Effective management of cash flow — the continuous movement of money into and out of a business — is crucial for operational stability, growth strategies, and long-term financial planning. Accounts Receivable (AR) is a huge part of the cash cycle. Historically, managing AR has relied heavily on manual tasks, which has had the potential for inefficiencies, errors, and delays in payment collection. But in modern AR management, companies are increasingly exploring artificial intelligence (AI) as a promising way to improve operations by boosting efficiency, minimizing errors, and optimizing financial processes.
Let’s look at how AI-driven accounting software might address common AR challenges, leverage predictive analytics to enhance collection practices, and provide real-world examples demonstrating the potential benefits of AI implementation.
How AI Accounting Software Can Address Common AR Challenges
Accounts Receivable departments often face recurring problems, including delayed payments, lengthy Days Sales Outstanding (DSO), invoicing inaccuracies, and unpredictable cash flow forecasts. AI-based solutions offer potential strategies to streamline these processes and reduce common inefficiencies.
Companies are increasingly exploring AI as a promising way to improve AR operations.
Tackling Late Payments
Analytics and predictions
AI can help mitigate delayed payments in AR. By leveraging historical payment patterns and real-time customer behaviors, businesses can use predictive analytics to forecast when invoices are likely to be paid, and to identify accounts that carry a higher risk of delayed payment or default. Machine learning algorithms are capable of learning a customer's payment tendencies, allowing the system to trigger earlier reminders when necessary.
More sophisticated platforms can achieve a high degree of accuracy in predicting payment timing through the analysis of extensive transaction data. This can be a key area of differentiation among providers. AI intelligence comes from data. Realizing the benefits of financial insights and forecasting requires a strong data foundation, as intelligence is determined by the quantity and quality of input. The smartest systems are built both on internal data (like observed payment behaviors) but also external data, such as market intelligence and trends. For example, Billtrust’s AI model leverages both behavioral data alongside industry benchmarking data from its network of 13M buyers.
Sophisticated platforms achieve a high degree of prediction accuracy through the analysis of extensive transaction data – both internal and external.
Optimizing collections
Intelligent collections software, for example, employs AI to optimize the collections process by prioritizing which customers to contact and suggesting the most effective collection strategies. These algorithms analyze a range of factors, including customer payment history, their responses to past communications, and their credit profiles, to develop optimized dunning schedules. This focused approach ensures that collectors direct their efforts towards high-risk accounts. The system can even send tailored reminders before an invoice's due date, for example, if a customer has a demonstrated pattern of paying late.
An AI-driven approach ensures that collectors direct their efforts towards high-risk accounts.
Personalized outreach
Generative AI is already being used to create personalized dunning emails and payment reminders, which can significantly improve response rates and accelerate collection times compared to standard, generic communications. These AI-generated messages can be customized to reflect the customer's payment history and preferred communication style.
Autonomous agents serving as AR helpdesks
Finally, autonomous AR agents, also known as
agentic AI,
can monitor incoming customer communications and automatically take actions like sending invoice copies, marking messages as disputes or responding to common payment inquiries. This would ensure that no customer request was overlooked and resolve issues that might otherwise lead to payment delays. These agents would function as AI-powered “AR helpdesks,” capable of handling a large percentage of routine inquiries without human intervention.
Agents function as AI-powered “AR helpdesks.”
Streamlining Collections (DSO)
AI automation reduces Days Sales Outstanding (DSO) by streamlining the accounts receivable processes. The technology eliminates delays through automated invoice generation and payment reconciliation, while machine learning identifies payment patterns to enable preemptive action. By analyzing payment histories, systems can automatically send timely reminders to customers with late payment tendencies. Companies report that customers who previously paid beyond net terms respond well to these personalized approaches. This strategic combination allows AR teams to focus on high-risk accounts while automated systems handle routine follow-ups, consistently lowering overall DSO metrics.
On average, Billtrust clients reduce DSO by 50%.
Reducing Invoice and Payment Errors
Manual AR processes often lead to errors, disputes, and delayed payments. Leveraging AI tools, such as Optical Character Recognition (OCR) and advanced algorithms, enable automated invoice processing with match rates typically exceeding 95%.
Additionally, these systems flag discrepancies in real time: AI-powered anomaly detection and alerting capabilities proactively identify irregularities, allowing businesses to promptly resolve issues and prevent revenue loss.
By ensuring accurate invoicing and payment processing, AI reduces errors in financial reporting, enhances compliance, and builds stakeholder trust. Additionally, these systems protect against fraud by detecting unusual transactions and duplicate billings that might otherwise go unnoticed, further safeguarding against financial losses.
Improving Cash Flow Forecasting
Accurate cash flow forecasting is critical for effective financial management. AI-driven analytics leverage historical data, industry-specific benchmarks, macroeconomic indicators through real-time AI search tools, and customer behavior to deliver precise and dynamic cash flow predictions — routinely outperforming traditional methods reliant solely on past trends.
These enhanced forecasts enable businesses to anticipate cash shortfalls or surpluses and proactively adjust financial strategies. For example, if AI predicts lower receivables due to seasonal trends or high-risk accounts, companies can secure short-term financing or reduce expenses ahead of time.
Holistic visibility empowers organizations to optimize working capital and make informed, strategic investment decisions.
Enhancing Collection Strategies with Predictive Analytics
AI-driven predictive analytics can significantly improve AR operations through precise payment forecasting, effective risk management, and personalized collection strategies. These capabilities facilitate proactive cash management, potentially reducing payment delays and defaults.
Predicting Payment Behavior
AI algorithms analyze transaction data, payment histories, and customer demographics to accurately predict future payments, enabling proactive AR management. Businesses can segment customers by payment risk, applying tailored credit policies and collection strategies — offering prompt payers favorable terms while closely monitoring higher-risk accounts. Further, predictive insights can integrate directly into automated workflows, triggering timely reminders, dynamically adjusting credit limits, or flagging high-risk accounts for immediate intervention, improving AR efficiency and reducing financial risk.
Predictive insights can integrate directly into automated workflows, triggering a variety of activities to accelerate AR.
Risk-Based Collection Management
AI-powered systems assign granular risk scores based on predicted customer payment behaviors, enabling companies to prioritize and efficiently allocate collection resources. Early identification of high-risk accounts through real-time data and anomaly detection allows timely intervention with targeted strategies, improving on-time payments and reducing bad debt. This dynamic approach ensures collectors focus on critical accounts, enhancing overall collection performance.
Personalized Customer Communications
AI technology enables highly personalized customer interactions by analyzing past payment behaviors, communication responsiveness, and sentiment from previous exchanges. Generative AI leverages these insights to create tailored communications like customized dunning emails that reflect customer relationships or adjust tone based on historical interactions, which has been shown to boost engagement and improve collection outcomes.
AI 'Co-Pilots' for AR Teams
Emerging AR software solutions often feature AI-powered assistants or “co-pilots” that provide AR professionals with real-time, actionable recommendations. These AI tools suggest specific actions, such as targeted follow-ups, payment term adjustments, or customized collection approaches, potentially enhancing productivity and effectiveness.
Billtrust’s Co-Pilot, for example, uses AI-based behavioral analytics to make recommendations that can both save and make companies money -- like shifting buyers to more cost-effective payment methods, reallocating credit, or adjusting payment policies to cut costs. Learn more about Co-Pilot.
Real-World Results from AI Implementation
Companies already utilizing AI-driven AR solutions from Billtrust have reported notable benefits:
- On average, clients using Billtrust’s AI-powered AR automation platform recognize a 50% improvement in DSO.
- International cinema chain Kinepolis reduced its DSO by 13 days.
- Manufacturer Acushnet used cash application automation to achieve matching accuracy rates of +99.9%, even with complex multi-line remittances.
- White Cap , a building materials supplier, recognized productivity gains equivalent to 3 full-time employees and saved $36,000 within the first few months of automating their AR processes using AI.
- Previously, a mobile storage provider processed 98% of its payments manually. Today, 98% of its payments are processed automatically. Additionally, they saved $1.8M by optimizing their credit card processing fees.
The Road Ahead
While AI-driven automation in AR management is still developing, its potential to revolutionize operations is undeniable. AI solutions offer promising opportunities to address recurring AR challenges, optimize collection practices, and improve cash flow forecasting accuracy. Future advancements in real-time analytics, autonomous collection workflows, and integrated financial system connectivity are likely to further enhance these benefits. Businesses that adopt AI solutions today are positioning themselves for stronger financial health, increased operational efficiency, and improved customer relationships in the future.
About the Author
Glenn Hopper is an author, speaker, and lecturer on the intersection of AI and corporate finance. He recently published the book AI Mastery for Finance Professionals . He is the Head of AI Research and Development at Eventus Advisory Group and holds a Master of Liberal Arts from Harvard University and an MBA from Regis University.
FAQ
It automates routine tasks, provides predictive insights, reduces manual processing time, improves accuracy, and accelerates cash flow through intelligent automation. .
AI finance tools use advanced algorithms and machine learning to automate tasks like matching payments to invoices, handling complex scenarios, and reducing manual intervention.
Agentic AI refers to artificial intelligence systems designed to autonomously perform tasks, make decisions, and achieve goals without human intervention.