La Française: Risk and opportunities with the wave of AI

July 15, 2024

Artificial Intelligence (AI) and digitalization carry significant social risks, from job losses in incumbent industries to greater security concerns and increasing discrimination. Generative AI (GenAI) could automate a significant portion of a job’s tasks, leading to potential job losses in occupations affected the most.

There is a major risk of misalignment between short-term financial incentives from the development and deployment of AI, specifically with GenAI and the interests of humanity. A recent study by the Capgemini Research Institute found that 72% of consumers are worried about the misuse of GenAI technology. According to the OECD AI policy observatory’s AI Incidents Monitor (AIM), examples of these risks have massively jumped up since the start of 2023.

There are also negative environmental impacts related to GenAI across the value chain that need to be considered. Carbon emissions across the entire value chain are expected to increase. The competition to build out data centre infrastructure has also raised questions about the capacity of national energy grids to cope with the expected jump in electricity demand linked to AI, and whether there is sufficient renewables generation in those markets to power the technology. E-waste and the need for rare minerals and metals for the infrastructure and production of GenAI applications are other potential risks we need to consider.

For investors, there can be no one-shot way to address the multi-faceted risks posed by the rapid adoption of AI and GenAI in the last two odd years. Engagement and stewardship tools will be most effective. Investors need companies (both developers and deployers) to apply Responsible AI practices across the organization to safeguard against the social and environmental risks that AI poses.

Organizations must establish clear principles for how they apply GenAI and set up guardrails to ensure its safe implementation and to specifically avoid bias, discrimination, misinformation and breach of privacy. While some environmental impacts, such as end-user energy consumption and data centre power efficiency can change with the wider decarbonisation of the grid, investors are becoming more concerned about the technology sector delivering on their climate goals. Both developers and deployers of AI will need to invest substantially in creating additional renewable power.

Regulations can also help. The EU AI Act formally approved last month is a good risk-based framework to assess and analyse a company's responsible AI governance and risk management systems. Additionally, multiple engagement toolkits have been released in the past six months, such as the WEF Responsible AI playbook for Investors (June 2024), RIA Australasia’s AI and Human Rights Investor Toolkit and AI: An engagement Guide by ICGN (March 2024) that can guide investors to create a robust engagement framework for Responsible AI.

In the Asset management industry, AI models can be used to create innovative quantitative investment strategies and risk management processes and to enhance portfolio returns. According to a Harvard study, GenAI can help to speed up financial analysis, such as reading thousands of pages of data filings to analyse a company’s earnings and future trajectory and analyse huge datasets to spot insights humans simply either would not be able to spot or do not have the time to spot.

La Francaise Systematic Asset Management, an asset management company of Groupe La Française (the holding company of the asset management business line of Credit Mutuel Alliance Fédérale) has created a system to integrate complex non-linear relationships and interactions in financial data. The system uses state-of-the-art machine learning models and combines them with traditional well-proven behavioural model to detect endogenous shocks early, by dynamically responding to the changing market environment. The same principles can be applied to sustainability analysis, where AI tools have been deployed to capture risks faster and more effectively.

At Crédit Mutuel Asset Management, we have been using third-party data sources that are sometimes powered by AI tools to enhance our in-house ESG analysis both quantitatively and qualitatively. AI-based tools have proven to be very effective in capturing controversies and governance failures from anywhere in the world which are impossible for human analysts to track.