In today’s rapidly evolving financial markets, investor relations professionals are finding themselves at the very forefront of technological advancements that are reshaping the industry.
One of the most transformative technologies making waves – and headlines – is artificial intelligence (AI). With its capability to process vast amounts of both structured and unstructured data and generate valuable insights, AI is becoming an indispensable tool, particularly when it comes to understanding and predicting investor behaviour.
Generative (Gen) AI is further shifting the dial, but there are also challenges to navigate including regulatory compliance, bias in models, and “hallucinations” – where models perceive patterns or objects that are non-existent, creating nonsensical or inaccurate outputs.
Given these considerations, AI excels best as a complementary tool rather than a replacement for the human expertise that drives effective investor relations. By leveraging AI alongside human talent, professionals can harness the full potential of these technological advancements while mitigating associated risks.
A timely transformation for the investor relations model
Financial markets generate colossal amounts of data every day. This data must be contextualised by investor relations teams to form clear and accurate reports that hold value for issuer stakeholders.
Historically, investor relations has relied heavily on manual processes, personal interactions, and an intuitive understanding of market dynamics. However, the sheer volume and complexity of data in today’s markets make it increasingly difficult for investor relations teams to keep pace using such traditional methods.
Institutional investors are increasingly exacting, requiring issuers to provide timely, accurate, and insightful information about financial performance and strategic initiatives. Informed counterparts facilitate more meaningful dialogue, enabling investors to gain deeper insights into company operations and future prospects.
This is where AI steps in, offering unprecedented capabilities to enhance the effectiveness of investor relations.
Machine learning algorithms can sift through these vast datasets rapidly and accurately, deciphering the relevance of, and relationships between, the different strands of data. Natural Language Processing (NLP) tools and Large Language Models (LLMs), such as ChatGPT, can further analyse texts and other content to identify themes and sentiment.
AI’s ability to effectively streamline and contextualise a vast array of data points not only enhances operational efficiency from an administrative perspective but also further eliminates the risk of human error. The resulting transparency and well-informed dialogue not only enhance trust and credibility but also drive long-term growth and resilience across the financial markets.
Examples of AI in action
One of the most compelling use cases for AI is predicting investor behaviour to help tailor strategies.
Institutional investors, such as mutual funds, pension funds, and hedge funds, play a crucial role in the financial markets due to the significant volume of assets they manage. Predicting their behaviour can therefore provide a substantial advantage to companies looking to manage their share prices and market perceptions effectively.
AI can analyse historical trading patterns, macroeconomic indicators, and specific institutional investor activities to generate these predictive insights.
For listed companies, understanding the positions of their shareholders, particularly institutional investors, is crucial for effective Annual General Meetings (AGMs). AI’s powerful data aggregation and analysis capabilities offer more timely insights into shareholder positions and voting tendencies, factoring in a range of elements such as proxy advisor recommendations and emerging industry trends.
AI can then segment institutional investors based on their investment strategies, risk appetite, and historical behaviour. This segmentation allows proactive investor relations teams to customise their messaging and engagement strategies to address the specific needs and concerns of different investor groups.
The impact of AI goes far beyond the AGM, extending deeply into day-to-day activities.
Within seconds, Gen AI can perform tasks for companies that would take much longer for an investor relations professional. This includes drafting proxy statements, quarterly earnings reports, and company background summaries for new shareholders, as well as aggregating publicly disclosed earnings information to create FAQs for shareholders and potential shareholders.
The balance of AI innovation and human oversight
While AI solutions are truly transformative, human oversight and judgement remain of the utmost importance when it comes to investor relations.
Some of the tasks that AI is applied to involves aggregating and analysing publicly available materials, but other tasks can involve sensitive company information that is non-public, confidential, and highly significant to both existing and potential investors. If companies fail to put sufficient safeguards in place, then they risk violating the world’s securities laws.
Organisations must therefore invest in robust data governance practices and make efforts to ensure their AI models are trained on diverse, high-quality datasets and transparent methodologies to help bypass any inherent biases that could damage trust. It’s also essential to build in a layer of human review to detect any potentially damaging hallucinations – there is a fine line between using AI correctly and having an over-reliance on the technology.
Ultimately, the AI journey should be a continual effort, extending far beyond the preparation for the AGM. By automating data aggregation and analysis, AI can free up valuable time for investor relations professionals to focus on strategic initiatives. This shift from data management to strategic engagement not only enhances efficiency but will also strengthen a company’s ability to meet investor expectations and foster more positive long-term relationships.
