Hyperpersonalization and Real-Time Decisioning: Driving Value in Finance
By shifting models from the back office to the point of interaction, institutions will unlock a new level of responsiveness, says Tealium’s Director of Solutions Consulting
By Eoin Connolly
The promise of agentic AI in financial services is no longer abstract. In a virtual session for Corinium, Drew Hoch, Director of Solutions Consulting at Tealium, described a future where financial institutions act in the moment to personalize experiences, prevent fraud and automate workflows across multiple channels.
“Agentic AI will transform customer experience with hyperpersonalised and hyperrelevant content,” Hoch says. “But you can’t do that without strong data foundations, human guidance and compliance at the core.”
Financial services customers expect tailored experiences. Whether they’re opening a new account or seeking investment advice, they want faster decisions and relevant offers. Agentic AI enables institutions to meet this expectation in real time by leveraging both structured and unstructured data (such as transactions, clickstream, voice transcripts, and location signals).
By analyzing behavior continuously to predict needs and delivering personalized recommendations at the precise moment of interaction, financial institutions using agentic AI can usher in a new paradigm of service for their consumers. Hyperpersonalization isn’t just about marketing offers. Today’s technology includes fraud detection, risk scoring, service routing and even proactive financial wellness advice as part of its umbrella suite of innovative features.
From chatbots to contextual advisors
Hoch described a new generation of AI-powered interfaces:
Smarter virtual assistants: Rather than static FAQ bots, these agents pull context from multiple systems – balances, recent activity, geo-location – to answer customer queries more precisely than ever.
Robo-advisors with contextual memory: Large Language Models (LLMs) combined with retrieval-augmented generation (RAG) can remember user preferences, simulate “next best actions,” and personalize financial advice dynamically.
Omnichannel service: Agentic AI doesn’t just live in one app. It coordinates across a huge range of channels. including SMS, chat, web portals, and even branch systems, so customers receive personalized responses no matter where they engage.
One of Hoch’s most emphatic points is that agentic AI shines when deployed at the edge of the customer experience. Rather than batch-processing data hours later, institutions can act within milliseconds of a customer event.
Take application abandonment. If a customer starts a credit card application but hesitates at the income field, an edge-based model can score their likelihood to complete and trigger an immediate incentive to encourage completion. In-the-moment fraud detection models can monitor behavioral biometrics, device fingerprints, and geolocation data to distinguish genuine customers from fraudulent actors without creating friction.
And by using streaming data, banks can even tailor cashback or loan offers while the customer is still on the website or mobile app. By shifting models from the back office to the point of interaction, institutions unlock a new level of responsiveness.
Sample use cases across the customer journey
Hoch highlights several concrete use cases for agentic AI across the user’s personal journey with financial services.
Onboarding and lending: Use propensity models and LLMs to pre-fill forms, detect missing documents, and provide instant feedback. Combine identity verification with behavioral analytics to minimize fraud risk without adding friction.
Account servicing and support: Deploy conversational agents that access account history and customer sentiment simultaneously, enabling them to solve issues faster and hand off complex cases to human agents intelligently.
Fraud detection and risk monitoring: Leverage continuous behavioral biometrics to flag anomalies in real time. Unify fraud detection with customer experience so legitimate users aren’t blocked unnecessarily.
Wealth and investment advice: Combine RAG architectures with historical portfolio performance to generate next-best actions tailored to the client’s risk profile and life stage.
Hyperpersonalised marketing: Offer dynamic rewards and cashback tied to a customer’s spending habits, with the ability to adjust offers as behavior changes.
Hoch cautions that hyperpersonalization must be grounded in compliance and ethics. Capturing user permissions across every data source, filtering sensitive data, auditing agent actions, all of these are crucial aspects of ensuring any agentic AI is deployed ethically, without institutional risk.
Explainability is another big element. Regulators and customers have to be able to see why an AI agent made a decision, while even autonomous workflows need clear escalation paths and manual review options. Without these guardrails, agentic AI can undermine trust. Which is, after all, the very asset financial institutions depend on.
Agentic AI is reshaping financial services, but its biggest visible impact will be in real-time customer interactions. Institutions that treat hyperpersonalisation as a system-level capability, not a bolt-on feature, will deliver experiences that feel as trusted as they do seamless.
“In a rapidly evolving AI landscape,” says Hoch, “the institutions that test and move fast without sacrificing trust will lead.”
