Elevating Explainability and Access in Financial Services: How Gen AI is Transforming the Landscape

Impact of Generative AI in the Financial Services Industry

What is the impact of generative AI in the financial services industry? This question was addressed by a panel of experts at VB Transform 2024, offering in-depth insights on the subject.

The panel featured leaders from Bank of America, Brex, Google, and Cerebrus, who discussed how AI is transforming the financial services landscape. Generative AI is being utilized across various applications, including customer service, engineering support, and enhancing operational efficiency.

Generative AI Elevating Industry Standards

David Horn, head of AI at Brex, stated, “Generative AI is very good at raising the floor.” He emphasized the inherent complexity of finance, particularly for smaller businesses that lack resources like a dedicated Chief Financial Officer (CFO). Generative AI can simplify complex financial topics, providing an accessible, natural language understanding that may serve as a digital CFO for many organizations.

Bank of America's Generative AI Initiatives

Awais Bajwa, head of Data and AI Banking at Bank of America, highlighted the significant potential of generative AI. Key applications include enhancing developer productivity within the bank’s extensive engineering team of over 10,000 developers. Additionally, it aids knowledge workers in processing information more efficiently through knowledge discovery and summarization. While customer-facing recommendations and automated customer service present exciting future possibilities, these applications are still in the early exploration phase.

Bajwa stressed the importance of explainable AI in their evaluation process, stating, “We need to understand the weights and the data that the model is being trained on.”

Unlocking Insights from Financial Data

Financial services organizations often manage vast amounts of data. Zac Maufe, global head of regulated industries at Google Cloud, described generative AI as a transformative tool that can help these organizations derive deeper insights from their data. “We live in a data-rich world, but are frequently insight-poor,” he noted.

He acknowledged that data silos—resulting from technology limitations and organizational preferences—hinder effective data utilization. With an abundance of both structured and unstructured data in the financial ecosystem, Maufe emphasized the need for faster and more accurate insights.

Given the inherent regulatory and compliance concerns in the financial sector, the adoption of generative AI may be more cautious. Maufe observed that many generative AI implementations are currently focused on internal use cases, often involving human oversight as a control point. Nevertheless, he envisions a future where generative AI becomes more prevalent in financial services.

“There’s significant progress being made around grounding, explainability, and embeddings to make generative AI ready for primetime,” he remarked.