The integration of artificial intelligence (AI) and machine learning (ML) in business valuation and forensic practices is rapidly advancing. On October 29, 2024, the National Association of Certified Valuators and Analysts (NACVA) issued its first Advisory Brief on “The Use of Artificial Intelligence and Machine Learning”, marking a significant milestone for the valuation profession.
This guidance provides a principles-based framework for using AI and ML in valuation engagements while emphasizing the continued necessity of professional judgment. While these technologies can enhance data analysis, predictive modeling, and efficiency, valuators must be mindful of the ethical, legal, and practical considerations involved.
The Role of AI and ML in Valuation
Valuation professionals are increasingly leveraging AI-driven tools to assist in analyzing market trends, financial forecasts, and risk assessments. However, NACVA’s Advisory Brief underscores a critical principle:
AI and ML should augment—not replace—professional expertise.
For instance, AI can identify historical revenue patterns, but human judgment is needed to interpret how industry trends, management strategies, or economic conditions impact future earnings.
Professional judgment remains essential, particularly when:
✔ Reviewing AI-generated cash flow projections
✔ Interpreting intangible asset valuations
✔ Adjusting market data outputs to fit real-world scenarios
Without careful oversight, valuation models could overlook qualitative business factors, industry shifts, or company-specific risks that AI alone may fail to capture.
Ethical Considerations in AI-Assisted Valuation
AI and ML introduce data privacy and ethical risks, particularly when handling sensitive client information. The NACVA brief urges analysts to ensure that AI tools comply with confidentiality standards and do not store or misuse private data.
Common ethical concerns include:
✔ Data integrity – Ensuring AI models use accurate, unbiased, and high-quality data
✔ Privacy protection – Preventing AI chatbots or ML systems from retaining confidential client data
✔ Fair valuation outcomes – Avoiding algorithmic bias that could distort valuation conclusions
For example, an AI tool analyzing industry benchmarks could unintentionally introduce biases from outdated or skewed data sources, impacting valuation accuracy. Analysts must validate AI-generated results to prevent inaccuracies from affecting financial decisions.
Verification: Ensuring AI-Generated Valuations Data is Reliable
AI and ML models can process vast amounts of financial and market data, but their outputs require verification to ensure consistency and reliability.
Key steps in verifying AI-assisted valuations include:
✔ Comparing AI-generated forecasts with manual calculations in Excel or statistical software
✔ Cross-checking historical financial data to ensure alignment with AI projections
✔ Benchmarking results against industry norms, macroeconomic indicators, and peer-group analyses
For example, if an AI tool predicts a company’s revenue growth rate to be far above industry averages, analysts must question whether the AI model overlooked key variables, such as seasonality or market saturation.
Transparency is also critical—valuators should disclose the use of AI-assisted data analysis while ensuring that human judgment remains central to decision-making.
Staying Ahead: Continuous Learning and AI Integration
As AI technology continues to evolve, business valuation professionals must commit to ongoing education and training. Staying updated on AI advancements enables valuators to differentiate between helpful AI applications and unreliable automation.
Key Takeaways for Valuation Firms:
✔ Adopt AI cautiously—treat it as a supporting tool, not a decision-maker
✔ Ensure AI outputs are accurate by applying professional judgment and verification
✔ Protect client confidentiality when using AI-powered financial models
✔ Invest in training to stay ahead of AI-driven valuation trends
A balanced approach—combining AI efficiency with human expertise and ethical considerations—will be key to leveraging machine learning successfully in business valuation. By embracing technology responsibly, valuation firms can enhance their efficiency without compromising accuracy, integrity, or professionalism.
Conclusion
The NACVA’s Advisory Brief on AI and Machine Learning represents a crucial step in guiding valuation professionals toward responsible AI adoption. As valuation firms integrate AI-powered tools into their workflows, they must prioritize professional judgment, ethics, and verification to maintain credibility in financial reporting, M&A advisory, and forensic engagements.
AI will not replace business valuators, but firms that strategically use AI while upholding valuation standards will gain a competitive edge in an increasingly data-driven market.
