There’s no doubt about it. Generative AI promises to transform how financial institutions conduct analysis, research markets, and serve clients. While there will always be a need for human oversight and ingenuity, using AI can make everything more efficient for financial professionals. For researchers, advisors, analysts, and other financial professionals, generative AI allows streamlining time-intensive processes—which allows more time for analysis to better advise your clients.
In this article, we explore 5 key applications of AI in finance and give some examples of how you can start using AI in your daily work, so you can have more time and energy to provide high-value strategic advice to your clients.
Analyzing markets, trends, and opportunities
Good financial advising requires extensive reading and analysis, conducting research on trends, markets, economic conditions, geopolitical issues, and other factors that may impact investments. If you are in advising role, you’re likely working with multiple clients at once, and you may not have time to do the level of research you need for each client.
With generative AI, you can streamline your research processes by asking the tools to analyse and summarise big reports to enhance your decision making. For example, if you’re working with clients looking at investment opportunities in Latin America, you could provide this prompt to your AI tool:
"Please analyse World Bank emerging market reports, IMF forecasts, and news articles from the past 6 months to summarize key trends, risks, and opportunities in the Latin American market in a 250-word brief."
From that prompt, the AI tool could rapidly digest these materials and generate a report highlighting key points like:
- IMF predicts 2.5% GDP growth in Latin America in 2023, down from 5%, due to global headwinds
- World Bank identified inflation, rising interest rates, and currency volatility as top risks for the region
- News articles point to Brazil's new pro-business government as a potential catalyst for growth
This would provide a synthesized analysis in seconds versus the hours spent manually gathering and reviewing the latest materials. You could then focus on further strategic analysis specific to their client’s Latin American investment goals.
Automating due diligence and documentation
Due diligence is a critical part of making investment decisions or advising clients. This requires extensive reviewing of thousands of pages of filings, contracts, reports, deals, assets, and documents.
With AI, the due diligence process can be streamlined, eliminating hours spent searching for key details. For example, if you’re a private equity analyst evaluating the acquisition of a retail company, you could use the prompt:
"Please review and summarize key details from the target company's SEC filings, financial statements, market reports, and acquisition agreements over the past 3 years. In a 300 word summary, include analysis of revenue trends, profitability, debt levels, store footprint, leadership changes, and any red flags."
The AI would rapidly digest all these materials and generate a due diligence briefing to highlight key historical financials, risks, and details to provide the analyst with a quick but comprehensive overview of the target.
This would accelerate the document review process that could take weeks manually. The analyst could then focus on building further strategic analysis specific to the private equity firm's acquisition goals.
Drafting client reports and presentations
A key part of your job as a financial advisor is presenting your suggestions to clients. Each presentation should be tailored to your client, but you can speed up the process by using AI to generate a draft.
For example, if you’re preparing a quarterly client portfolio update, you could tell the AI:
"Please review the client's investment statements and performance data from Q2 2022. Then draft a 100-word summary of the client's asset allocation and risk metrics compared to last quarter."
This would provide a customised quarterly update paragraph to include in the client report, which would create a great starting spot for your presentation.
You could further speed up your drafting by asking the following:
- "Generate a chart comparing the client's portfolio returns versus market benchmarks over 1 year."
- "Write 2-3 bullet points summarizing the client's top performing assets this quarter."
- "Draft a 200 word intro for the client report highlighting overall portfolio performance and any recommended actions."
From these drafts, you can refine and customize the reports with tailored messaging and advice for each client, enabling faster reporting.
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Monitoring news and regulatory changes
Staying updated on emerging financial news, regulations, and geopolitical events is crucial but extremely challenging to do manually. Even if you set up alerts, it’s hard to keep up with the pace of change.
With AI, you can automate this daily media monitoring to make sure you’re staying up to date with the latest developments. For example, if you’re an analyst at a hedge fund focused on emerging markets, you could ask the AI:
"Please summarise key political, economic and regulatory news over the past 48 hours related to Brazil, Mexico, and other Latin American countries that may impact our investment strategy and holdings in the region."
The AI would analyse recent news articles, government notices, and other sources to deliver a briefing highlighting the most relevant new developments, including interest rate trends, GDP growth, and changes in legislation.
The AI would serve as an automated assistant, efficiently deliver actionable intelligence to inform investment decisions and strategies in Latin America. The AI serves as an automated assistant, freeing up analysts' time.
Processing and analysing big data
To get up-to-speed on client backgrounds, you often have to process big data sets, including transaction histories, portfolio holdings, customer information, and more. Processing all of this information manually is incredibly time consuming, and it takes away from the time you could spend offering solutions to clients.
With AI, the data analysis can be automated. For example, you could ask an AI tool:
"Please analyse 3 years of historical transactions, holdings, and performance data for Client X's investment portfolio. Identify key changes in asset allocation over time and performance trends in a 250 word summary."
The AI would rapidly process and interpret patterns in the large dataset to generate an overview of things like asset allocation shifts, highest portfolio returns, or where many fixed income purchases came from. This would provide data-driven insights into a client’s unique investment patterns and performance that would be arduous to determine manually. The advisor could then tailor recommendations based on the synthesized data analysis.
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Financial services, AI, and the future
While the above are some of the most practical uses for AI in financial services, Generative AI uses will only continue to evolve and make work more efficient in the future.
For more information on the future of Generative AI, we invite you to view our LexisNexis® Future of Work Report 2024: How Generative AI is Shaping the Future of Work to explore more of ways Generative AI is changing the landscape.