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DBS’s AI and Machine Learning Journey
DBS first ventured into AI experimentation in 2012, with efforts accelerating in recent years. These include applying generative AI solutions to customer outreach, credit underwriting and hiring processes.
The company’s ambition to become an AI-powered bank is supported by its robust data and AI foundation, combined with agile working methods. This approach has enabled the deployment of AI at considerable scale and speed.
In 2018, recognising the competitive advantage of industrialised AI, DBS began a comprehensive and “aggressive” transformation to build a robust data platform.
This initiative involved assembling 700 data professionals to efficiently deliver impactful business outcomes at scale, promoting responsible data usage and establishing a single source of truth to power all analytics and AI solutions.
These foundations produced significant results. The bank developed over 350 AI use cases and delivered more than 800 AI models. It also reduced the time-to-value from AI and machine learning from 18 months to two-to-three months.
Building on this foundation, DBS has rapidly identified and deployed generative AI use cases to unlock new opportunities and enhance customer service. Currently, the bank has over 20 generative AI use cases under implementation, with an estimated 90% of employees having access to AI tools at work.
In a 2022 DBS blog post, Gupta remarked that the bank’s AI utilisation was scoring just two or three on a scale of 10, highlighting the technology’s untapped potential for the bank’s operations.
He added that whilst digitalisation was considered “table stakes” at most banks at that time, DBS’s AI and machine learning capabilities had already placed it significantly ahead of competitors.
Tan Shu Shan is set to succeed Gupta on 28 March 2025.
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