In an era of digital transformation reshaping industries, artificial intelligence (AI) is crucial. From streamlining operations to personalising customer experiences and enhancing risk management, AI’s potential is vast, but its application requires a blend of vision, technological innovation, and ethical responsibility.
Reflecting industry-wide shifts, DBS Bank has moved from AI experimentation to an organisation-wide approach.
It applies AI to enhance customer service and improve operations.
DBS Bank’s chief data and transformation officer, Nimish Panchmatia told iTnews Asia that the bank has created over 100 algorithms that analyse over 15,000 customer data points to provide personalised financial advisory to customers.
“We are exploring how to utilise behavioural and location data to serve specific customer segments – parents and avid shoppers – effectively,” said Panchmatia.
For small and medium enterprises (SMEs), the bank is leveraging AI-powered algorithmic models to identify potential credit risks to help businesses manage finances, he added.
DBS has transitioned from having over 240 experimental generative AI projects to over 20 practical use cases in different stages of implementation.
One is the CSO (customer service officer) Assistant, rolled out to 500 customer service workforces in Singapore, Hong Kong, Taiwan, and India.
Panchmatia says the tool helps CSOs manage over 250,000 monthly customer queries.
The system transcribes, summarises, and recommends solutions for customer queries in real time, helping CSOs access relevant information and provide timely responses, he added.
This has led to a 20 percent decrease in the average time per service request.
Accelerating AI deployments
Panchmatia said the bank focuses on three foundational pillars, which include process, technology, and people.
On the process front, we’ve created a standardised approach called the DBS AI Protocol (ALAN), which has enabled us to scale to over 800 models and 350 use cases across the bank.
– Nimish Panchmatia, Chief data and transformation officer, DBS Bank
It enables data scientists to reuse past models and techniques, streamline AI projects by locating relevant data, and reduce development time.
He highlighted that the technology infrastructure is centered around the bank’s internal ADA Platform (Advancing DBS with AI), which holds over 5.3 petabytes of data.
This enables the bank to personalise customer interactions.
On the people side, the bank has established a programme – Data Chapter – with 700 professionals for reskilling and upskilling.
This focuses on technical skills including coding and softer cognitive skills, including data-driven thinking and responsible data usage.
Detect scam trends
DBS Bank applies AI and machine learning for risk management to detect emerging scam trends and complex transaction patterns.
Panchmatia mentioned that the bank’s legal and compliance team uses an AI-driven solution trained on over 300 features and 10 data sources.
This helps assess each transaction’s risk level and flag high-risk transactions within 25 milliseconds, said Panchmatia.
He added that this approach has resulted in a 17 percent increase in funds saved from scams, with the AI alert models proving five times more effective than traditional methods.
In its generative AI efforts, DBS Bank is focusing on five operational areas, including servicing, processing, quality assurance, training, and analysis.
In servicing, for instance, the CSO Assistant tool has reduced average call handling time by up to 20 percent, said Panchmatia.
The bank aims to enable generative AI to handle customer requests with minimal human intervention.
According to Panchmatia, DBS Bank prioritises ethical, responsible, and legally compliant data use to support AI-driven operations.
Guided by the Monetary Authority of Singapore’s FEAT principles (Fairness, Ethics, Accountability, and Transparency) and the bank’s PURE framework (Purposeful, Unsurprising, Respectful, and Explainable), DBS ensures data governance.
A Responsible AI task force oversees efforts, addresses potential biases, safeguards against AI limitations, and maintains human oversight to address ethical issues, said Panchmatia.
This structured approach allows the bank to work responsibly across markets while managing the challenges of transparency and compliance in financial services.