Complete guide to Accounts Receivable AI

If you constantly chase late payments, spend hours on manual tasks, or struggle to forecast cash flow accurately, it may be time to integrate AI into your accounts receivable processes.

Accounts receivable AI uses machine learning and natural language processing to handle routine tasks, predict when customers will pay, and personalise communication. Unlike traditional automation, it’s more flexible and adapts to each customer’s behaviour and history.

In this guide, we will explore the key features and benefits of accounts receivable AI and explain how businesses and self-employed people in the UK can implement it.

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What is accounts receivable AI?

Accounts receivable AI involves using artificial intelligence to streamline traditional AR tasks, from entering data and sending invoices to automating payment reminders and analysing data for insights into customer behaviour.

AI handling these tasks leads to more efficiency in the AR process, faster payment collection and improved cash flow forecasting. According to a recent study, 71% of organisations that implemented AI in their financial operations were able to significantly improve cash flow management and reduce payment delays by 30%¹.

While traditional AR automation also reduces errors and improves efficiency, accounts receivable AI goes a step further by learning from data, adapting to changing customer behaviour and making intelligent decisions that help you and your team work more proactively.

How do accounts receivable AI work?

Accounts receivable AI is powered by technologies such as machine learning, natural language processing, robotic process automation, and predictive analysis.

• Machine learning: Machine learning allows AI to learn from data and continuously improve and adapt. It helps accounts receivable AI analyse past payment patterns to make predictions, send personalised follow-ups and get better at prioritising high-risk accounts over time.
• Natural language processing (NLP): This technology helps AI understand human language on a deep level and communicate empathetically. In accounts receivable, NLP powers chatbots that can chat with customers, answer payment questions, explain invoices, and help resolve disputes.
• Robotic process automation: Robotic process automation is the technology that allows accounts receivable AI to carry out repetitive tasks like data entry, invoice matching and payment reconciliation, giving you more time to focus on other activities like growing revenue.
• Predictive analysis: Predictive analysis technology helps accounts receivable AI to make predictions using insights from historical data. With this technology, AI-powered tools can forecast late payments, predict cash flow, or assess credit risk.

Key features of AI in accounts receivable

Accounts receivable AI has several key features that ultimately help improve cash flow management and operational efficiency.

• AI-powered invoice generation and delivery: You can use accounts receivable AI to create and send invoices automatically based on billing schedules, contract terms, or specific events. This will reduce manual effort, delays and errors.
• Payment prediction using machine learning: AI spots patterns in past data to help you predict cash inflows more accurately and prioritise follow-ups to customers who are likely to pay late.
• AI-powered dunning emails/reminders: Instead of the usual generic emails, AI-powered automation tools can send personalised and timely reminders across multiple channels by looking at customers’ payment habits and past responses. This helps you collect payments faster and reduce friction in follow-ups.
• Real-time credit scoring: AI can assess a customer’s credit risk using real-time data from various sources like payment trends, industry data, and behaviour signals. This way, you can make smarter decisions about extending credit or adjusting terms.
• Dispute categorisation and routing: Accounts receivable AI reduces the time it takes to resolve disputes and significantly improves customer experience. It does this by using natural language processing to identify the type of dispute from customer messages and automatically send it to the right team.
• Smart cash application with NLP/OCR: AI-powered accounts receivable tools use optical character recognition (OCR) and natural language processing (NLP) to match incoming payments to the correct invoices, speeding up reconciliation and reducing human errors.
• Customer behaviour analysis and segmentation: AI can analyse customer behaviour and group them based on factors like payment patterns, risk level or responsiveness. This segmentation will help you be more strategic in communicating with customers, collecting payments and assigning credit.
• Anomaly and fraud detection: Accounts receivable AI helps reduce fraud risk and financial leakage. It does this by detecting and flagging inconsistencies like unusual payment activity, duplicate invoices, or suspicious behaviour by comparing real-time transactions against expected patterns.
• Real-time reporting and analytics: AI-powered accounts receivable tools often contain dashboards that provide real-time data on metrics such as days sales outstanding (DSO), collection rates, outstanding invoices, and customer payment trends. These insights help you make more informed decisions.

Benefits of AI in accounts receivable

• Faster collections and improved cash flow: AI is known for its speed and accuracy. Accounts receivable AI significantly reduces time spent creating and sending invoices and sends timely payment reminders, leading to faster collections.

• Lower DSO (Days Sales Outstanding): Accounts receivable AI features like automated invoicing and payment reminders reduce the amount of time spent waiting for payments.

• Reduced manual workload for finance teams: AI handles repetitive tasks like entering data, sending invoices, and matching payments to invoices, giving you more time to focus on more strategic tasks.

• Fewer errors and disputes: One of the biggest issues with manual AR is that it's prone to errors. By using AI in your accounts receivables, you can significantly reduce errors in invoicing, data entry and reconciliation.

• Stronger customer relationships through tailored communication: AI’s natural language processing helps it better understand customer disputes and respond to queries promptly and empathetically. In fact, a recent study shows that AI responses are perceived as more empathetic than those of trained crisis responders². Additionally, AI can scan customer responses, identify disputes, and send them to the right channel so customers can be attended to quickly. All of this helps to build trust and foster better customer relationships.

• More accurate financial forecasting: With machine learning and natural language processing, AI can process large datasets quickly and easily identify patterns in customer behaviour, leading to more accurate predictions and financial forecasting.

• Lower credit risk exposure: Accounts receivable AI’s ability to assess customer creditworthiness, predict late payments, and spot unusual payment behaviours will help you make informed decisions when assigning credits.

• Scalability without needing to grow the AR team: Ordinarily, more tasks require more team members, but with the right AI tools, you can handle more tasks without needing extra hands. This significantly reduces costs and time spent on hiring, onboarding, and training.

Implementing AI in accounts receivable

Here’s how to implement AI into your accounts receivable processes:

• Access your current AR processes: Map out your AR workflows to identify gaps and areas where AI might be of help. For example, if your team spends a lot of time manually matching payments to invoices, an AI-powered cash application tool could automate that task. Or, if you’re regularly chasing late payments, AI can help predict payment behaviour and send payment reminders at the right time.
• Define clear goals: Decide what you want to accomplish with accounts receivable AI, whether to increase collections, improve forecasting, or reduce manual tasks. Whatever it is, ensure it’s aligned with your broader finance KPIs, like cash flow visibility or operational efficiency.
• Choose the right AI tool or platform: Now that you know your goals, you can choose the best AI tool to meet those needs. Some businesses may decide to develop their own, although it can be time-consuming and expensive. Look out for AI tools that integrate with the accounting software you already use and offer features like prediction, smart reminders, and automated reconciliation.
• Prepare data: AI relies heavily on data to be efficient and accurate. Inaccuracies in data can affect accounts receivable AI operations. Ensure your invoice history, payment records, and customer details are clear, consistent, and properly organised—especially if they come from different systems.
• Do a pilot test: Conducting a pilot test lets you identify and fix potential issues before they become bigger problems. Choose one function to start with, such as sending automated payment reminders. You could choose to test customers with recurring payments or a specific region or customer segment. Set clear metrics, prepare your data, and let the pilot run for a brief period. 4-6 weeks is usually enough time for the pilot to run. Keep an eye on payment patterns and your team’s workload so you can spot any issues early.
• Train your team: For accounts receivable AI to be useful, your team needs to know how to use it. You can provide onboarding, QandA sessions, and clear guides to help them get comfortable and work more efficiently.
• Monitor and improve: Use dashboards and reports to see how well the AI tool is supporting your team’s goals. Track metrics like Days Sales Outstanding (DSO), Collection Effectiveness Index (CEI), and the percentage of overdue invoices.

Accounts receivable AI is the future of accounts receivable automation. By implementing AI in your accounts receivable process, you can drastically reduce manual tasks and the errors that come with them. With AI-powered tools, you can make smarter decisions, strengthen customer communication, and improve cash inflows and the overall financial health of the business.

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FAQs - accounts receivable AI

Here are some of the most common questions:

What is AI in accounts receivable?

AI in accounts receivable involves using artificial intelligence to handle common accounts receivable tasks like invoicing, sending payment reminders, and payment reconciliation. Using AI for accounts receivable operations helps you save time, reduce errors and improve cash flow.

Is Accounts receivable AI suitable for small businesses?

Yes. Any business can benefit from AI regardless of size. Using accounts receivable AI ensures that repetitive tasks do not strain small finance teams, but they have the time and resources to focus on more strategic tasks.

How is accounts receivable AI different from traditional automation?

While traditional AR automation relies on fixed rules and requires manual updates to workflows and templates, accounts receivable AI uses machine learning to adjust timing and messages based on how customers actually pay. It can learn, improve and adapt based on patterns it has identified. For example, it can predict which invoices are likely to be paid late or personalise follow-up messages based on customer behaviour.

How is AI used in accounts receivable?

AI is used in accounts receivable to automate tasks, make predictions, and improve cash flow visibility. It can handle tasks like predicting late payments, automating payment reminders, automating cash applications, and spotting unusual payment activity.

Sources used:

Sources last checked on 24-June 2025.