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8 use cases for generative AI in finance
Generative AI's data analysis capabilities and ability to help improve reports can help simplify some aspects of a finance department's operations. Learn more.
While CFOs must also be aware of generative AI's limitations, the technology can potentially help improve finance department operations in several different ways.
Finance departments have a reputation for being rather conservative when adopting new technologies, but finance leaders should potentially overcome their risk-averse nature when it comes to generative AI, as long as they take precautions. Some generative AI use cases are financial reporting and summarization, budgeting and forecasting, expense management, tax preparation and compliance, strategic decision-making, fraud detection and prevention, M&A, and employee training and documentation.
Learn more about some of the key use cases for generative AI in finance.
1. Financial reporting and summarization
Financial statements and performance summaries can be complex documents. Generative AI can summarize key points and draw attention to potentially otherwise overlooked elements.
AI can also help with writing reports. AI can adjust messaging and tone so reports resonate with specific stakeholders, such as executives, investors or employees.
However, human reviewers should double-check the reports for accuracy because generative AI can make factual errors.
2. Budgeting and forecasting
Finance departments have long used predictive analytics and other analytics tools to generate budget scenarios. However, users must generally define these scenarios clearly. Generative AI can create multiple budget scenarios based on historical data and test various economic scenarios without precisely defined parameters.
AI-driven forecasting can enable CFOs to make better-informed decisions.
3. Expense management
Credit card companies often divide consumers' statements into certain spending categories. Finance departments can do the same at a more sophisticated level by using generative AI that was trained with historical data.
Generative AI's ability to categorize large volumes of expenses can make it easier for finance department employees to identify anomalies and unnecessary costs.
4. Tax preparation and compliance
Generative AI can help users create accurate and detailed documentation with clear audit trails for regulatory reviews. In addition, AI tools can monitor tax laws and provide recommendations for action if tax laws are changed.
In some cases, generative AI may be able to prepare tax filings and reports that are compliant with current regulations. However, expert human reviewers are essential for that process.
5. Strategic decision-making
The generative AI applications for scenario planning are somewhat similar to budgeting and forecasting, but in this case, generative AI simulates a range of future possibilities, such as supply chain disruptions or macroeconomic changes.
Generative AI can suggest tailored mitigation strategies, helping CFOs potentially make better decisions.
6. Fraud detection and prevention
Generative AI learns from large volumes of historical data and is skilled at anomaly detection.
Generative AI's capabilities can help with fraud detection and prevention and also help uncover new patterns of activity, such as a change in customer spending patterns or new economic conditions, which can help CFOs plan for new trends.
7. M&A
One of the most time-consuming processes a finance team can undertake is conducting due diligence for potential M&A. The process always involves many documents dating back years.
Generative AI's ability to summarize reports and financial data and identify key points can be valuable for M&A. Generative AI can also highlight opportunities and risks in potential M&A scenarios.
8. Employee training and documentation
Every company has its own operating procedures and guidelines, and finance department procedures can be especially detailed.
Generative AI can develop training modules for employees and automatically update financial policies as they evolve, in addition to customizing each training module for specific job roles.
Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.
