre-overdue risk detection and early follow-up
• Collections dashboard with prioritized queue
• Escalation workflow management
• Bad debt prediction and write-off recommendations

Results

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