Artificial intelligence-powered banking platform Backbase has partnered with financial services data analytics network Plaid, the companies announced Monday, February 16, 2026. The collaboration aims to address data fragmentation within financial institutions, a challenge that hinders innovation and impacts customer experience.
According to a joint release, banks struggle with siloed data, frequent integration failures with legacy systems, and lengthy customer onboarding processes. These issues also make it difficult and expensive to create personalized financial experiences. The partnership seeks to resolve these problems by integrating Plaid’s financial data connectivity with Backbase’s platform, enabling faster customer onboarding, aggregated account data views, and personalized financial journeys.
“Artificial intelligence is rapidly changing what’s possible in financial services, but only with strong data foundations,” said Adam Yoxtheimer, head of partnerships at Plaid. “By combining Plaid’s real-time connectivity and intelligence with Backbase’s banking platform, more banks can access the permissioned data and corresponding insights needed to deliver enhanced, personalized experiences.”
The solution is currently available to banks globally through Backbase’s website. The move comes as the financial sector increasingly focuses on the application of AI, with much of the impact occurring behind the scenes, according to a recent report by PYMNTS.com. The report highlighted AI’s growing role in areas like compliance, cash management, and payment routing, where AI agents are now initiating tasks and moving funds based on real-time data.
This shift marks a critical test of trust in AI’s operational capabilities within financial institutions. Agentic AI, which can plan, reason, and execute multi-step workflows with minimal human intervention, is moving beyond pilot programs and becoming integral to core operations. PYMNTS Intelligence research indicates that 43% of chief financial officers anticipate agentic AI will significantly impact dynamic budget reallocation based on real-time cost signals, while another 47% predict a moderate impact. Finance leaders are increasingly relying on these AI agents to monitor spending, optimize cash flow, and identify anomalies without relying on traditional month-end closing processes.