How AI is Turning Trusted Data Into Treasury Advantage

As artificial intelligence gains momentum across financial services, banks are increasingly looking beyond automation to determine how AI can improve balance sheet management, strengthen decision-making, and respond more effectively to changing market conditions.

While the technology promises significant gains in speed and analytical capability, its long-term value will depend on the quality of the underlying data and the governance frameworks supporting it.

Jyothi BS, Co-Founder and Group Managing Director of Surya Soft, believes the biggest obstacle facing banks is not a lack of information but the challenge of making sense of increasingly fragmented data.

Drawing on the company's experience working with banks across more than 20 countries, she said institutions often struggle because data resides across multiple treasury, finance, risk, regulatory, and core banking systems using different formats, definitions, and reporting timelines.

“The most consistent barrier is not a shortage of data, but its complexity,” Jyothi says. “The primary reason to build our Data Harmoniser platform is to address these challenges by rapidly processing and reconciling data from disparate sources into a single, trusted view of the balance sheet.”

Creating that trusted view delivers benefits beyond operational efficiency. By eliminating manual aggregation and ensuring data is timely, consistent, and reliable, banks establish the foundation necessary for analytics and AI to generate faster and more informed decisions.

While AI continues to attract considerable attention, Jyothi argues that its greatest value lies in supporting experienced professionals rather than attempting to replace them.

She points to pattern recognition across large, structured balance sheet datasets as one of the technology's strongest use cases, enabling institutions to identify rate sensitivity concentrations, liquidity mismatches, and emerging risks far more quickly than traditional manual analysis.

“What we see working well in practice is AI applied to pattern recognition across large, structured balance sheet datasets,” she adds. “The institutions extracting the most value are those treating AI as an accelerant to expertise, not a replacement for it.”

Beyond identifying risks, AI is also expanding into decision support. Jyothi highlighted applications including natural language interaction with balance sheet data, allowing treasury and asset and liability management teams to ask questions directly without relying on technical specialists.

She also cites optimization capabilities, such as helping institutions evaluate high-quality liquid asset strategies while balancing profitability, liquidity, and regulatory constraints.