Panelist Sameer Gupta

Sameer Gupta

DBS Bank

Singapore

Sameer Gupta is chief analytics officer at DBS Group. He is also head of the DBS Data Chapter, which brings together over 700 data professionals to establish a pool of deep data and AI modeling experts. He has more than 25 years of banking and financial services experience across advanced analytics, AI and machine learning, strategic marketing, customer experience, and change management. Previously, he was the customer care and insights solutions leader for banking clients in the Association of Southeast Asian Nations at IBM. Before that, he spent 16 years in various roles across Asia and Europe with GE Capital. He holds an MBA from the Indian Institute of Management.

Voting History

Statement	Response
Organizations are sufficiently expanding risk management capabilities to address AI-related risks. Agree	“Organizations have been gradually unlocking the potential of data over the years while simultaneously developing data and risk management frameworks to mitigate associated risks. Recently, the adoption of AI/ML and generative AI capabilities has accelerated the industrialization of data usage in organizations, introducing a new set of amplified risks, such as fairness, bias, explainability, ethics, and hallucination. This has led to significant changes in how organizations manage data-related risks, shifting the focus from managing the data itself to ensuring its responsible use. In essence, organizations are expanding their risk management frameworks to address AI-related risks. However, the challenge lies in ensuring that these measures can keep pace with the rapid evolution of AI capabilities, particularly in light of the emerging risks associated with generative AI.”

Statement

Response

Organizations are sufficiently expanding risk management capabilities to address AI-related risks. Agree

“Organizations have been gradually unlocking the potential of data over the years while simultaneously developing data and risk management frameworks to mitigate associated risks. Recently, the adoption of AI/ML and generative AI capabilities has accelerated the industrialization of data usage in organizations, introducing a new set of amplified risks, such as fairness, bias, explainability, ethics, and hallucination. This has led to significant changes in how organizations manage data-related risks, shifting the focus from managing the data itself to ensuring its responsible use.

In essence, organizations are expanding their risk management frameworks to address AI-related risks. However, the challenge lies in ensuring that these measures can keep pace with the rapid evolution of AI capabilities, particularly in light of the emerging risks associated with generative AI.”