10 Ways Banks are Using Generative Artificial Intelligence Right Now!

Banks are the life blood for many small and medium-sized businesses across North America. They often know the community, business leaders and local business environment better than anyone else.

Their typically fiscally conservative but willing to try new business practices or technology if they can see a clear value proposition for their customers and their bottom line. Is AI something that could differentiate their businesses?

The answer is a resounding YES!

Savvy, forward thinking bankers are increasingly turning to Generative AI to enhance their competitive edge. Here are ten ways they’re doing it, complete with real-world examples:

AI in Banks (see use links below)

Personalized Customer Experiences: Banks like Wells Fargo are pioneering Large Language Models (LLMs) to create multimodal banking experiences, offering personalized services [3].
Efficient Risk Assessment: Commonwealth Bank of Australia (CBA) uses Generative AI for enhanced customer experience and combating financial abuse [3].
Fraud Detection: JPMorgan Chase employs Generative AI to analyze emails for signs of fraud, improving their detection systems [5].
Automated Customer Service: Bunq has introduced a GenAI bot for its banking app, streamlining customer service interactions [3].
Market Trend Analysis: Deutsche Bank provides AI-powered personalized investment guidance, helping customers navigate market trends [3].
Regulatory Compliance: HSBC is training AI software to manage back-office functions, ensuring compliance with regulations [4].
Operational Efficiency: Royal Bank of Canada (RBC) uses AI to automate process-heavy tasks, increasing operational efficiency [4].
Credit Scoring Models: Banks are refining credit scoring with AI, though specific examples are under proprietary use and not publicly disclosed.
Investment Strategies: AI-driven models assist banks like Deutsche Bank in developing robust investment strategies [3].
Customized Banking Products: Generative AI helps banks create financial products tailored to their community’s needs, as seen with Wells Fargo‘s LLMs [3].

5 Ways Community Banks Can Integrate Private LLMs to Safeguard Corporate Content

Community banks are also cautiously adopting Private LLMs to protect sensitive data and reduce AI Liability exposure. Here’s how they’re doing it:

Data Control: Morgan Stanley Wealth Management uses an advanced chatbot powered by OpenAI’s technology to leverage the bank’s research and data library while maintaining data control [7].
Customized Solutions: Banks can develop bespoke tools that align with their operational models, similar to ABN AMRO‘s use of Generative AI to summarize conversations between bank staff and customers [7].
Secure Customer Interaction: By managing customer data securely, banks like ABN AMRO ensure privacy and build trust with their customers [7].
In-House Expertise: Developing in-house expertise with Private LLMs allows banks to produce more accurate and relevant insights, as demonstrated by Morgan Stanley [7].
Strategic Innovation: Private LLMs enable banks to innovate strategically, developing unique services that differentiate them from competitors, much like Morgan Stanley‘s approach [7].

In conclusion, banks are not just keeping pace—they’re setting the pace, using Generative AI and Private LLMs to redefine the banking experience for their customers and secure a competitive edge in the digital age. However, they do need to keep pace on the evolving AI Legal Landscape as new precedents are set and new laws hit the book. See this video for more on AI Legal Challenges.

This article includes specific examples of large banks and community banks using Generative AI, Private LLMs, Data Security and with references to further information on their applications.

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