Data analytics experts and accountants with training and experience can help businesses forecast financial performance and predict future events and trends, providing proactive value and supporting strategic decision-making. Implementing predictive analytics can strengthen financial planning and forecasting, enhance strategic decision-making (including increasing profitability and optimizing operations), mitigate risk, and improve fraud detection. Predictive analytics, therefore, are key to paving a path forward for businesses.
Accountants, especially auditors, are known for evaluating past transactions using descriptive analytics, which summarizes historical data and events to understand what happened to a business’s finances. This type of analytics helps generate financial statements, perform audits, and track key performance indicators. During this process, accountants analyze financial data and identify anomalies such as unusual transactions or patterns that could indicate fraud or errors. Diagnostic analytics are then used to dig deeper and identify why something happened. This analysis of past events, using descriptive and diagnostic analytics, is instrumental to building reasonable forecasts in the future.
Predictive analytics then looks to the future to explore what might happen if specific conditions occur. More specifically, it looks at current and historical data patterns to determine the likelihood that those patterns will recur and to identify trends, correlations and causation within one or more datasets. Predictive analytics uses a series of techniques to forecast future trends and likely outcomes, including predictive modeling, artificial intelligence (AI) algorithms, machine learning, data mining and statistics. Predictive modeling uses various types of data, including historical, numerical (e.g., quantitative data that is continuous or discrete), categorical, textual, and industry-specific. This data can include structured information such as sales, customer data, market and economic data, and a myriad of other types of non-financial metrics.
AI-powered algorithms are the foundational components that enable machines to learn from data, make decisions, and solve problems. These algorithms are built on historical data to construct predictive models. Machine learning is a subset of AI that may enable systems to automatically learn, improve, and make predictions from data without being explicitly programmed. Data mining techniques use machine learning algorithms and statistical analysis to detect patterns in large datasets. Accountants must be mindful, however, that while predictive analytics can be useful, the true value of these results is grounded in the quality and accessibility of the data. As Jason Vogel, Director of Forensic Data Analytics and Technology at CBIZ, notes, “Both humans and AI need to be mindful of the difference between data mining and ‘data dredging’, which is the misuse of statistical significance to support a false conclusion. As we have seen in the news, AI has a penchant for pleasing the user and will skew toward providing an answer that aligns with the stated goal.” Therefore, to effectively use predictive analytics, antiquated financial and data-monitoring systems may need to be updated.
Data visualization is often used to communicate predictive analytics (along with descriptive and diagnostic analytics) in a way that is easy to understand. Data visualization can represent complex data relationships through the use of common graphics, such as charts (pie, stacked, line), graphs, and dashboards. Specifically for predictive analytics, these tools help identify trends, patterns, correlations and anomalies or outliers in data, facilitating better decision-making and forecasting.
Accountants and data professionals can use predictive analytics to advise businesses in several areas, including:
Budgeting and Forecasting: Use historical, financial, and non-financial data to create more accurate budgets and sales forecasts.
Cash Flow Forecasting: Project future cash inflows and outflows to help manage working capital.
Decision-Making: Assist in making informed business decisions in areas like product and service demand, investment strategies, customer behavior, inventory management, and market trends.
Risk Mitigation: Forecast potential areas of risk, allowing for implementation of preventive measures and early intervention.
Fraud Detection: Analyze transactional patterns to identify anomalies and take preventative measures, strengthen defenses against fraudulent activity and intervene quickly to minimize financial losses.
Auditing: Utilize machine learning and algorithms to comb through massive amounts of data in seconds, searching for unusual transactions that would be invisible to traditional auditing techniques.
Tax Planning: Forecast future income and potential tax liabilities to optimize tax planning strategies and minimize tax burdens.
Predictive analytics are also useful to forensic accountants. For instance, instead of businesses waiting for fraud to be discovered, forensic accountants use predictive analytics to identify vulnerabilities and potential threats. Artificial intelligence and machine learning models can assign risk scores to transactions, customers, vendors, or employees, enabling forensic accountants to prioritize investigations in high-risk areas. Algorithms can also analyze historical data to identify patterns relating to prior fraud, then identify new, comparable transactions and events.
Businesses, no matter their size or industry, can benefit from predictive analytics because it provides proactive value rather than a rear-view mirror approach.
Reprinted with permission from the March 3, 2026 edition of the “Legal Intelligencer” © 2026 ALM Global Properties, LLC. All rights reserved. Further duplication without permission is prohibited, contact 877-256-2472 or [email protected].
© Copyright CBIZ, Inc. All rights reserved. Use of the material contained herein without the express written consent of the firms is prohibited by law. This publication is distributed with the understanding that CBIZ is not rendering legal, accounting or other professional advice. The reader is advised to contact a tax professional prior to taking any action based upon this information. CBIZ assumes no liability whatsoever in connection with the use of this information and assumes no obligation to inform the reader of any changes in tax laws or other factors that could affect the information contained herein. Material contained in this publication is informational and promotional in nature and not intended to be specific financial, tax or consulting advice. Readers are advised to seek professional consultation regarding circumstances affecting their organization.
“CBIZ” is the brand name under which CBIZ CPAs P.C. and CBIZ, Inc. and its subsidiaries, including CBIZ Advisors, LLC, provide professional services. CBIZ CPAs P.C. and CBIZ, Inc. (and its subsidiaries) practice as an alternative practice structure in accordance with the AICPA Code of Professional Conduct and applicable law, regulations, and professional standards. CBIZ CPAs P.C. is a licensed independent CPA firm that provides attest services to its clients. CBIZ, Inc. and its subsidiary entities provide tax, advisory, and consulting services to their clients. CBIZ, Inc. and its subsidiary entities are not licensed CPA firms and, therefore, cannot provide attest services.
