In 2026, AI will unlock new revenue streams for the financial services industry
By Rinesh Patel, Global Head of Financial Services at Snowflake
The financial services sector in India underwent a significant transformation this year, driven by the adoption of AI chatbots and various AI tools. Come 2026, the industry will shift from isolated AI tools to a version that will be more accountable, resilient, and responsible. According to a recent report by the Reserve Bank of India (RBI), it is estimated that GenAI will enhance banking operations in India by up to 46%.
With that backdrop, it becomes imperative for financial institutions to deploy solutions that offer tangible business impact and revenue generation, while also efficiently managing risk.
#Prediction 1 : Financial services will demand AI accountability as the experimentation phase ends
The days of “AI for AI’s sake” spending are over. In 2026, financial institutions will pivot from proof-of-concept projects to demanding measurable business impact from every AI dollar spent. Banks and asset managers will no longer accept experimental chatbots or isolated AI tools—they’ll require clear evidence that AI is driving specific outcomes: customer retention rates, revenue growth percentages, operational scale improvements, and enhanced digital adoption metrics. This shift will force organizations to abandon siloed AI experiments in favor of integrated solutions built into core data architectures, with boards demanding comprehensive measurement frameworks that treat AI investments with the same rigor as any other strategic technology deployment.
#2 Prediction: AI risk management will evolve beyond ethics to encompass operational survival
As AI agents and large language models become embedded in critical financial operations, the risk conversation will expand dramatically beyond bias and hallucinations. In 2026, financial institutions will grapple with AI risks that directly threaten operational resilience, from data residency challenges to systemic failures that could trigger regulatory sanctions. European regulators are already signaling stricter oversight, and boards will demand comprehensive risk frameworks that treat AI deployment as seriously as any other mission-critical system. Firms without robust data foundations will find themselves unable to deploy AI at scale.
#3 Prediction: Profitability will drive hyper-specialized AI applications across financial sectors
Unlike other industries pursuing AI for efficiency gains, financial services will double down on AI’s revenue-generating potential. In 2026, this profit-first mentality will spawn highly specialized AI applications tailored to specific financial sectors: banks will deploy AI to optimize lending margins and reduce credit losses, asset managers will use it to uncover alpha in alternative data, and fintech companies will leverage AI for real-time risk pricing. The question won’t be “Can we use AI?” but rather “How much money can this AI application generate?”
Financial institutions will deploy AI agents across their data ecosystems to streamline both how work gets done and how it’s controlled. In 2026, financial institutions will begin introducing AI agents into core business processes, from risk monitoring and surveillance to customer reviews and portfolio operations.
As these systems take on multi-step work traditionally handled by teams of analysts, leaders will confront a dual challenge: how to measure productivity in a world where humans and AI collaborate, and how to govern agents that can make decisions and take action. This will drive a major shift in operating models. Firms will move from measuring tasks handled by people to evaluating the performance of blended human–AI workflows based on speed of detection, accuracy of decisions, consistency with policy, and overall business impact.