There is a steady change in customer expectations from financial services providers as they expect frictionless and invisible banking services, which essentially is a step-up from ‘digital banking’ to ‘ambient banking’.
Today, most customers do not actively “go to the bank” for routine financial activities because financial services are embedded directly into everyday transactions. Consumers can pay for rides, food deliveries and e-commerce purchases without logging into a banking app, as payments are executed through wallets and integrated payment rails. Subscriptions, bill payments and marketplace purchases are processed automatically through stored credentials, requiring little or no manual action.
Automated finance has expanded beyond payments to regular income, savings, borrowing and insurance. For example, gig-economy platforms provide access to working capital based on earnings, e-commerce apps provide BNPL options, travel bookings are bundled with insurance, etc. We are increasingly observing in our work with banks and fintech platforms that financial services are becoming embedded directly within customer ecosystems, reducing reliance on traditional banking interfaces.
Go deeper with GlobalData
Access deeper industry intelligence
Experience unmatched clarity with a single platform that combines unique data, AI, and human expertise.
Many of these capabilities were enabled without AI agents, relying instead on rule-based decision systems. However, across our recent transformation programs, institutions are now moving beyond static automation toward adaptive systems, orchestration layers and APIs that continuously learn to enable better decision-making.
AI is elevating customer experience to the next level
According to the WEF’s 2025 AI in Financial Services report, projected AI investments across banking, insurance, capital markets and payments are expected to reach $97bn by 2027. Nearly 70% of financial services executives that we talk to expect AI to directly contribute to revenue in the coming years. Interest in Agentic AI is increasing as banks look to automate complex workflows that previously required manual coordination.
AI agents can extend embedded finance by shifting experiences from customer-initiated interactions to systems that operate continuously in the background. One clear example is onboarding, which now involves automated data collection, validation, KYC checks and risk assessments, with human involvement focused on exception handling and higher-risk scenarios. In transformation programs we have supported, this has helped banks reduce onboarding friction.
Financial institutions are already using AI to generate personalised investment and financial planning insights in real time, helping customers make informed decisions without needing to actively seek advice. We are now seeing early deployments extend into proactive financial optimisation and risk identification, enabling institutions to intervene earlier and deliver more relevant outcomes.
Agentic AI systems operate across core banking systems, CRM platforms and servicing workflows, ensuring continuity across digital and human interactions. Conversations that begin digitally can continue seamlessly with relationship managers because context and decisions are already available. In one engagement with a global fintech, Agentic AI enabled intelligent case routing, reducing SLA breaches and operational backlogs by prioritising high-impact exceptions.
This shift is also improving operational responsiveness. In another engagement with a fintech platform serving banks in North America, modernisation efforts enabled faster pricing adjustments, reducing release timelines from weeks to much shorter cycles. This demonstrates how stronger platforms and automation allow institutions to respond faster without increasing operational complexity.
Technology enablers for Invisible AI
Invisible AI depends on strong foundational capabilities, including unified data environments, modern architectures and operating models designed to support continuous decision-making.
APIs serve as a critical layer by enabling banks to securely connect internal systems and external ecosystems. They allow financial services such as payments, credit evaluation, and fraud detection to operate within customer journeys, rather than as separate processes.
A unified data foundation is equally important. Real-time access to enterprise data enables more accurate decision-making, personalised experiences, and reliable automation. Many banks are still operating with fragmented systems, which limit their ability to scale AI beyond individual use cases. This fragmentation remains one of the most common barriers we encounter when supporting banks in scaling AI adoption across enterprise operations.
Event‑driven architectures further strengthen this foundation by allowing AI systems to react instantly to changes and trigger actions in real time without waiting for manual requests. To sustain trust, Invisible AI must embed governance, explainability and human-in-the-loop controls. This ensures models remain compliant, auditable and resilient as they evolve.
Where human judgment must remain central
As Agentic AI systems begin to act autonomously, it becomes critical to define where humans stay in control. Routine approvals can be automated, but material decisions cannot. Loan denials, large commercial credit decisions and sensitive customer situations demand empathy, context and fairness. AI can accelerate assessments, but human decision-makers must retain authority over outcomes that materially affect lives and livelihoods.
Human judgment is equally essential when supporting financially vulnerable customers and resolving disputes. Customers need clear explanations and paths for recourse when AI-driven decisions are challenged.
At the organisational level, human oversight is essential for setting risk policies, managing exceptions and ensuring responsible deployment. The most effective institutions treat AI as a capability that strengthens human decision-making rather than replacing it.
What separates AI leaders from followers
Leading institutions are moving beyond pilots and deploying AI across end-to-end workflows, including customer service, compliance, operations and software development. From what I am observing across our client engagements, institutions that are successfully scaling AI are those embedding intelligence directly into operational workflows rather than treating it as isolated innovation efforts.
As fraud becomes more sophisticated, banks are strengthening detection through behavioral analysis, continuous verification and unified monitoring across channels. These capabilities allow fraud prevention to operate continuously without disrupting customer experiences.
Responsible AI practices, including governance, risk management and data protection, are becoming core operational priorities rather than standalone compliance activities.
Institutions that remain constrained by fragmented systems and isolated pilots will find it difficult to scale these capabilities. Competitive advantage will increasingly depend on how effectively intelligence is embedded into core operations and decision-making.
The shift underway is not about introducing more visible technology, but about making financial services more responsive, reliable and adaptive. Banks that invest in strong data foundations, modern architectures and disciplined governance will be better positioned to scale these capabilities.
Barath Narayanan, Global BFSI and Europe Geo Head at Persistent Systems
