Generative AI For Banking
And Financial Services

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.

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.

A notable pain point for financial institutions has been the reliance on static often outdated financial information to assess creditworthiness.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

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.