They say a rising tide lifts all ships. This aphorism is particularly on the nose when assessing the recent surge of generative AI use cases in the banking and financial services sectors.

More broadly, generative AI has proven to be a game changer in numerous sectors. The technology’s neural networks mimic organic brain function to rapidly analyze large datasets, find patterns, and make decisions.

Applications based on generative AI have been used in everything from demand forecasting, supply chain optimization, and product development to helping healthcare providers manage medical inventory.

Financial services is a conservative, cautious industry by nature, typically lagging in the adoption of new technology, relying on trusted third-party vendors for development, and focusing on safe use cases that minimize the risks of exposing sensitive data. Today, it’s doing so in several key areas that could provide gateways to further incorporation.

AI in Banking and Finance Use Cases

Major institutions and companies began to experiment with the earliest iterations of the generative adversarial network (GAN) technology with note-taking apps that could transcribe and suggest a next course of action. Today, we’ve advanced to such futuristic uses as automated chat-enabled conversations with end users.

Below are four major banking and finance use cases for AI adoption, along with examples from companies exploring them right now.

Infographic: Behind the Algorithm: Exploring Generative AI Use Cases in Banking and Finance

1. Contact Centers

Contact centers are the nerve center of a company’s customer service operations. From interactive voice response (IVR) systems to robotic process automation, businesses have worked to embrace technology that enhances contact center productivity without replacing human interaction.

Generative AI serves as a kind of neural sidekick to both customers and support engineers, offering assistance in completing technical support requests, accessing product information, and automating routine tasks.

AI in Action

Ally.ai, developed by Ally’s in-house technology team and powered by Microsoft Azure OpenAI Service, is a pilot program designed to assist associates in transcribing and summarizing live customer service phone calls.

The trial results have been impressive, with approximately 82% of summaries generated by Ally.ai requiring no human modification. With this significant reduction in manual effort, associates can focus their energies on more meaningful customer interactions.

Ally’s approach to integrating generative AI is guided by three core principles:

  1. Prioritizing use cases that enhance employee productivity and optimize internal processes
  2. Implementing human intervention and controls for oversight and training
  3. Safeguarding customer data privacy by preventing the disclosure of personal identifying information

Another noteworthy outcome of AI-human collaboration in call centers is the accelerated advancement of less-tenured representatives, who can perform fewer administrative tasks and spend more time engaging with customers.

By eliminating the drudgery of rote or mundane tasks, generative AI provides what humans from any other age in history would have considered superpowers, enabling teams to focus on higher-value interactions and abstract problem-solving.

2. Code Writing

Generative AI coding tools use particularly large language models (LLMs) to generate full or partial lines of code.

ChatGPT and GitHub Copilot, for instance, are trained on a diverse range of examples from publicly available sources. They can already understand and replicate the syntax, patterns, and paradigms found in human-generated code.

GitHub Copilot, a crowd favorite among developers, epitomizes the rising tide of AI-powered coding assistance. The tool provides contextualized suggestions based on an organization’s codebase, allowing developers of all experience levels to enhance their coding efficiency and productivity.

This symbiotic relationship between developers and AI coding tools extends beyond individual productivity gains. By promoting the use of descriptive variable names, documentation, and explicit code comments, generative AI facilitates more maintainable and collaborative coding practices.

Natural language interactions with AI chatbots further enhance this development ecosystem.

AI in Action

Goldman Sachs has been experimenting with generative AI tools for some time, seeking ways to incorporate them into business practices while maintaining separation from client-facing financial products.

In one of their longest-running generative AI projects, Goldman Sachs developers are using AI to generate code and documentation and to test that code. Rather than replacing developers, generative AI streamlines and facilitates their work — in some cases, providing up to 40% of a developer’s code.

3. Search and Synthesis in Data-Intensive Fields

Financial data could account for as much as 5% of the GDP, according to McKinsey. It stands to reason that the ability to efficiently search, synthesize, and derive insights from vast troves of data would be virtually priceless.

Gen-AI is reshaping the way organizations navigate and extract value from their intel.

At the forefront is Microsoft 360’s groundbreaking tool, Semantic Index for Copilot, which creates a structured representation of information that’s easily searchable and retrievable.

Semantic Index has been likened to a digital librarian; however, instead of merely categorizing books by their titles or authors, the librarian employs their vast knowledge of the books’ contents to group them by subject matter and theme. This approach improves the accuracy of search results and helps to generate sophisticated, multilingual responses.

The value proposition of Semantic Index for Copilot lies in its ability to democratize access to knowledge and insights.

AI in Action

Morgan Stanley Wealth Management (MSWM) announced a strategic partnership with OpenAI to develop a bespoke chatbot with access to over 100,000 research reports and documents.

The initiative will enable financial advisors to analyze large amounts of data and receive responses in easily understandable formats sourced exclusively from MSWM’s intellectual capital.

Morgan Stanley spearheaded previous AI projects such as Next Best Action and Genome, which used data analytics and machine learning to deliver personalized messages and communication to clients.

4. Content Creation

The impact of generative AI in content creation extends well beyond consumer interaction to encompass broader marketing strategies, particularly in business-to-business (B2B) marketing.

Marketers use AI-generated content to design highly personalized messages for the unique needs and preferences of the companies they target. By analyzing company data, industry trends, and customer behavior, generative AI enables marketers to craft bespoke outreach campaigns that resonate with decision-makers and influencers within organizations.

The real secret weapon of generative AI in content creation, however, lies in its ability to adapt and evolve in real time based on feedback and engagement metrics. By continuously learning from user interactions and refining its content-generation algorithms, gen-AI enables marketers to stay ahead of the curve and deliver content that drives meaningful engagement and conversion.

What’s Next: Generative AI Decision-Making

One of the primary focuses for generative AI in banking and financial services is augmenting decision-making processes without venturing into risky financial recommendations or underwriting.

When we’re successful at this, financial institutions will be able to use gen-AI to influence decisions safely.

In the meantime, gen-AI is impacting operating models across the board. For many companies and organizations, integration requires major adaptations, both upstream (executive level) and downstream (contact centers).

Obstacles and Challenges

Undoubtedly, certain obstacles stand in the way of deploying enterprise-grade AI for banking and financial services use cases.

Finance and Banking Regulations

Regulatory limitations and compliance remain the biggest challenges. Just this year, we saw the ratification of a seminal AI law in the European Union. In the U.S., senate hearings were held and executive orders passed.

More regulations loom on the horizon, and that’s a good thing — responsible stewards of AI technology want a strong regulatory framework. For now, we have to accept the complexities involved in how governments and multinational banks will approach AI.

Complex Integration

Then there’s the practical consideration of how to integrate gen-AI into an overall operating model, which can be its own challenge.

When you add a process or tool to an existing system, you have to consider what’s being removed or altered in order to accommodate it. Generative AI will rarely replace the entirety of any one aspect of your business, which means even the most trail-blazing innovation must fit well within existing systems.

A thorough assessment of your business’s current state — in the area of innovation and in any other potentially affected area — allows you to identify both opportunities and possible unintended consequences to account for. With this kind of thorough, strategic approach, you ensure the net value of the innovation isn’t rendered ineffective by gaps, friction, or oversights in the ecosystem.

Whether it’s improving customer interactions, streamlining internal processes, or enhancing product offerings, the overall experience for users and employees must remain a central focus throughout.

Integration Example

Contact centers as an AI use case for banks offer an excellent illustration. Customers can already visit a bank teller or call a phone number to discuss an issue. If the bank adds AI chat as an online option, they now have to consider how this addition affects the existing methods, plus consider new oversight, governance, exception rules, etc.

In this way, the innovation introduces complexity even while creating efficiency.

For a successful AI integration, the bank must employ change management strategies to incorporate the update into their overall system. Otherwise, hidden costs may prevent them from capturing the value they originally foresaw.

Integration of AI for banking and financial services use cases requires a transition, evaluating how the technology will impact the experience of users across different touch points within an organization.

Quote: Behind the Algorithm: Exploring Generative AI Use Cases in Banking and Finance

Use Cases for AI in Banking and Finance: Final Thoughts

Uncovering appropriate banking or finance use cases for AI takes a strategic and nuanced approach. Businesses want to both create better experiences and reduce costs without either inhibiting the other.

Method recognizes the crucial balance between efficiency and experience in financial services, drawing from our extensive industry expertise to help businesses achieve that win-win scenario. If you’re looking for a partner who has the ability to analyze your ecosystem for relevant opportunities, send us a message today.