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Generative AI has changed the landscape for businesses as it goes beyond basic search functions to synthesize, summarize, contextualize, and analyze large volumes of information.
For researchers, analysts, advisors, and other professionals at investment banks, wealth management firms, insurance companies, and other financial institutions, generative AI enables the automation of tedious research steps. Rather than having analysts spend long hours pulling data from various sources, firms can use specific prompts to have generative AI systems instantly produce summaries, analyses, and insights to accelerate due diligence.
While producing fully customized client deliverables still requires human skill, generative AI gives financial teams a major boost in productivity. Research that once took days or weeks can be completed in hours or minutes. This allows professionals to focus on higher-value work like drawing strategic conclusions, advising clients, and tailoring recommendations.
In this article, we’ll review some top strategies for financial professionals to integrate generative AI into their workflow to improve efficiency and grow faster.
Conducting due diligence on new investments requires extensive research into associated industries, underlying market conditions, political factors, consumer behaviors, and more. In the past, analysts would manually gather data from disparate reports, cross-reference it, and build models to surface key trends and projections.
With generative AI, however, an analyst can simply prompt the system by asking something like “Provide an overview and forecast of the cloud computing market over the next 5 years using the latest industry research and financial data." Within seconds, they can receive a comprehensive synthesized analysis detailing market size, growth projections, leading players, risks, and opportunities to quickly inform investment decisions.
This means that you can get an understanding of the financial landscape faster so you can make the necessary business decisions or better support your clients to get ahead in the market.
MORE: How to use big data analytics in finance
Ongoing monitoring of portfolio companies and potential investment targets requires in-depth profiling to assess risks and opportunities. Detailed profiles covering financials, leadership, products, customers, supply chain, ESG factors, subsidiaries, and operations traditionally take many hours per company to compile manually.
Generative AI allows financial analysts to input a target company name and promptly receive a robust profile, including summaries of the company's history, founders, total employees, leadership team, top products, revenue and profit metrics, market share, facilities, and recent M&A activity.
For example, you could ask a generative AI program to generate a 250-word overview profile for an organization you may be looking to acquire including history, founders, revenue and profit metrics, leadership team, recent acquisitions, business segments and top products.
The AI would synthesize available data into a robust company profile in seconds, serving as a starting point for further analysis and illuminate the direction for future business decisions.
By automating this profiling, generative AI for financial services saves dozens of hours otherwise required for manual research. The AI-generated profiles serve as strong starting points which analysts can then build upon with further customized diligence tailored to the investment goals.
MORE: How to capitalize on generative AI to enhance decision-making
Monitoring earnings reports and lengthy regulatory SEC filings like 10-Ks is essential for tracking existing investments. But these filings often exceed 100 pages making quick analysis difficult. Generative AI can review earnings transcripts, 10-Ks, 10-Qs, and other filings to produce concise summaries of key statistics, performance drivers, leadership commentary, forward guidance, and risk factors.
Rather than needing to read voluminous text-based financial data or filings end-to-end, analysts can leverage AI summaries to efficiently extract the most important insights, financials, and strategic priorities communicated by management teams. This enables faster market research and more informed investment decisions.
MORE: SWOT analysis 101 for financial services
From new regulations and monetary policies to mergers, scandals, and executive changes, prompt awareness of developments impacting financial markets is crucial but challenging to achieve manually. Generative AI can rapidly scan and analyze relevant news articles, blogs, government notices, and other sources to highlight the most important updates for financial institutions on a continuous basis.
By prompting the AI to deliver recaps of the latest news and regulatory activity, analysts stay continuously informed of any breaking developments or emerging risks that may warrant adjustments to investment strategies or client recommendations. The AI can also be tuned to focus on specific industries, companies, or geographies based on its prompts.
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While generative AI promises major efficiency gains, financial institutions need responsible adoption strategies that address:
MORE: Overcoming the top 8 challenges in generative AI
Generative AI has immense potential to automate tedious research tasks for financial services. While human oversight is still needed, AI can analyze endless information faster than any team.
Applied prudently, generative AI allows financial firms to automate repetitive tasks, enabling staff to focus on high-value work. But to manage risks, governance, security, validation processes and continuous human oversight are necessities. Within a responsible AI framework, financial institutions can leverage generative tools to unlock major productivity gains and knowledge discovery.
For more exploration of how Generative AI is changing the business landscape, download the LexisNexis® Future of Work Report 2024: How Generative AI is Shaping the Future of Work.