AI Moves Banks From Solving Problems to Building Experiences

By combining permissioned customer data with artificial intelligence (AI), financial institutions can tailor moments of interaction today and anticipate the needs and products clients will demand tomorrow, in a shift toward “cognitive banking” that builds on trusted relationships.

What Cognitive Banking Is

Cognitive banking refers to embedding AI-driven inferencing and pattern recognition on top of permissioned data (transactions, financial behaviors, linked accounts) so that banks can shift from reactive servicing to proactive guidance.

Rather than waiting for customers to navigate menus or submit queries, cognitive banking systems sense intent, flag opportunities, and offer “next-best actions” — be that a liquidity suggestion, a personalized loan offer, or a fraud alert.

PYMNTS Intelligence traced how AI in banking is entering its “next era,” where conversational interfaces evolve from simple Q&A bots to tools capable of strategic insight and contextual counsel.

Importantly, PYMNTS reports that nearly 3 in 4 bank customers want greater personalization and that embedded conversational AI could win back 72% of bank customers by providing that tailored experience. Thus, cognitive banking is not just about automation — it’s about personal relevance, timing and trust.

How Institutions Are Executing It

1. Conversational Interfaces That Go Beyond FAQ

The shift is underway: The new BoA AskGPS tool, announced this week, enables employees in the Global Payments Solutions unit to pose simple to complex client questions and receive authoritative answers in seconds. This breaks from traditional knowledge bases — it’s not just “search” but inference, context and response.

2. Personalization via AI-Driven Channels

When AI systems understand the trajectory of a customer’s finances, they can surface timely offers: a more competitive rate, a personalized saving plan, or early warning on liquidity pressure. In that sense, cognitive banking augments traditional product pipelines — credit, deposits, payments — with a layer that “knows what’s next.”

3. Trust, Risk and Governance as Core Layers

PYMNTS Intelligence findings argue that banks need layered intelligence: Combining traditional data, real-time anomaly signals, and human oversight to maintain trust and guard against runaway decisions.  In essence, cognitive banking must be governed. AI models should not dominate decision-making but serve as augmenting engines with oversight, explainability, and privacy guardrails.

Why Cognitive Banking Is Becoming Table Stakes

Customer expectations are rising. We found that 72% of customers would stay or return if they received personalization via embedded conversational AI.

Efficiency gains are nontrivial. Conversational systems embedded in workflows reduce friction and speed response, freeing staff bandwidth for more strategic tasks.

Scale of AI investment signals urgency. PYMNTS recently reported that AI captured 42% of U.S. venture capital in 2024, up from 36% in 2023, highlighting how rapidly capital and focus are tilting toward intelligent systems.

So banks that delay risk falling behind in relevance, operational resilience, and competitiveness.

Risks, Barriers and Pitfalls

Cognitive banking offers promise, but several obstacles loom:

Bias, fairness and opacity: Models must be audited so they do not encode or amplify unfair patterns.

Data privacy and consent fatigue: Permission must be explicit and revocable, with transparent disclosures.

Explainability and regulatory scrutiny: Clients and regulators will demand clarity on how AI arrives at decisions.

Talent, culture and change management: Retooling operations to think in “AI-first” ways is nontrivial.

Decision sovereignty boundaries: Giving AI too much power risks misalignment and loss of institutional control.

Cognitive banking cannot be grafted on lightly. It demands an integrated rethink of tech, risk, governance and strategy.

Cognitive banking is not a distant aspiration. It is actively taking shape.

Yet the differentiator won’t be who has a model. It will be who builds trusted, permissioned personalization at scale. Banks that align model governance with customer transparency, embed oversight, and infuse AI intelligence into daily flows will lock in loyalty, cut churn, and open new revenue vectors.