Cutting-edge finance teams are trying to figure out how to use AI to boost efficiency and increase finance’s organizational value-add.
AI’s predictive capabilities are already revolutionizing how finance approaches risk management, analyzes historical data, detects market trends and incorporates real-time information into forecasts.
Glenn Hopper of Eventus Advisory Group speaks extensively about finance AI use cases, including GenAI, and has some ideas about where finance will go from here.
In this interview with our Katie-Kuehner Hebert, Hopper, CFO of Eventus until two months ago when he became head of AI R&D, details how CFOs have been leveraging AI and how they can expand its use across the finance function to support strategic planning and decision-making.
How could CFOs expand the use of AI?
One of AI’s key promises is increasing the automation of repetitive and time-consuming tasks like data entry, bank reconciliations and AP/AR workflows. Automation would reduce the risk of human error and free up valuable time.
Very shortly, AI could transform the way finance executives approach regulatory compliance. AI-powered systems could assist in meeting complex and ever-evolving regulations by automatically analyzing and interpreting legal documents, flagging potential violations and generating compliance reports.
How will AI large language models (LLMs) reshape the finance landscape?
Traditionally, analyzing financial statements has been time-consuming and labor-intensive, requiring manual review and interpretation of lengthy, detailed documents. Part of the inherent power of LLMs is that they can automatically extract relevant information from financial statements, including KPIs, risk factors and management commentary.
LLMs’ natural language processing capabilities can quickly identify trends, anomalies and insights that human analysts might miss. Similarly, in risk assessment and management, LLMs can analyze various unstructured data sources, like news feeds and regulatory filings, to identify risks and red flags. By monitoring unstructured data sources in real-time, LLMs can signal emerging exogenous risks, such as market volatility, geopolitical events or reputational threats, allowing CFOs to make timely adjustments.
Will there be applications in analysis and planning?
Scenario analysis and stress testing are areas in which LLMs could assist, helping assess potential impacts on financial performance and testing resilience. LLMs are also opening the door to new financial analysis and research forms. By processing and synthesizing large volumes of unstructured data, including earnings call transcripts, analyst reports and market research, LLMs can generate valuable insights and recommendations for investment decision-making.
What are the challenges of LLM adoption?
Successful adoption requires careful consideration of data quality, model interpretability and ethical implications. Finance professionals will need to collaborate closely with data scientists and technology experts to harness the full potential of LLMs.
In addition, a significant investment in data infrastructure, talent acquisition and change management will be the foundation of a successful implementation. Navigating the ethical implications of AI, ensuring that algorithms are transparent, unbiased and aligned with corporate values and societal expectations, is essential. It can’t be an afterthought—it should be part and parcel of any AI implementation.
From a practical standpoint, how does an organization approach AI integration?
Like rolling out any new platform, implementing AI into workflows should be rolled out in a phased and “agile” approach. An iterative process prioritizes high-impact/low-risk use cases and builds organizational buy-in.
Finance should identify areas where AI can deliver the most value, such as automating repetitive tasks, enhancing risk assessment models or improving investment analysis. Starting with targeted pilots allows for validation of AI benefits and can help build a compelling case for broader adoption.
AI integration also demands a shift in culture, skills and processes. That means embracing experimentation and fostering collaboration with cross-functional teams, including data scientists and IT professionals. Building a solid data governance framework, ensuring data quality and addressing ethical considerations are critical components of a successful integration.
