Ethics Oversight Board: Utilization of AI in Report Writing—Third Quarter 2023

Utilization of AI in Report Writing: Is This Ethical? How About This? 

By Karen M. Lascelle, CPA, CVA, CFE
NACVA Ethics Oversight Board

This December, Reneé Fair, CVA, and I will be presenting “Ethical Considerations in AI and the Impact on Valuation”at the NACVA and the CTI’s 2023 Business Valuation and Financial Litigation Super Conference in Fort Lauderdale. Our objectives include understanding what artificial intelligence (AI) is, identifying five key ethical considerations and examples, learning how to establish relevant frameworks within valuation practices to avoid ethical dilemmas, and showcasing resources available regarding NACVA’s professional ethics. Preparation for this has me contemplating AI and the roll it plays in our everyday lives, and how it affects the valuation world and the work we do for our clients, including the manner in which we portray ourselves and our work product. Would it be ethical for us to utilize AI in our valuation reports? If we did, how might we go about doing so?

Utilizing AI to create a valuation report can enhance its quality, efficiency, and accuracy. Here is how you can make your valuation report look its best by incorporating AI:

1. Data Gathering and Preprocessing:
   • AI can automate data collection from various sources such as financial databases, market reports, and company filings.
   • It can preprocess and clean the data, ensuring accuracy and consistency.
2. Predictive Modeling:
   • Implement machine learning algorithms to predict valuation factors, like revenue projections, growth rates, or market multiples.
   • Include AI-driven regression models, decision trees, or neural networks to make more accurate predictions.
3. Risk Assessment:
   • Use AI-powered risk assessment tools to identify and evaluate potential risks associated with the valuation, including market volatility, regulatory changes, and economic conditions.
4. Market Analysis:
   • Employ natural language processing (NLP) to analyze news articles, social media, and industry reports to gauge market sentiment and trends that can impact valuation.
5. Financial Modeling:
   • Utilize AI to automate financial modeling, which can generate various scenarios and sensitivities to assess the impact on valuation under different conditions.
6. Automated Reporting:
   • Implement AI-driven reporting tools that can generate structured and visually appealing reports automatically. These reports can include tables, charts, and graphs to make the data more understandable.
7. Customization:
   • Tailor your AI algorithms and models to the specific industry or sector you are valuing, as different industries may require different valuation approaches.
8. Explainable AI:
   • Ensure that the AI-driven valuation models are interpretable and provide explanations for their outputs. This can build trust with stakeholders and regulatory bodies.
9. Continuous Learning:
   • Incorporate AI models that can adapt and learn from new data over time, improving the accuracy of valuations as new information becomes available.
10. Compliance:
    • Make sure your AI-driven valuation process complies with relevant regulatory standards and guidelines in your industry, such as International Valuation Standards (IVS) or Financial Accounting Standards Board (FASB) guidelines.
11. Human Expertise:
    • While AI can automate many aspects of the valuation process, it is crucial to have human experts review and validate the AI-generated results. Expertise and judgment are still essential in valuation.
12. Data Security and Privacy:
    • Implement robust data security measures to protect sensitive financial data, especially when using AI for data processing and analysis.
13. Peer Comparison:
    • Use AI to compare your valuation results with similar companies in the industry, providing context and benchmarks for stakeholders.
14. Presentation:
    • Design a clear and professional layout for your valuation report, incorporating AI-generated visuals and insights in a user-friendly manner.
15. Audit Trail:
    • Maintain a comprehensive audit trail that documents the AI-driven valuation process, from data collection to model selection and validation.

By integrating AI into the valuation process while ensuring transparency, accuracy, and compliance, you can create valuation reports that are more comprehensive, efficient, and valuable to your stakeholders. Remember that AI should augment human expertise rather than replace it, so a combination of AI and expert judgment is often the best approach.

Looking at the above 15 points brings a major question for me: do I agree with all of these points? Well, not entirely. Why? Well, there are different reasons depending on the point. But a major issue is that I did not write them. I opened up ChatGPT, typed in “How can my valuation report look best utilizing artificial intelligence” and Default (GPT-3.5) spit out the above narrative in approximately five seconds (beginning with “Utilizing artificial …” And ending with “… often the best approach.”). Now, that is not to say that there is not some very good advice in there. Much of it does not pertain to what I asked. I was not asking about performing the valuation itself with AI. I was only asking about the report. And I certainly was not looking for that extensive of a narrative, but overall, I think you can see that it was well written for the most part. Well written enough that if I was reading it from another author, I may not have known that they did not write it themselves. That is where the ethics issue really stands out for me. Are we now getting to a point where people are not taking responsibility for the content they are writing versus the content “others” are writing for them? How are we to know what is real? What is tested and tried and true? You can be sure that when we analyze this topic further in December, we will come armed with our own opinions and thoughts, and not just some thoughts generated by AI.