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What’s the impact of generative AI in the financial services industry? A panel of experts at the VB Transform 2024 discussed this on Wednesday, offering deep insights.
The panel featured leaders from Bank of America, Brex, Google, and Cerebrus, who shared their views on how AI is transforming the financial services sector. They highlighted that generative AI is being used for a range of purposes, including customer service, engineering support, and improving operational efficiency.
David Horn, head of AI at Brex, explained that generative AI is remarkably effective at simplifying complex financial topics. This is particularly beneficial for smaller businesses that can’t afford a dedicated Chief Financial Officer (CFO). With generative AI, complicated financial concepts can be simplified into easy-to-understand language, effectively providing a digital CFO capability for these businesses.
How Bank of America is testing out generative AI for financial services
Awais Bajwa, head of Data and AI Banking at Bank of America, highlighted the significant promise of generative AI. One of the key use cases at Bank of America involves enhancing developer efficiency and productivity within their large engineering team of over 10,000 developers. Generative AI also aids knowledge workers by enabling more efficient information processing and summarization. Bajwa mentioned that future applications might include customer-facing recommendations and automation in customer service, though these are still in early exploration stages.
Bajwa stressed the importance of explainable AI for Bank of America. He emphasized that understanding the data and the model’s training process is crucial.
Generative AI is bringing new insights to financial services data
Financial services organizations handle a vast amount of data. Zac Maufe, global head of regulated industries at Google Cloud, noted that generative AI is a catalyst for unlocking better insights from this data. Even though we live in a data-rich world, he mentioned that we often lack meaningful insights. The reason for this, according to Maufe, includes technology constraints and organizational preferences that lead to data silos.
Maufe believes the future lies in obtaining faster and more accurate insights from both structured and unstructured data. He also pointed out that concerns about regulations and compliance tend to slow the adoption of generative AI in financial services. Many deployments are currently for internal use only, with human oversight as a control point. However, Maufe envisions a near future where generative AI is even more prominent in the financial sector. Efforts are ongoing in areas like explainability and embeddings to make generative AI more mainstream.