DBS Bank has had to cross significant hurdles in its years-long efforts to adopt artificial intelligence (AI), during which it realized success goes beyond figuring out the training models.
Data, in particular, proved a major barrier, according to DBS’ chief analytics officer Sameer Gupta. In 2018, the Singapore bank embarked on its journey to leverage AI across four primary areas spanning the development of analytics capabilities, data culture and curriculum, data upskilling, and data enablement.
“The vision here was to use data to drive greater benefits for the organization,” Gupta said in an interview with ZDNET. To do that, he said the bank recognized the need to make access to AI pervasive across the company as well as deliver economic value from AI. The cost of delivering AI solutions also needed to be continuously reduced.
Efforts were geared toward developing the right use cases and talent, including machine learning engineers, and building a data culture that encouraged all employees to constantly think about how data and AI could help with their work. It meant providing a training program that guided staff on how and when to use, and not use, data.
The bank got to work on establishing the infrastructure to facilitate its AI adoption, encompassing the data platform, data management structure, and data governance. It implemented a framework on which all its data use cases must be assessed. Coined PURE, this is based on four principles — purposeful, unsurprising, respectful, and explainable — that DBS believes is essential to guide the bank in using data responsibly.
Its data platform, ADA, serves as a single central source, enabling the bank to better ensure data governance, quality, discoverability, and security.
Today, more than 95% of data deemed useful and necessary to facilitate DBS’ AI-powered operations is discoverable on the platform. The platform holds more than 5.3 petabytes of data, comprising 32,000 datasets that include videos and structured data.
Getting to this point, however, proved a mammoth task, as Gupta revealed. In particular, organizing the data and making it discoverable required significant work, mostly involving manual and human expertise, he said. Laborious hours were spent identifying the metadata, with tools to automate such tasks sorely lacking.
He added that the bank used many applications, each holding data needed to support its AI initiatives.
With data spread across different systems, he noted that “a lot of heavy lifting” was needed to bring datasets onto a single platform and make these discoverable. Employees must be able to extract the data they need and the bank had to ensure this was done securely, he said.
DBS today runs more than 300 AI and machine learning projects, which it says yielded a revenue uplift of SG$150 million ($112.53 million) last year and saved SG$30 million ($22.51 million) in risk avoidance, for example, from improved credit monitoring. These AI use cases cover a range of functions, including human resources, legal, and fraud detection, according to Gupta.
The bank’s AI initiatives are on track to generate further economic value and cost avoidance benefits this year, doubling to SG$350 million ($262.56 million). It is aiming for this figure to hit SG$1 billion ($750.17 million) in the next three years. Singapore’s largest bank, DBS currently has some 1,000 data engineers, data scientists, and data engineers.
No ‘magic bullet’ with AI adoption
Asked if it was exploring the use of generative AI, Gupta confirmed the bank already was running more than 10 pilots, but stressed that it was early days yet. The various teams, including marketing, sales, and IT, would need to have further conversations over the next few months to better understand from these tests how generative AI can benefit the bank, he said.
He added that it also needs to ensure the use of such AI applications continue to adhere to its PURE principles and Singapore’s FEAT principles that guide the sector’s use of AI. Other known risks such as hallucinations and copyright infringements also will need to be assessed, he said.
DBS currently runs 600 AI and machine learning algorithms, which collectively help power interactions with its five million customers across the region, including in China, Indonesia, and India.
That it uses 600 AI models, however, is immaterial, said Gupta, who emphasized instead the aim to achieve the optimal efficiency and accuracy from the least number of AI models.
Highlighting a misconception that the model in itself is everything, he noted that it actually plays a small role in ensuring companies benefit from their AI use.
Instead, they need to work through all technical elements, which should include building in mechanisms to monitor their AI use and continuously gather feedback to identify areas of improvement. It will ensure the organization learns from its application of AI and makes changes wherever needed, including to its AI models and operational processes, as it works out the kinks and plugs the holes.
“You need to persevere to get the full benefit. There is no magic bullet,” Gupta said.
Asked if DBS was using AI to better anticipate outages, such as the disruptions it experienced in the past year, he said the bank is working to identify how it can do better, including tapping data analytics. Noting that many factors can cause spikes in demand, he said there is potential to leverage AI, for example, in operations to detect anomalies and determine next course of action.
He was unable to comment specifically on the service outages, but said a special committee comprising four of the bank’s board members is leading a full review of the company’s technology resiliency. External experts also have been roped in to help with the review, he said, adding that more details will be provided once this is completed.
Last month, human error was revealed to be the cause of DBS’ May outage but was unrelated to the disruption in March. Singapore’s Senior Minister and Minister in charge of MAS, Tharman Shanmugaratnam, said in a written parliamentary reply that the error was found in software used for system maintenance and had resulted in a “significant reduction” in system capacity.
This affected its ability to process online and mobile banking, electronic payment, and ATM transactions, said Tharman, citing the bank’s preliminary investigation.
Funds to help sector adopt AI
Singapore on Monday said it was setting aside SG$150 million ($112.53 million) over three years to further support the financial sector’s efforts to innovate via the use of technology.
The Financial Sector Technology and Innovation Scheme (FSTI 3.0) will continue to facilitate capability development and adoption in key areas such as AI and data analytics as well as regulation technology, or regtech. Specifically, industry regulator Monetary Authority of Singapore (MAS) will look to fuel the adoption of AI and data analytics among smaller financial businesses.
FSTI 3.0 also encompasses new tracks under which funds will be expanded to include corporate venture capital entities and ESG (environmental, social, and governance) projects. MAS also will run open calls for use cases in emerging technologies, such as Web 3.0, with grant funding to be offered for trials and commercialization.
For DBS, the focus now is to ensure its AI projects can scale and access remains pervasive across the organization, said Gupta.
“We need to make sure we’re industrializing how AI is developed and deployed in the bank, so we can reduce the effort to implement it. You can’t do this if every use case is done in a bespoke way,” he noted.
He also underscored the importance of ensuring AI continues to be measured, so the bank is able to determine if it is generating positive outcomes. “We need to ensure there are benefits for both employees and customers,” he added.