Generative AI and conversational AI technology have the potential to greatly enhance the work of finance and accounting professionals. By automating manual and transactional activities, AI enables finance professionals to focus on higher-value tasks such as strategic planning and analysis. Generative AI specifically empowers faster and more accurate data-driven decisions by identifying patterns and anomalies in historical data. However, there are considerations and risks associated with the use of generative AI models in finance and accounting.

Generative AI refers to AI models that can generate new data based on existing data. These models are built using foundation models, which are AI models trained on vast amounts of data to identify patterns and make predictions. Generative AI and foundation models can significantly improve the accuracy and effectiveness of financial analysis and decision-making.

Balancing Risks and Rewards

It is important to balance the benefits of generative AI with its potential risks. While generative AI can provide valuable insights and streamline financial processes, there is a need for transparent and open AI training to ensure accuracy and avoid misleading results. Finance and accounting professionals should work together with information technology experts to train generative AI models and ensure they understand the context of financial concepts.

IBM’s Institute for Business Value (IBV) recommends implementing secure AI-first intelligent workflows in finance and accounting. This involves prioritizing which finance and accounting use cases should be augmented with generative AI models based on precision, risk, stakeholder expectations, and return on investment. The promise of generative AI in finance and accounting is evident, with executives expecting a significant increase in its use in the next year.

Key Considerations for Generative AI

When applying generative AI technology in finance and accounting, decision-makers should keep the following considerations in mind:

• Be good stewards of financial insights generated by generative AI models. Human validation and accountability are crucial, as well as the ability to answer stakeholder questions about AI-generated content.
• Apply the controllership lens by considering risks, materiality, and financial exposure. Operators should validate the accuracy and completeness of inputs and outputs and map use cases against key control matrices.
• Internal business partnering is necessary to ensure effective analysis and reporting. Alliances between finance and other business units should be established to advance trust and collaboration.

IBM Consulting’s finance and accounting practitioners can provide valuable insights and best practices for implementing generative AI in finance operations. By embracing generative AI, finance and accounting professionals can streamline processes, enhance critical functions, and improve overall efficiency.

Frequently Asked Questions (FAQ)

What is generative AI?

Generative AI refers to AI models that can generate new data based on existing data. These models are trained on vast amounts of data to identify patterns and make predictions. In finance and accounting, generative AI can enhance data-driven decision-making by identifying hidden patterns and anomalies.

What are foundation models?

Foundation models are AI models that serve as the basis for generative AI models. They are trained on vast amounts of data and can identify patterns and make predictions. Foundation models are foundational to generative AI in finance and accounting.

?

Why is transparent AI training important?

Transparent AI training ensures that generative AI models understand the context of financial concepts and produce accurate results. It also allows for continual critique and improvement of AI models. Transparent AI training is crucial for avoiding inaccurate or misleading results in finance and accounting.

How can generative AI benefit finance and accounting professionals?

Generative AI can benefit finance and accounting professionals by automating manual and transactional activities, freeing up time for higher-value tasks such as strategic planning and analysis. It can also improve data-driven decision-making by identifying patterns and anomalies in financial data that may be missed by traditional analysis methods.

What considerations should be kept in mind when applying generative AI in finance and accounting?

When applying generative AI in finance and accounting, decision-makers should consider the need for human validation and accountability, apply the controllership lens to manage risks and controls, and establish strong internal business partnerships to ensure effective analysis and reporting.

Sources: IBM Blog