Subhankar Panda urges banks to integrate AI into financial planning at ICSCCT 2026
At the International Conference on Sustainable Computing and Communications Technologies (ICSCCT 2026), hosted by the Faculty of ICT at the University of Malta and published in partnership with Springer, Subhankar Panda took the stage as keynote speaker to make an issue that few in the audience have disputed but that many are still slow to act on: the next decade of banking will be defined by the extent to which organizations embrace AI and machine learning into their core planning, not just their back-office tools.Subhankar’s talk moved beyond the familiar narrative of AI as a cost-cutting layer installed on legacy systems. Instead, he frames AI and machine learning as tools that need to be within a bank’s financial planning function itself — shaping how risk is priced, how liquidity is forecast, and how decisions that used to take weeks by committees are now made in record-breaking minutes.“Banks that treat AI as an add-on will continue to improve yesterday’s operations.
“And the banks you engage with as a planning partner will start asking better questions about tomorrow’s balance sheet.”From automation to anticipationA recurring thread in the title was the shift from AI that automates known tasks to AI that anticipates unknown tasks. Subhankar pointed to machine learning models that are now able to stress test against scenarios that human analysts would rarely consider building on their own, and to show early signals in transaction data long before they show up in a quarterly report.
This shift, he said, changes the job of a financial planner as much as it changes an engineer’s job: spending less time compiling numbers, and more time defining what the numbers mean.“The value is not in the model that provides an answer. The value is in the banker knowing what question is worth asking.”Trust, governance, and model limitsSubhankar was careful not to present the adoption of AI in banking as a purely technical problem.
Part of the keynote focused on governance – the need for clarity in credit and risk decisions, the regulatory weight that financial institutions bear, and the reputational cost of deploying models that cannot take into account their own reasons. In a sector where a single miscalibrated model can impact both customer trust and compliance exposure, he suggested that responsible deployment is as important as capability.This focus ties into the broader argument running in Panda’s recent work: that reliability engineering and AI adoption are not separate conversations. His writing on AI-driven test automation in enterprise delivery has raised a relevant point in the world of software – that as systems grow more autonomous, the system for verifying them must grow just as quickly, or the speed at which AI promises turns into a risk rather than an advantage.
When applied to banking, the same logic applies: an AI-based planning system is only as trustworthy as the testing and governance built around it.A call for institutional patiencePanda concluded his speech by warning against treating AI transformation in banking services as a single project with an end date. He described it instead as a permanent capability that must be funded, staffed, and continually revisited—akin to how organizations approach risk management than how they approach software rollouts.“The banks that will get this right in five years are the ones that treat it as infrastructure now, not the ones waiting to buy the end product.”ICSCCT 2026 attracted researchers and practitioners from the fields of computing, sustainability and applied technology over two days of sessions at the University of Malta, with proceedings published through Springer.
