When the Assistant Starts Advising
Imagine this: your mobile banking app doesn’t just reply when you ask for your balance, it acts. It reviews your spending habits, rebalances your portfolio, reminds you of an upcoming bill, and suggests a product that improves your yield. All without a prompt.
That’s the next frontier in banking, the move from generative assistants that chat to autonomous advisory engines that think and act. In 2023–2024, most banks were racing to launch GPT-like copilots. In 2025, the smart ones are re-architecting for agentic AI, systems that do, not just talk.
The real question for every digital bank, wealth manager, and fintech founder: are you still building “chatbots”, or are you architecting AI agents that drive outcomes?
Understanding the AI Evolution Curve
The early era of Generative AI (GenAI) in financial services has delivered impressive conversational interfaces, assistants that could draft emails, summarize insights, or answer policy questions. But those systems remain reactive. The new paradigm is Agentic AI (autonomous AI agents). Unlike GenAI, which responds, agentic AI initiates actions, connects to systems, executes workflows, learns from results, and collaborates with humans in closed feedback loops.
In wealth management, this means a shift from “recommend me a fund” to “rebalance my holdings and execute under these parameters.” In retail banking, it’s from “show my spending trends” to “auto-categorize and optimize my saving behaviour.” This isn’t about replacing people; it’s about replacing repetitive decisions with orchestrated intelligence.
Why the Pivot Is Happening Now
Three tectonic forces are pushing this evolution from chat to autonomy:
- Rising Customer Expectation:
Today’s clients expect their bank to think for them, proactively, personally, and in real time. A Cerulli Associates survey found 77% of bank advisors plan to integrate AI into their practice by 2027, proving that autonomy is fast becoming a customer expectation, not a futuristic luxury. - Platform Fatigue with Fragmentation:
Legacy banking platforms can’t coordinate insights across silos. This limits GenAI’s usefulness. Vendors like Backbase and Temenos are embedding agentic orchestration directly into core banking platforms, unifying data and workflow triggers. - Tech and Governance Maturity:
With LLMs, retrieval-augmented generation (RAG), and strong model governance frameworks converging, banks can now safely deploy AI agents that act without breaching compliance. The hybrid AI service model, blending deterministic and generative AI with guardrails, is becoming a mainstream banking architecture.
Five Leaders Powering the Autonomous Advisory Era
Below are five vendors from the BFSI innovation landscape that exemplify the transition from “assistive” to “autonomous” AI. Each has verifiable proof points and market traction.
1. Backbase: From Digital Banking to Agentic Banking
Backbase is one of the most visible pioneers in this transition. In April 2025, it launched the AI-Powered Banking Platform, introducing “Agentic AI” as a native capability. Rather than layering chatbots on top of digital experiences, Backbase embeds autonomous agents inside the platform to handle onboarding, service, and sales. Its Intelligence Fabric unifies behavioral, transactional, and operational data into a real-time intelligence layer, allowing banks to generate context-aware recommendations that the system itself can act upon. The company’s AI Factory framework helps institutions move from pilot to production through modular governance, and its partnership with Banque Saudi Fransi (BSF) illustrates this in practice: BSF’s next-generation platform uses Backbase’s agents to power digital onboarding, payments, and card management. The result is fewer manual touchpoints, faster resolution times, and measurable improvements in conversion and satisfaction.
2. Finastra: Agentic AI in Corporate and Retail Banking
Finastra has taken a structured approach to agentic AI, positioning it as the next leap for corporate and retail banking. Its 2025 whitepaper “Agentic AI Assistants in Banking” described a future where autonomous assistants would complete workflows and orchestrate decisions across systems. This thinking has already materialized in Assist.AI, a contextual AI service used in trade finance to automate document clarification and routine staff queries. The solution reduces manual intervention and accelerates turnaround time for complex transactions. Beyond that, Finastra’s lending division is developing “AI Assist”, a natural language interface that connects human advisors and agentic systems to execute tasks collaboratively. Underpinning all this is the Analytics & AI platform, which consolidates customer and operational data to deliver precise recommendations. Finastra’s shift represents a pragmatic evolution, from intelligent support to intelligent execution, giving banks a foundation for true autonomous advisory.
3. FIS: Productivity Agents for the Banker’s Desk
FIS has reimagined productivity through its Agentic AI for Bankers ecosystem. This platform equips relationship managers and operations staff with voice-driven agents capable of transcribing meetings, summarising insights, populating CRM fields, and even suggesting next-best actions in real time. The aim isn’t to replace bankers but to eliminate repetitive tasks so they can focus on relationship depth. Its integration within FIS Digital One allows these agents to function seamlessly across channels, handling service inquiries and simple transactions without escalation. The firm’s pilots have reported improvements in frontline productivity. FIS’s philosophy is “human-in-control autonomy”: giving AI the authority to act where appropriate while maintaining transparency and supervision, a model that neatly bridges the gap between assistive and agentic intelligence.
4. Temenos: Agentic AI Inside the Core
Temenos has embedded AI autonomy within the core banking layer, a move that sets it apart. Its Financial Crime Mitigation (FCM) AI Agent autonomously screens transactions in real time, dramatically reducing false positives and compliance overhead. Meanwhile, its Product Manager Copilot uses learning algorithms to identify customer segments and generate new product configurations dynamically. Together, these demonstrate how core systems can evolve from static rule engines into adaptive advisory frameworks. The company’s 2025 launch of the AI-Powered Money Movement and Management Platform further illustrated this philosophy, an ecosystem where payment repair, fraud detection, and customer interaction occur with minimal human involvement. Temenos proves that the future of agentic AI isn’t just in digital channels; it’s also deep within the bank’s operational DNA.
5. ServiceNow: The Process Layer for Agentic Banking
ServiceNow’s contribution lies in making the process layer of banking intelligent. Its Agentic AI for Banking architecture allows AI agents to manage cross-departmental workflows, turning complex service chains into autonomous processes. A 2025 implementation for a major Indian private bank demonstrated this vividly: the institution achieved a 65% reduction in process time and a 45% uplift in customer satisfaction by using ServiceNow to automate lending and claims management. The platform’s ability to orchestrate AI agents across front, middle, and back office means banks can embed autonomy not just in customer service but in the very structure of their operations. ServiceNow has effectively become the connective tissue for financial institutions moving toward autonomous orchestration, ensuring every digital action has an accountable, trackable workflow behind it.
Regulatory & Trust Guardrails
With autonomy comes accountability. Regulators and compliance leaders are watching closely as banks give AI systems agency over decision-making.
Key governance pillars include:
- Explainability: Banks must trace every AI-driven recommendation. Vendors like Backbase and Temenos embed audit trails directly into their agentic layers.
- Human Oversight: Agentic systems must allow manual override, Finastra and FIS maintain “human-in-the-loop” principles.
- Data Integrity: Unified data architectures (like Backbase’s Intelligence Fabric) are essential to prevent bias or data drift.
- Fairness & Suitability: Especially in wealth advisory, regulators expect automated recommendations to meet fiduciary standards.
- Operational Resilience: Vendor frameworks now integrate AIOps to ensure continuous monitoring of AI agent performance.
In essence, trust is the license for autonomy, and the vendors winning today are the ones baking explainability and compliance into their architectures.
Final Take: The Era of Autonomy Is Here
The banking industry’s AI pivot marks a profound shift, not in tools, but in trust models. Generative assistants spoke; agentic advisors act. By 2028, one-third of GenAI interactions in banking to be executed through autonomous agents. Banks still chasing chatbot programs are already behind the curve.
The readiness questions for 2025 and beyond are clear:
- Which of your AI programs can execute actions, not just respond?
- How will you govern those actions under regulatory oversight?
- Do your data and workflow layers support agentic orchestration?
- And perhaps most crucially, does your organization trust AI enough to let it advise?
Because the future of digital banking isn’t conversational, it’s cognitive and autonomous.
The assistant era is ending. The advisory engine era has begun.
Will your bank be ready?