There are patterns hidden in your data. They may be difficult for humans to parse quickly, but predictive analytics can break down these hidden patterns and build new ones that offer insight into what might or might not occur based on your current actions. From an accounts receivable (A/R) perspective, being able to predict the future — or at least, the probability of a future event — seems an invaluable tool to managing your revenue streams and overall cash flow.
Obviously, there are no guarantees, but with clean data and the right accounts receivable analytics software, you can begin making your financial decisions with less guesswork and more confidence. In this article, we’ll delve deeper into the nature of predictive analytics, how it is intersecting A/R, and what benefits or challenges that may create.
What is predictive analytics in accounts receivable?
Just as its name would imply, predictive analytics is a method of predicting the future by reviewing or analyzing historical data. In more detail, this process leverages existing records, real-time metrics, advanced algorithms, and machine learning to identify past trends and determine the probability that the same or similar patterns might recur in the future. In A/R, predictive analytics would leverage your billing and collections records and data to predict the payment behaviors of current and potential customers.
How predictive analytics in accounts receivable works
The actual operations will depend heavily on the A/R analytics platform you choose. In general, your solution will begin with data collection — either employing data mining techniques or pooling all the relevant information into a central data repository.
This information would typically include:
- Existing records, metrics, and reports related to customers (e.g., payment histories, credit reports)
- Sales
- Your invoice to cash (I2C) processes
- Your overall A/R performance
Your solution will then sift through this data, detect existing patterns, and build corresponding predictive models — such as the likelihood that a given customer will pay on time or what your cash flow might look like next month.
Ideally, these models will be tagged with a probability score that will give you actionable insight on how to move forward with your A/R operations. And when you’ve refined your models enough to be consistently accurate, you can begin leveraging them to build more proactive strategies around credit policies, cash flow forecasts, and more.
Why predictive analytics in A/R matters
Predictive analytics delivers a clear competitive advantage by helping you optimize your A/R, accelerate payment cycles, and avoid unnecessary risk — among other advantages. Essentially, it empowers you to convert what has traditionally been a more reactive business function of collecting and processing payments into a proactive, strategic part of your fiscal management.
Common challenges of predictive analytics in A/R
Of course, in business, there are no free lunches, so while this technology offers several clear advantages, there are some drawbacks you’ll need to consider and potentially overcome to employ it. Drawbacks like:- Risk: While predictive models frequently provide probability scores to indicate the likelihood of their occurrence, there are no guarantees, and acting on this information is still a gamble.
- Bad input: Your models will only be as good as the underlying data, so if you have outdated, inaccurate records in your system, expect inaccurate results.
- Data vulnerability: The more software and systems you provide access to your sensitive financial and customer data, the more potential avenues of attack you create for scammers and criminals.
- Up front costs: Whatever solution you choose, you’ll have to pay for it, and commonly, the more nuanced and reliable the analytics, the higher the price tag.
- Skills gaps: Depending on the complexity and usability of your analytics platform, you may need to hire additional staff — or put them through time-consuming training — to effectively use the solution.
- Regulatory concerns: Depending on where you are located, your predictive accounting efforts may need to comply with government regulations, such as the California Consumer Privacy Act (CCPA) or the General Data Protection Regulation (GDPR).
Accounts receivable predictive analytics examples
Fine-tune payment cycles to cut out delays and other interruptions
By analyzing when and how your customers pay their outstanding invoices, you can build models that help identify and address process bottlenecks, problem customers, and other factors before they cause a delay. For instance, you might identify that if you send out a gently-phrased dunning notice two days before the set due date for customers with fewer than 100 employees, they are 80% more likely to pay on time.Simplify planning efforts with accurate cash flow forecasting
Beyond managing individual payment timelines, predictive models can help project larger trends such as how much revenue you’ll gather from your customers over the next two months. With a more precise income model — particularly one that will become more accurate over time — you can make smarter, more informed decisions when budgeting for future operations and growth.Manage credit risks by leveraging real-world data
Imagine a platform that could pore over the payment history, credit rating, and currently reported financials of a potential buyer and assign them a risk score that identifies how likely they are to render final payment. And forewarned with this information, you could, in turn, authorize a given business-to-business (B2B) purchase, reject it, or adjust payment terms to compensate — such as requiring an up-front deposit or setting up an installment billing plan.Resolve customer issues before they can get worse
Do you know which customer is about to rage quit from your subscription plan? Or which one is likely to register a dispute on a recent invoice? Through predictive accounts receivable you can not only anticipate these occurrences, but you can also take measures to deal with the underlying problems before they have an opportunity to ruin a business relationship.Benefits of implementing predictive analytics in accounts receivable
Notably, the benefits generated via predictive analytics align nicely with the already mentioned use cases.Accelerate cash flow and drive more efficient A/R processes
When you can proactively avoid payment pitfalls, you spend less time, energy, and labor chasing each invoice, reining in your A/R spend and freeing up staff to focus on other, more strategic efforts. At the same time, you will also shrink your days sales outstanding (DSO), meaning you’ll have faster access to the capital from each sale.Create a more flexible business while supporting growth
With faster, more consistent access to cash and the ability to better predict when future funds will arrive, you can build out detailed blueprints for growth plans, technology refreshes, or other projects with more confidence. At the same time, your business will possess the financial agility to respond to market shifts or other opportunities in the moment.Stop working for free and avoid overdue invoices
If you know how your customers are likely to behave, you can avoid exposing your business to unnecessary risks, instead building payment terms, dunning policies, late fees and other incentives that encourage prompt, complete payments. Similarly, you can project which buyers — and potential buyers — are most likely to comply with these established guidelines, limiting the possibility of accruing bad debt.Maintain healthy customer relationships with proactive support and problem resolution
Managing churn is another critical function of effective, proactive A/R. And when you can accurately predict when customers are going to dispute an invoice, cancel a subscription, or miss a payment (and incur late fees), you can often get out in front of these challenges and develop solutions with them that are mutually beneficial. At the same time, you can also more easily identify and walk away from problem customers before they burn up more of your time, energy, and resources.Predictive analytics in action with Invoiced
Ultimately, predictive analytics deliver a clear competitive advantage to both your business and your financials. And we believe that the Accounts Receivable Automation platform offered here at Invoiced by Flywire can help get you started. Our automated invoice workflows and broad integration capabilities help ensure that you only have highly accurate financial data on hand to fuel your broader analytics efforts. Meanwhile, our Cash Collection Forecasting tool will yield highly accurate predictions on when you can expect to receive payments from your customers, empowering you to better manage your cash flows. So if you’d like to move forward with and reap the benefits of your own A/R-focused predictive analytics program, schedule a demo today.
FAQs on predictive analytics in accounts receivable
1. What’s the difference between predictive analytics and regular A/R reporting?
Traditional reporting captures and records the past and present performance of your A/R operations. Predictive analytics, on the other hand, analyzes this reported data to uncover patterns and build models that forecast what is most likely to occur in the future.
2. Can predictive analytics improve cash flow forecasting?
Yes. Cash flow forecasting is an ideal use case for predictive analytics. By applying advanced algorithms and machine learning against your historical cash flow data, you can create projections of what those cash levels will look like in the near future.
3. What types of data are used in predictive A/R models?
As you might suspect, analytics engines will pull data from sources across all your A/R operations. This might include: invoices, purchase orders, receipts, financial records, A/R aging reports, customer relationship management (CRM) records, credit bureau data, payment histories, dispute and chargeback histories, payment terms, currency fluctuations, reports on industry trends, interest rates, inflation projections, sales notes, call logs, customer satisfaction scores, and more.
4. How does Invoiced use predictive analytics to improve collections?
Invoiced’s Cash Collection Forecasting tool uses historical payment data and machine learning to predict when invoices will be paid, helping finance teams prioritize outreach, automate reminders, and proactively manage collections.
5. How is Invoiced’s approach to predictive analytics different from other platforms?
Invoiced integrates predictive analytics directly into its A/R automation tools, using machine learning to deliver real-time payment forecasts and risk insights. Unlike standalone analytics solutions, Invoiced ties predictions to actionable workflows, so you can automatically adjust follow-ups, credit terms, and cash flow planning based on forecasted behaviors.
