The world hit the jackpot with GenAI last year, potentially adding $2.6 to $4.4 trillion across industries every year. In the banking sector alone, we’re looking at a potential boost of $200 billion to $340 billion annually. No wonder Banks and Financial Services companies are already experimenting with different large language models (LLMs) and use cases to see what works best. Starting these pilots is one thing, but scaling up to really reap the benefits is another beast, with costs, risks, and technical hiccups. So, before going all in, it’s smart to take a step back to understand what LLMs are and what they offer.

Whether it’s processing and understanding customer queries at lightning speed, offering personalized financial advice, or detecting anomalies that hint at fraudulent activities, LLMs are equipped to handle tasks with a level of sophistication and nuance previously unattainable. Let’s peel back the layers to see what is on offer and how large players are implementing it in their daily lives.

Customer Service:
Traditionally, customer service in banking relied heavily on visiting a branch in person. While there’s a charm to human interaction, it comes with limitations—availability being a prime one. Imagine providing non-stop, smart service where chatbots aren’t just scripted responders but can actually understand and engage with customers on a personal level, offering precise, tailored advice 24/7.

A prime example is a leading European bank that rolled out ‘BankGPT.’ They also launched an internal tool to streamline IT support for their staff, pulling answers from their own databases. This move to a GenAI-driven platform catapulted their customer service and enhanced internal operations.

The results are significant. They achieved a more secure and compliant information exchange and cut down response times for service desk queries. All by making LLMs central to their strategy, showing the rest of us in banking the power and potential of diving into AI.

Fraud detection:

We cannot overstate the importance of anomaly detection in finance. Traditional systems, often handcuffed by their reliance on historical data, struggle to adapt to the novel and evolving tactics of fraudsters.

Consider the challenge faced by a major US credit card company. With a daily deluge of transactions and a sprawling loyalty program, their traditional monitoring tools were no match for the cunning of modern fraud. The company needed a solution that could keep pace with the rapidly changing credit card fraud, one that could predict and adapt rather than just react. Naturally, they implemented a GenAI solution for this anomaly detection. The new system was not confined to the limits of known fraud patterns. Instead, it could anticipate fraudulent tactics by generating new, hypothetical scenarios, continuously learning and adjusting its thresholds for what constitutes suspicious activity. The system offered real-time alerts for any anomalies, drastically reducing the window for fraudsters to operate. This not only minimized financial losses but also protected the company’s reputation and customer relationships.

Risk Assessment:

A notable pain point for financial institutions has been the reliance on static often outdated financial information to assess creditworthiness.

A major player in corporate banking struggled with evaluations that were either incomplete or lagging, making it hard to make informed, timely decisions. Relying on static data meant missing out on the full narrative of a client’s financial health. The turning point came with the introduction of a GenAI system. Unlike anything they had before, this system could pull real-time data from an array of sources – from financial reports to social media feeds – painting a comprehensive picture of a client’s status and prospects. This is not a simple case of aggregating data. The quantitative and qualitative risk assessment capability has potential to put the bank leagues ahead of where they started

Knowledge Management:

A prominent financial firm’s information was scattered across multiple platforms including Confluence, Jira, SharePoint, Git, and custom systems. Accessing knowledge became a slow and laborious process, especially for new employees who relied heavily on subject matter experts for orientation. The firm needed a way to make it as intuitive as possible for all users.

Real-time data ingestion from diverse sources, combined with the AI’s summarization and question-answering capabilities, meant teams could quickly find relevant information. The LLM-powered platform also included an intuitive search interface and contextual recommendations based on user profiles, making the discovery process hyper-efficient.

We’re seeing these groundbreaking use cases actively rolled out and scaled up worldwide. As trusted tech partners to top banking and financial services firms, we have been on the ground, experimenting with, deploying, and scaling LLM-based solutions. From this vantage point, we have distilled the essentials for selecting the perfect-fit model.

Making the Right Choice

Here’s the crux: banks aren’t looking for a universal tool but rather a solution that aligns with their unique blueprint. The challenge? Not all models serve all purposes equally. Among the crowd, you find a model that excels in customer engagement, making interactions not just transactions but experiences. Another model might specialize in anomaly detection, and a third might be more suited for operational efficiency. A framework in place will help speed up the process.

Start with why

Before diving into LLMs, take a moment to really pinpoint why you’re doing this. Is it to make your customer service feel more understanding? Or perhaps to make sense of vast amounts of data with a human touch?

Bring everyone into the conversation

The magic of LLMs touches all corners of your organization. From the tech team to customer service reps, make sure you’re listening to a variety of voices. Too many transformation efforts fail due to a lack of stakeholder buy-in.

Choose wisely

Picking the right LLM isn’t just a checkbox; it’s about finding a partner in your mission. Consider if the model is adaptable, evaluate how it handles data privacy, and consider the reputation of the vendor. The technology should align with the organization’s values and the values of those it serves.

Measure what matters

Lastly, define success with the right KPIs. Whether it’s improved customer satisfaction, team efficiency, or innovation, track the real-world impact of your LLM integration. Revisit these metrics to ensure the technology is adding value and do a course correction if needed.

Tuning LLMs to customer needs
Integrating LLMs is about strategically enhancing how banks and financial institutions operate and serve their customers. To get it right, we must understand the customer’s requirements deeply and accurately. We must then directly tie the solutions to solving identifiable challenges. By aligning LLM capabilities with specific, articulated needs, organizations can see the ROI we set out to achieve.

Loved what you read?

Get practical thought leadership articles on AI and Automation delivered to your inbox


Loved what you read?

Get practical thought leadership articles on AI and Automation delivered to your inbox


But the real magic of LLMs lies not just in responding to current customer needs but in anticipating future ones—sometimes even before customers themselves realize what they need. This vision is what will set the leaders apart from the followers. So, as we consider the future of BFS in an AI-driven world, the question isn’t just about which LLMs to choose but how we can leverage them to build a future that’s more connected, efficient, and responsive to the needs of the customers we serve. Is your organization ready to rise to this challenge?

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies