What is AI in accounts receivable? Process, benefits and trends

Artificial intelligence (AI) is having an outsized impact in many fields.

From AI-driven sales outreach replacing traditional reps to AI search tools taking over Google as a provider of answers to your most burning questions, it's got many finance professionals wondering:

Is AI coming for my job?

In most cases, the answer is still no, but there is a huge range of ways in which artificial intelligence can support how finance teams work. 

In this article, we’ll be looking specifically at AI in accounts receivable, exploring how it can empower AR teams to work more efficiently and improve their ability to keep cash flowing into the business.

Implementing AI in accounts receivable 

Let’s first take a step back and answer:

What is AI in accounts receivable?

In the finance world, artificial intelligence is the use of computers and algorithms to perform tasks that usually require human intelligence. Common examples include:

• Analyzing large data sets
• Identifying patterns and trends
• Making decisions based on information and goals 

In accounts receivable (AR), this often translates to streamlining credit decisions, automating follow-ups, and predicting payment behavior based on previous transaction data.

The technologies that enable AI to work in AR

In order to mimic human intelligence and apply it to accounts receivable decision-making, AI integrates three important technologies:

1. Natural Language Processing (NLP): Helps systems interpret and respond to customer emails, enabling faster query resolution.
2. Predictive analytics: Analyzes historical data to forecast customer payment behavior and flag high-risk accounts.
3. Cognitive automation: Combines machine learning with business logic to make more nuanced decisions, such as when to escalate collections.

But not all of the tech that AR uses to achieve these goals is strictly AI. 

• Artificial intelligence (AI): Systems designed to mimic intelligence.
• Machine learning (ML): A subset of AI where systems learn from data on their own and improve over time without explicit programming.
• Automation: Rule-based systems that execute tasks automatically but don’t adapt or learn. This can be combined with AI (e.g., knowing what tasks to complete automatically and when), but automation itself is not AI.

How AI automates accounts receivable processes 

AI is transforming how AR teams manage their workload. It's helping reduce manual effort, improving accuracy, and allowing teams to work more efficiently.

Here are some of the key ways it enhances AR processes.

Automating invoice generation and follow-ups

AI can automatically generate and even send invoices based on sales activities or contract terms, which can help to minimize delays and reduce human errors.

These tools can also track due dates and customer behavior (like non-payment) to trigger follow-up emails at the right time or escalate accounts that need human intervention.

Smarter credit scoring and risk assessment

Instead of relying solely on static credit reports, AI can help AR teams assess risk in real time by analyzing a broader set of data, such as:

• Payment histories
• Industry trends
• Macroeconomic indicators

This helps AR teams make more informed credit decisions and adjust terms dynamically based on changing risk profiles.

Streamlining payment processing and reconciliation

Using AI, accounts receivable teams can match payments to invoices with greater accuracy, even if remittance details are missing or incomplete.

AI can also flag discrepancies, suggest corrections, and update records automatically, all of which help to reduce the time teams spend on manual reconciliation and follow-up.

Benefits of AI in accounts receivable management 

Beyond automating tasks, AI brings strategic benefits that directly impact financial health and team performance. Here are five reasons more AR teams are adopting it.

1. Reducing late payments and improving cash flow

By analyzing payment patterns and predicting risk, AI helps AR teams to prioritize high-risk accounts and optimize follow-up timing. This leads to fewer overdue invoices, faster collections, and more consistent cash flow.

2. Enhancing accuracy and efficiency in invoicing

AI reduces human error by generating invoices based on real-time data. It can also automatically validate invoices before they’re sent. This improves billing accuracy and reduces the back-and-forth with customers over mistakes, strengthening customer relationships as a nice side-effect.

3. Strengthening data security and privacy

AI finance tools can help enforce access controls, track usage, and identify anomalies in transaction data that could signal fraud or data breaches.

4. Lowering operational costs

By automating repetitive tasks like matching payments or sending reminders, AI frees up accounts receivable staff to focus on exception handling and strategic work. This reduces the need for additional headcount as your business scales, keeping operational costs low and improving profit margins.

5. Personalizing customer interactions

AI can analyze customer behavior and history to tailor communications, recommend payment plans, or identify which follow-up messages are most effective. This leads to better customer relationships and more effective collections.

Challenges and considerations for AI implementation 

Just as AI is transforming accounting, it has an important role to play in reshaping how AR teams work. But that doesn’t mean that adopting AI in accounts receivable will necessarily be smooth sailing, and there are a few challenges to bear in mind. 

1. Data privacy and security

There are many privacy and ethics concerns surrounding artificial intelligence. In the context of accounts receivable, data security is of specific importance.

That’s because AI systems rely on large volumes of sensitive financial and customer data. 

Businesses have obligations (like GDPR) to protect that information, which means carefully choosing AI systems that implement strong cybersecurity measures is essential to maintaining trust and avoiding breaches.

2. Integration with existing systems

Many accounts receivable functions are tied to legacy ERP or accounting platforms, which introduces a technological challenge when bringing AI into the picture.

Introducing AI systems requires seamless integration with these tools, not least to give them access to the data they need to produce helpful insights. Unfortunately, not all legacy systems are all that open to integration.

As a result, AR teams considering introducing AI will need to assess whether their current tech stacks support integrations via API, or whether they’ll need to introduce middleware to bridge the gap or even replace legacy systems with modern alternatives.

3. Change management and staff training

AI, in many cases, is not a replacement for human workers, but a tool that team members can use to work more effectively.

This means that to get the most out of your investment in AI, your AR team will need specialized training, not just on how to use the new tools but also on how to trust and validate the results.

Buy-in from leadership and clear communication across departments are critical for a smooth transition.

Future trends in AI for accounts receivable 

AI is still in the early stages of transforming AR. As the technology matures, it’s expected to drive major shifts in how receivables are managed, forecasted, and optimized. Here are four trends to watch:

1. Emerging technologies with deeper capabilities: We’re likely to see even more advanced generative AI, large language models (LLMs), and intelligence document processing, which will enable AI systems to handle more complex tasks, like summarizing account status or interpreting unstructured customer emails with great accuracy.
2. More advanced predictive analytics: Today’s AI systems can make some pretty impressive future predictions, but there is still plenty of room to improve. Future models will likely integrate a wider range of data, such as economic indicators or customer-specific financial systems, to better forecast late payments.
3. Redefining customer relationships: Future AI systems will likely enable AR teams to deliver context-aware follow-ups instead of generic reminders, and produce adaptive payment plans that don’t require a lot of human intervention.
4. Autonomous AR workflows: We’re moving toward systems that don’t just support AR teams but actively manage end-to-end workflows. Future AI systems will be able to manage invoice generation, customer communications, and reconciliation with minimal human input.

Getting started with AI in your accounts receivable department 

Adopting AI doesn’t have to be a single-day, full-scale transformation. In most cases, in fact, it's better for AR teams to start small and scale up as they start seeing results for each new step. 

Here’s how to get started:

1. Assess your current AR processes

Begin by mapping out your existing workflows, identifying areas with repetitive tasks, bottlenecks, or higher error rates. These are prime candidates for AI integration.

2. Choose the right tools for your needs

Look for AI solutions that align with your business size, industry, and existing tech stack. Some tools focus on collections automation, while others prioritize credit risk analysis or payment matching. Prioritize tools that integrate with your existing tech stack

3. Follow best practices for implementation

Start with a small-scale pilot before a full rollout to uncover issues early and build internal confidence. Communicate clearly with your team about what AI will and won’t be able to solve. Track metrics like DSO (days sales outstanding), late payment rates, and processing times to evaluate ROI and fine-tune your approach.

Automate AR with BILL

BILL automates accounts receivable to help accountants, business owners to save time, mitigate risk, and fuel growth. 

With powerful tools like streamlined expense management and  fraud prediction, accounts receivable teams can put more resources into the strategic initiatives that move the needle.