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More than 180 zettabytes is the projected volume of data created, captured, copied, and consumed globally for 2025, according to Statista. For today’s accounting firms, the question is how can they benefit and leverage their business clients’ data to deliver the strategic guidance that businesses need? For many, the answer lies in accounting predictive modeling.
In recent years, the adoption of artificial intelligence (AI) has been on the rise within the accounting profession as a growing number of firms explore how the technology can drive efficiencies and help them better serve clients.
With the increased adoption of AI has come a marked shift in data analytics, given its ability to process vast amounts of data quickly. By swiftly identifying trends, patterns, and anomalies, accounting professionals can deliver more data-driven, strategic insights to their clients. Within tax advisory, for instance, firms may use AI tools to generate predictive insights to help clients navigate future tax implications based on their financial decisions.
To help accounting professionals better understand how predictive modeling can benefit their firm, this article explores what accounting predictive modeling is and how firms can use it to gain greater insights into their data.
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What is predictive modeling in accounting?
In short, predictive modeling in accounting uses data-driven insights to evaluate future trends, mitigate potential risks, and identify opportunities for clients. AI-powered algorithms build upon historical data, as well as external and non-financial data, to construct predictive models.
This makes the reporting method significantly different from the profession’s traditional rear-view mirror approach, in which a summary of a client’s financial performance is provided after the fact.
“It is a cumulative type of value. As you go up the analytics food chain, you start off with what happened and then also trying to understand why it happened. With predictive, you are starting to incorporate all of that historical data to start predicting what might happen,” said Domingo Huh, lead UX designer for Thomson Reuters Labs.
Added Huh, “For businesses, it is all about improving that business intelligence and hopefully then being able to go into a more prescriptive type of analytics where you can get recommendations that are actionable. So, rather than just trying to understand what might happen, maybe there is another step where it can definitely give you suggestions and remedies.”
Given the benefits to be gained and the rise in client demands, it comes as no surprise that more and more firms are looking to provide predictive modeling. As Huh noted, some benefits include:
- Enhanced financial forecasting
- Improved fraud detection
What types of data are most useful for predictive modeling?
There are various types of structured and unstructured data that prove to be useful when doing predictive modeling. Such data types include, but are not limited to:
- Historical financial data
- Market and economic data
- Non-financial performance metrics, like client sentiment
However, it is critical to keep in mind that to unlock the true value of data, it must be quality data that is easily accessible. Too often, businesses are hampered by legacy systems that lack sufficient integration and automation. This results in data that is siloed and difficult to access.
“The importance of all of this is that there’s always going to be an explosion of different types of data that can be useful to predictive modeling,” said Huh. “But the big hurdle right now for a lot of firms is: how do you get that [data] into a place that is easily accessible and can work well together in combinations to get the information that you are looking for?”
What are the main types of data analytics?
To further understand predictive modeling, it may prove helpful to review the four main types of analytics and how predictive analytics fits in. Let’s take a closer look.
- Descriptive analytics (what): This examines what happened in the past. Techniques include data mining and aggregation, metrics reports, and summary statistics.
- Diagnostic analytics (why): This is rooted in the past and examines why an event happened. Techniques include principle components analysis, sensitivity analysis, and regression analysis.
- Predictive analytics (if): This looks to the future to explore what might happen if specific conditions occur. Techniques include predictive modeling, machine learning algorithms, and quantitative analysis.
- Prescriptive analytics (how): Looking to the future, and anchored by a set of rules, this examines which actions are best based on the desired outcomes. Techniques include AI, simulation analysis, and recommendation engines.
Huh also highlighted a fifth type of analytics that is beginning to emerge: cognitive analytics. “[Cognitive analytics] is really more about combining AI and machine learning to mimic human-thinking processes or thought processes and decision making.”
Added Huh, “I think the future is quite exciting because there is an opportunity for us to change the way that we interact with data. Whether you want to collaboratively walk through data or rapidly get a task-based answer, it will be powered by these analytic capabilities. And every interaction leaves a trail of learning opportunities for both the professional and their AI counterpart.”
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What are the predictive modeling types?
Predictive models fall into two buckets — non-parametric and parametric — and each one not only has a specific use but also leverages different techniques like descriptive, diagnostic, predictive, and prescriptive analytics.
What is the difference between non-parametric models and parametric models? Non-parametric models can adapt to complex and irregular patterns within data because they do not rely on predefined parameter settings. Parametric models, on the other hand, make predictions based on a fixed number of parameters.
The more common modeling types include, but are not limited to:
1. Classification model
This modeling type delivers simple responses (i.e., yes or no responses) to questions — making it one of the most used models. It leverages historical data to produce a broad analysis of a query. For instance, a financial institution may use it to quickly get data-driven answers to questions like, “Is this applicant likely to default?
2. Outliers model
This model analyzes datasets to identify anomalies or outlying information. This is an ideal model for identifying instances of fraud. For example, financial institutions can use this model to identify unusual transactions in a consumer’s account and determine whether a third party has breached a consumer’s account.
3. Clustering model
Businesses may turn to this model type to determine marketing strategies for certain groups of consumers. What makes it an attractive model for marketing? It clusters data into different categories based on similar characteristics, and then uses the data from each group to determine broader outcomes for each cluster.
These are just a handful of the various predictive modeling types. As noted earlier, each model has a specific use case and leverages different techniques and data types to achieve its intended goal. To determine the best approach, firms should gain an understanding of the different modeling types.
How are firms using predictive analytics to gain insights into their data?
There are currently several ways in which firms can leverage predictive analytics. These could include estimating a client’s cash flow over a specific period, forecasting the impact of changes in taxation, predicting when expenses could rise, or helping establish new supply chains.
Additional ways in which firms could use predictive modeling include:
- Fraud detection: Predictive analytics can help protect business clients against costly fraud-related losses by analyzing patterns and identifying anomalies.
- Strategic decision-making: Firms can use it to analyze trends and help clients make more informed business decisions in areas like investment strategies and budget allocation.
- Tailored services: Firms can use it to help clients predict the demand for their products or services. This helps ensure that clients appropriately allocate resources and, ultimately, experience a boost in profits.
The future of predictive analytics in accounting
When asked if he had thoughts about the future of AI and analytics in accounting, Huh said, “I think it’s more important than ever now to start exploring and trying to be bold with the techniques that you employ. We’re starting to see some interesting movement towards immediate tooling on the fly. If I can write a prompt that creates a tool instantly for me, then we’ve certainly entered into a whole new type of future.”
In many respects, the future is here. Firms are already using AI to conduct research and gain insights from their data at never-before-seen speeds. Is your firm among them? Take action today to ensure that your firm isn’t left behind.
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