As banks across Southeast Asia increasingly explore artificial intelligence to improve services and efficiency, one critical factor often receives less attention: the operational systems that generate the data AI relies on.
According to Rakesh Nandakumar, AVP of Strategic Accounts for Southeast Asia at enterprise workflow platform Kissflow, the success of AI in banking depends heavily on whether institutions have already digitized their internal processes.
In an exclusive interview with FintechNewsPH, Nandakumar said many organizations are eager to adopt AI tools but underestimate the operational changes required to support them.
Rakesh Nandakumar, AVP of Strategic Accounts for Southeast Asia, Kissflow
“AI can crunch numbers and data better than any human possibly can,” he said. “But if everything is manual, then you don’t have the data to crunch.”
For financial institutions exploring AI-driven decision-making, he argued, digital workflows are the essential first step.
From customer apps to operational systems
Over the past decade, banks in the Philippines and across Southeast Asia have invested heavily in customer-facing digital services. Mobile banking apps, digital wallets, and QR-based payments have become widespread, particularly following the rapid expansion of digital payments during the pandemic.
However, internal operations within many institutions still rely on manual workflows involving email requests, spreadsheets, and paper-based documentation.
This creates a gap between the digital experiences customers expect and the operational systems banks use to process requests.
“Customers are ready to apply for something immediately,” Nandakumar said, citing the example of credit card applications. “But sometimes they are asked to send an email. That email has to be read, reassigned, tracked, and processed manually.”
Such processes can create delays and inefficiencies even when customer-facing services appear digital.
The shift toward self-service banking — where customers can initiate and complete transactions directly through digital channels — has raised expectations for speed and convenience. To support these services, banks must ensure their internal operations are equally digitized.
Why AI depends on structured operational data
Artificial intelligence has become a major focus across the financial sector, particularly in areas such as risk assessment, fraud detection, and operational analytics.
But Nandakumar emphasized that AI systems rely on structured operational data generated by digital processes.
If workflows remain fragmented across spreadsheets, paper forms, and email communications, extracting meaningful insights becomes far more difficult.
“If everything is Excel and paper, then how are you going to translate that into data which AI reads?” he said.
Digitizing workflows transforms operational activity into structured datasets that can be analyzed by AI systems. These datasets can reveal patterns in customer behavior, approval processes, and operational performance.
Without that digital foundation, institutions may struggle to unlock the full value of AI technologies.
Automation and regulatory visibility
Beyond operational efficiency, digital workflow systems can also improve regulatory compliance and internal governance.
Manual processes often make it difficult to track approvals, locate records, or verify decision histories during audits. Information may be scattered across physical documents, emails, and shared files.
Digitized workflows create a centralized record of each request and approval step, providing what Nandakumar described as a clear audit trail.
“Any request you raise will have what we call a status tracker,” he said. “It will tell you who approved, when they approved, and at which stage the approval took place.”
For compliance teams in highly regulated sectors such as banking, this type of visibility can reduce the time required to retrieve documents and verify processes.
Expanding the role of non-IT teams
Another shift accompanying workflow automation is the growing role of “citizen developers” — employees outside traditional IT departments who build process automation tools using low-code platforms.
Instead of relying entirely on central IT teams, organizations can enable business units to design solutions for their own operational challenges within controlled governance frameworks.
According to Nandakumar, this approach can significantly accelerate digital transformation across large organizations.
“Imagine every individual in my company is a problem solver,” he said. “Then and only then will you be able to achieve 100 percent digitization.”
In some cases, organizations adopting this model have trained hundreds of employees to build workflow solutions that support thousands of operational processes.
Digital transformation in the Philippine context
Nandakumar also pointed to cultural factors that could support the adoption of workflow automation in the Philippines.
Collaborative problem-solving and a willingness to experiment with new tools are characteristics he believes align well with citizen development models.
“Community problem-solving is deeply embedded in Philippine culture,” he said. “People would rather solve things as a unit rather than waiting for someone else to come and solve it.”
As financial institutions continue exploring AI and automation technologies, the Philippines is expected to remain an active market for digital transformation initiatives within the banking sector.
Building the operational foundations of AI-driven banking
For banks evaluating AI adoption strategies, Nandakumar said the key priority should be ensuring that operational systems are capable of generating usable digital data.
While customer-facing digital services may be the most visible part of banking transformation, the underlying workflows that process requests and approvals play an equally important role.
“If your operations are not modernized, it’s like having new wheels but no engine in the car,” he said.
In that sense, the path toward AI-driven banking may begin less with advanced algorithms and more with the foundational work of digitizing everyday operational processes.
