Automation and AI Are Transforming Trade Credit and Collections
New Automation Tools Reshaping Accounts Receivable Management
Automation and artificial intelligence (AI) are transforming accounts receivable (AR) and B2B trade credit management by replacing manual, error-prone processes with intelligent, AI-driven tools. Credit decision-making, collections, cash application, deductions, and communications are greatly enhanced by AI-powered AR automation.
Ultimately, these tools enable enterprises offering trade credit to streamline collections and improve cash flow. Real-time insights into credit risk and payment behaviors are turning AR into a strategic function that enhances efficiency, quality, and growth.
Key advancements include:
Predictive Analytics and AI Platforms: AI forecasts overdue payments, prioritizes collections, and tailors strategies to reduce risk and optimize working capital. Solutions from a score of companies like Serrala and BlackLine use machine learning (ML) to improve recovery rates and cash application accuracy.
Embedded Finance and Real-Time Credit Assessment: Platforms such as CrediLinq use AI to assess creditworthiness instantly, enabling faster, data-driven trade credit decisions within B2B marketplaces.
AR Automation Tools: Solutions like Visa’s AR Manager automate payment reconciliation, integrate with ERP systems, and provide dashboards for managing receivables, reducing delays, and accelerating payment cycles.
AI-Driven Communication: Generative AI and natural language processing automate invoice generation, follow-ups, and payment notice management, saving time and enhancing customer interactions.
Strategic Financial Management: CFOs and treasurers leverage AI-powered forecasting and real-time data to manage liquidity, simulate scenarios, and strengthen financial resilience.
Benefits of AI and automation in AR include faster payments, reduced manual workload, improved accuracy, better cash flow forecasting, and enhanced customer relationships. These innovations shift AR from a reactive back-office task to a proactive, value-adding business function that drives financial stability and growth.
That's a great question. The issue: How granular is your data? Maps are two-dimensional, as I see it, so everything is flattened out, and you are essentially looking at averages. Terrain, however, is three-dimensional, so there is a lot more nuance than a map can convey as long as you have all the data. Unfortunately, that's not always the case. My guess is that on a macro basis, optimizing for the map is probably good enough, because AI is not always going to predict the eventual outcome and will likely miss both ways. On a micro or account level, I want to incorporate as much terrain as possible. Does that comport with how you are looking a this?
When forecasting gets too good, do we risk optimizing for the map instead of the terrain?