At a glance
- Predictive analytics is becoming more accessible to accounting professionals.
- AI tools can help accurately forecast risk and optimise cash flow.
- Start small with existing software or low-cost AI.
Every day, accounting professionals help clients and stakeholders make crucial business decisions using financial data. Historical numbers can now be transformed into powerful insights about future risks and opportunities through predictive analytics software.
For many practitioners, these tools might sound out of reach, requiring data scientists and enterprise-grade systems. But as technology becomes more accessible and affordable, companies of all sizes are finding ways to harness it, using client and company data to make strategic decisions to shape the future of business.
What is predictive analytics and how can small and medium practices use it to deliver value?
What is predictive analytics?
Predictive analytics is a type of data analytics that uses statistical techniques, AI and machine learning to analyse data and forecast future events that humans might miss. It’s the technology that helps Netflix suggest what you might want to watch next.
Michael Spear, Manager (Data & AI) at BDO Australia, says many accounting professionals already use some form of predictive modelling.
“Your typical accountant, whether they’re working out future expenses, reviewing likelihood of payment by debtors, or forecasting future cashflow using Excel, is processing historical evidence or data, and combining it with their own knowledge, to make a statement about a future value. That’s a form of predictive modelling,’ he says.
However, unlike traditional reporting and forecasting, which typically projects past trends forward, predictive analytic software can make it easier to predict outcomes.
Firms around the world use them to leverage clients’ transaction, customer, operational and economic data to spot patterns and deliver data-driven recommendations. For instance, rather than simply forecasting cash flow based on previous months, these tools can factor in multiple variables to create more accurate predictions:
- Seasonal trends
- Economic indicators
- Industry patterns and
- Weather data.
Spear notes analytics are a growing trend thanks to the broad adoption of generative AI tools and impressive use cases from the likes of Airbnb – the company uses significant data sets to determine how to leverage supply-demand information to change pricing models by the minute.
“It seems like all predictive analytics is new, but it’s not. Every day, accountants are building models that can be so complex that they can rival a lot of these machine learning and AI models,” he says.
The business case
Newer predictive analytics tools that use AI, such as the IBM Watson Analytics suite and Alteryx, apply real-time data modelling to identify risks and opportunities in pricing, expected revenue, cost analysis, balance sheet analysis and cash flow forecasting.
Shantell Williams, Chief Technology Officer at Tiimely, says these kinds of tools enable businesses to “anticipate customer needs, optimise risk management, and make faster, more accurate decisions”.
“It’s not just about analysing past behaviour—it’s about proactively shaping better financial outcomes.”
Instead of spending hours poring over spreadsheets to identify trends or anomalies, these systems can instantly flag patterns and potential issues across entire client portfolios. Although Spear warns against becoming overly reliant on new AI tools.
“Accounting professionals know their business inside out”, she says.
If the modelling is producing surprising predictions or patterns, question the reasons behind the data The data inputted into the model could be incomplete or inaccurate, the type of model could be wrong for the task, or, sometimes, the unexpected results can highlight opportunities or risks for the company.
For firms that primarily provide clients with standard compliance work, investing in predictive analytics tools could mean offering higher-value advisory services.
For example, the same data used for BAS lodgment data could rapidly:
- Identify seasonal trends affecting cash flow
- Flag clients at risk of working capital shortages
- Spot growth opportunities in specific revenue streams
- Predict potential compliance issues before they occur.
While pricing for these services is still evolving, early adopters are taking various approaches. Some practices bundle predictive analytics into existing advisory packages, and others create standalone services. The key is matching the service level to client needs and demonstrating clear value.
Making predictive analytics work for you
For smaller practices, the traditional barrier for specialised analytics technology has been cost and complexity, but there are now multiple entry points for firms ready to enhance client service.
Choose the right tools
- If you’re new to predictive analytics and want to forecast a future outcome, consider starting with a lower-cost tool like ChatGPT.
- Check if your existing accounting software offers predictive AI tools, like Sage Intacct.
Start small
- Begin with a specific service area like cash flow forecasting rather than a practice-wide roll out.
- Test with a small group of clients in industries with seasonal fluctuations or rapid growth.
Build your capabilities
- Consider having one team member develop deeper analytics expertise.
- Invest in targeted training programs to bridge knowledge gaps.
Remember, the real value isn’t in the predictions themselves, but in your interpretation of what they mean for your business or clients. This is where you’ll differentiate your services and demonstrate your value.
Details about IPA’s digital short course on Artificial Intelligence for Accountants here.
