Harnessing the Potential of AI in Financial Services

Generative AI Technology Takes Center Stage

New generative AI technologies promise to transform financial services, improving efficiency, personalization, and accessibility. However, responsible adoption requires addressing ethical risks, regulations, talent requirements, and change management.

Generative AI tools like ChatGPT leverage large language models (LLMs) to create customized content. These new tools can personalize financial recommendations, tailor educational materials, and provide guidance to clients at a tremendous scale based on individual attributes and behaviors.

While promising immense improvements, generative AI also comes with risks around bias, transparency, and responsible data usage that firms must mitigate. Managing the people and process changes needed to successfully implement AI is equally vital.

Process Automation and Beyond

Many financial institutions already use another type of AI technology called machine learning to automate high-volume, repetitive processes in areas like compliance, reporting, risk assessments, and fraud detection. Chatbots handle routine customer inquiries, while robo-advisors enable round-the-clock automated investing.

Newer generative AI can take automation even further. For example, by processing and extracting key details from earnings reports, AI tools could prepare standardized inputs for financial models. This could save analysts hours of manual work each quarter.

Democratizing Sophisticated Financial Advice

AI-driven personal finance systems offer the potential to make sophisticated financial planning and wealth management services—traditionally reserved for high-net-worth clients—accessible to more consumers. Topics, once requiring sessions with financial advisors, like estate strategies, tax minimization approaches, and complex portfolio asset allocation, can now be translated into everyday language and made digestible based on a client’s unique priorities.

While AI may automate certain tasks, skilled human financial advisors remain vital, especially for building trusted client relationships over time. Additionally, advisors can spend more time and focus on building client relationships and higher-value financial strategies when they are freed from handling routine questions and other manual tasks. 

Supercharging Research Analyst Productivity

Generative AI tools like ChatGPT promise to transform investment research productivity. Research analysts currently devote substantial time and effort to parsing lengthy reports, filings, and transcripts to track their assigned coverage areas. Now, analysts can use AI tools to quickly process dense documents and automatically flag key statistics, surprises, trends, and other critical signals for review. 

By automating time-consuming tasks like processing, AI allows research analysts to dedicate more of their time and expertise towards critical thinking, interpretation, and expert judgment. AI writing assistants also allow analysts to rapidly stress test assumptions and refine report drafts to avoid unintentional bias.

Centering Ethics and Responsible Usage

While promising, AI is not without risks. Experts recommend that financial institutions implement guardrails to mitigate such risks. One concern lies in perpetuating historical biases that may disproportionately harm certain groups. Since predictive models use information from existing data sets, lingering biases in source data could lead new platforms to generate unfair or unethical content. 

Financial institutions also face escalating regulatory scrutiny around data privacy, security, transparency, and control—even with third parties. The European Union’s AI Act and the US Securities and Exchange Commission’s proposed requirements for broker-dealers and investment advisers would place the onus on business leadership teams to fully understand where client data resides, how it flows between systems, the computations running on it, and data use policies for AI vendor relationships. 

Maintaining consistent human oversight and review procedures remains critical to catch issues immediately and intervene responsibly as AI models access more sensitive data, including client or customer data.

Cultivating AI Talent Through Ongoing Skills Training

Successful AI adoption in financial institutions requires a strong emphasis on developing in-house AI skills across data science techniques, benchmarking, and model refinement. Training should also include veteran staff with institutional knowledge who need to understand how to appropriately apply AI. For example, using proprietary company data with AI models, structuring effective prompts, and implementing human review protocols before sharing content with customers. 

Despite the proliferation of AI tools, human judgment and emotional intelligence remain indispensable when advising clients or handling customer data. Proficient leadership and management skills are essential for banking and finance professionals to leverage AI effectively while prioritizing customer benefits and minimizing risk.

Moving Forward

The outlook for AI in financial services is promising, but realizing its full potential requires a thoughtful approach. Organizations should strategically incorporate AI into existing processes where it can provide clear value and careful planning, governance, and oversight to ensure employee alignment and effective risk management.

Financial institutions that invest in strategic planning and provide purposeful training on responsible AI usage will reap the greatest benefits. This approach not only helps meet ethical and regulatory obligations to customers but also ensures that the adoption of generative AI tools is a success. Balancing this strategic approach with long-term optimism is essential for companies to leverage AI tools effectively and gain a meaningful competitive edge.

Additional Resources

On-Demand Webinar: What Financial Services Firms Need to Know About Generative AI

Artificial Intelligence in Finance & Banking

Financial Services Training–Reimagined