By Guru Sahajpal, AVP of Banking and Financial Services at Cognizant
As artificial intelligence (AI) reshapes the financial services landscape, banks and fintechs are facing a critical inflection point in how they deploy this transformative technology. After years of pilots and experimentation, the fintech industry is poised to move from testing to transformation – but not all AI investments will deliver the same ROI.
So, which strategies will actually drive revenue and provide a competitive advantage? I’ve identified six pivotal trends that will define the fintech industry’s AI evolution in 2026. From agentic commerce to enterprise-wide governance, these predictions reveal that successful uses of AI in fintech depend on more than just technology adoption, but also strategic prioritization, ethical guardrails, and unwavering customer trust.
- Prioritize new revenue-generating activities to accelerate ROI
While banks and fintechs should continue leveraging AI for problem-solving and loss-prevention, they also need to prioritize their 2026 AI investments around new revenue-generating activities. Agentic commerce is primed for growth as AI-driven retail traffic expands into transaction execution, including payments. AI should automate significant portions of complex, time-consuming activities (e.g. credit scoring and underwriting, new customer onboarding) to help accelerate revenue realization.
I expect the most significant impact to come from banks and fintechs’ AI investments in enabling hyper-personalized financial advice such as tailored offers based on customers’ financial data and life events. These offers will result in significantly improved acceptance rates, deliver customer satisfaction, and generate new revenue streams.
- Enterprise-wide AI adoption without appropriate guardrails in place is a recipe for disaster
All enterprise-wide AI rollouts must happen within well-defined boundary constraints that follow 3 simple rules: (1) first, do no harm, (2) if harm is done (owing to unforeseen/unplanned circumstances), establish mechanisms to identify and mitigate/reverse it timely, and (3) ensure against (or minimize) algorithmic biases by establishing a perpetual learning mechanism that trains the AI’s LLMs on diverse datasets to deliver continual improvement. Enterprise-wide AI adoptions that follow these rules or others similar in intent will ensure the AI solutions being rolled out are both ethical and responsible.
- Enterprise AI adoption starts with the tone at the top
Organizations need to see and hear their leaders’ commitment to AI, starting with the CEO. This commitment needs to be communicated across the organization via simple, transparent messaging about what they’re doing, why, and what it means for each person. AI isn’t just the Chief Data Officer’s or the Chief AI Officer’s responsibility, it is everyone’s. Organizations must adopt a culture of communication, inclusion, and innovation, and ensure concerns about employment and career impacts are adequately addressed to gain the requisite buy-in from associates across all levels.
- Omission is a much bigger sin than commission
Too many organizations have invested in successful pilots and proofs of concept that never saw expanded rollouts. Organizations need to break out of this analysis paralysis mode to fund adoptions at scale. The technology is not the problem – fragmented processes, data quality limitations, organizational silos, and people challenges are what constrain organizations’ ability to meet their AI goals and should be solved thoughtfully to realize the full potential of their AI deployments.
- Trust remains the underlying X factor in evolving AI in payments and fintech
Payments service providers (PSPs) and fintechs are racing to deliver agentic commerce – autonomous AI agents acting as customers’ personal assistants to find, compare, buy and pay for financial products and services without the need for human involvement/intervention. While this holds great promise for both providers and consumers, the winners will be limited to those PSPs and fintechs that can provide the evidentiary conviction necessary to convince their customers that their agentic commerce solutions are safe and secure.
- Two moonshot use cases that could transform the industry in 2026
The 2008 financial crisis and resulting bank failures and bailouts triggered a tsunami of regulations that financial institutions have struggled to keep up with ever since. All compliance activities such as recurring regulatory filings (e.g. resolution and recovery plan filings, CCAR/DFAST reporting, AML/KYC and OFAC compliance, liquidity reporting – LCR, NSFR, etc.) and all related compliance activities requiring traceability and demonstration of burden of proof will be prime targets for an autonomous, omnipresent Financial Crime Watchdog/Cop that serves as the first line of defense for preventing all types of fraud and financial crime against financial institutions.
Mergers and acquisitions (M&As) remain one of the highest value-unlock/creation activities in financial services, yet require extremely manual-intensive, time-pressured, and judgment-heavy efforts in support of data ingestion, due diligence, synergy and valuation modeling, deal structuring, pricing, negotiation, and post-merger execution planning. A moonshot AI use case for M&A would be an autonomous deal intelligence engine that powers the end-to-end M&A process and drives it forward, delivering faster deal velocity by minimizing downside and providing better post-merger outcomes that deliver synergies that match forecasts.
In 2026, financial institutions that prioritize making profitable AI investments, establishing responsible governance frameworks, and building trust at every touchpoint will lead the market. By moving beyond pilot projects to scaled adoption and leading with an innovative culture, financial institutions can secure a unique competitive advantage in today’s rapidly evolving AI era. The fintech leaders who “win” in 2026 will not be those with the most advanced AI deployments, but the ones who deploy it with strategic clarity, intention, and responsibility.
