Artificial intelligence has already changed the way data is compiled and processed on a mass scale – and in particular in the investment industry, reshaping how professionals make decisions, manage portfolios, and analyze market data. A prominent challenge facing the industry today is the design of executive pay – specifically, structuring it in ways that incentivize strong performance over many years rather than just over a few fiscal quarters.
Proxy advisors’ analysis of executive pay packages for “say-on-pay” voting (the process where shareholders vote to approve or disapprove the compensation packages of a company’s top executives) considers factors like pay-for-performance alignment, the fairness of the package relative to peers, the structure of the compensation (e.g., short-term vs. long-term incentives), among others. The process is data intensive and can be laborious and time consuming.
The data that investors need for informed voting is often scattered across regulatory filings. A more thorough process would require investors to perform independent analysis that goes beyond disclosures. This is practically impossible, with hundreds or thousands of annual votes. As a result, proxy agencies like Glass Lewis and Institutional Shareholder Services (ISS) are often the shortcut to arrive at proxy voting decisions. But they can contribute to short-termism when their guidance focuses on the wrong factors. And their guidance often includes factors that companies themselves choose.
What investors need, and what proxy advisors could provide them, is a more comprehensive view of executive compensation data, stringing together a pay-for-performance narrative over time; a tool that runs back in time to previous awards and forward to future payouts – a movie, not just a snapshot. AI could be applied to scrape and process dispersed data to present it in an organized and standardized way.
With this enhanced data, investors would be better equipped to support the design of longer-term pay plans. The data could even include alternative metrics that we describe in detail in our report on CEO compensation:
- Wealth-at-risk – the amount of money or assets that could potentially be lost due to market fluctuations
- Pay duration – the length of time over which compensation is paid out.
The computations are complex, as are the data needs to run them. But it need not be given existing technologies.
The power of AI, and specifically generative AI, lies in its pattern recognition and predictive abilities, filling in data gaps and automating complex calculations for independent analysis. A significant advancement would be the creation of a comprehensive database, which could play a pivotal role in defining the reporting standard for pay disclosures. Furthermore, it would also aid shareholders in running projections or simulations, adopt new indicators (like wealth-at-risk or pay duration), or reconciling non-standardized pay metrics across many corporate filings with audited financial statements.
There are already examples in the ESG space. MSCI is using AI and alternative data to extract investment-related insights from unstructured ESG data. Clarity AI offers a number of technology-based tools for companies and investors that automate ESG reporting, data collection, and decision making. A partnership between Glass Lewis and Arabesque could bring benefits to broader proxy voting outcomes, not just for executive compensation and say-on-pay voting.
Ultimately, as AI reshapes the landscape of data analysis, proxy advisors can harness it in a way that solves some of the most critical questions in the investment industry.
