GenAI is the key to CX improvement in retail banking

How GenAI levels up CX improvement

GenAI marks a fundamental shift in how retail banks can listen to and act on customer feedback. Instead of relying on static surveys or rigid keyword systems, banks can now tap into AI models that understand the nuance, emotion, and complexity hidden in everyday customer interactions.

By interpreting language more like a human, and doing so at scale, GenAI unlocks patterns and insights that would otherwise go unnoticed. It transforms feedback from a scattered collection of grievances into a living, actionable intelligence system that can power better service, faster problem resolution, and smarter business decisions across the organization.

Summarize feedback across channels at scale

GenAI models can ingest feedback from multiple sources—branch surveys, call transcripts, mobile app reviews, and social media posts—and summarize it into cohesive, human-readable themes. Unlike traditional systems that rely on keyword tagging, GenAI models can cluster semantically related feedback and reveal issues that aren’t tied to pre-programmed categories.

For example, if different branches report “ATM glitch,” “machine froze,” and “deposit stuck,” GenAI can recognize these as a unified theme signaling an emerging system-wide issue. Banks like DBS are already seeing value from this approach, with GenAI tools summarizing live customer conversations to surface actionable service trends faster.

Detect early signs of dissatisfaction

GenAI can identify subtle language patterns indicating frustration, confusion, or dissatisfaction, even when customers don’t directly lodge complaints. By analyzing tone, phrasing, and context across thousands of interactions, AI can flag accounts at risk, long before traditional metrics like churn rates pick it up.

For instance, NatWest Group is piloting GenAI models that analyze service transcripts to spot early dissatisfaction. Service managers can then intervene proactively, whether that’s by offering additional support, clarifying confusing terms, or simplifying application processes before frustrations escalate.

Recommend proactive measures

GenAI can go beyond flagging issues by recommending specific next actions. By analyzing historical resolution data and contextual customer profiles, AI can suggest the best-fit responses: escalating to a senior advisor, offering goodwill credits, or triggering an app update fix notification.

Internal pilots at Goldman Sachs demonstrate how AI-generated insights from client interactions help relationship teams craft faster, more customized recovery actions. In retail banking, the same logic can apply at the branch, contact center, or digital engagement level, improving not only customer retention but also employee efficiency.

Enable dynamic, role-based insights

GenAI can deliver tailored insights for different teams (e.g., branch managers, product owners, compliance officers), allowing them to act faster without being overwhelmed by irrelevant noise. Instead of relying on static dashboards, teams can ask natural language questions and receive synthesized answers specific to their responsibilities.

A regional manager might query, “What top three customer issues emerged across my branches this week?” and get an immediate breakdown tied to specific actions. This level of role-specific feedback insight shifts the bank’s ability to respond from monthly reporting to real-time operating.

Best practices to implement GenAI for feedback management

Managing GenAI for customer feedback isn’t like installing a new software tool. GenAI models don’t arrive fully ready to operate in a banking environment. They must be trained, adapted, guided, and continuously refined. Without careful attention to fine-tuning, context-specific prompting, real-world feedback loops, and dynamic governance, even the best models can drift, misinterpret, or underperform.

For banks, success depends on treating GenAI as a living system that must evolve alongside customer expectations, regulatory shifts, and organizational priorities.

Fine-tune GenAI on banking-specific data

Generic large language models won’t perform reliably in banking without fine-tuning. Banks must fine-tune models on domain-specific datasets—customer service logs, complaints archives, feedback from different banking products—to recognize the nuances of financial language, regulations, and brand tone. Without this, models risk misunderstanding intent or generating irrelevant summaries.

Engineer contextual and dynamic prompts

Prompt engineering must become an active, strategic capability. Different types of customer feedback require different prompting strategies. A mobile app review prompt needs a different structure than a mortgage complaint prompt. Developing a robust prompt library, tested for each customer journey stage, will significantly improve the accuracy and value of AI outputs.

Build dynamic human-in-the-loop systems

Rather than relying on basic after-the-fact human reviews, banks should design dynamic human-in-the-loop systems where subject matter experts (SMEs) actively guide model behavior during deployment. SMEs can validate emerging issue clusters, approve intervention strategies, and feed real-world correction signals back into the model continuously, not just during annual audits.

Operationalize smart retraining triggers

Feedback management models should not be retrained only on a calendar basis. Banks should set dynamic retraining triggers based on indicators such as detected data drift, shifts in complaint language, major product launches, or regulatory changes. Automating these signals helps models stay fresh and relevant without slipping out of sync with customer realities.

Mature governance beyond compliance

Banks should go beyond baseline regulatory compliance and develop feedback-specific AI governance frameworks. These should cover areas like bias detection in issue surfacing (e.g., does the model under-detect complaints from certain demographics?), explainability standards (e.g., can teams understand why an issue cluster emerged?), and traceability (e.g., can banks audit how a complaint was interpreted and acted on?).

A frontier AI data foundry platform can help

Banks looking to operationalize GenAI for customer feedback management need more than access to large language models. You need a strong foundation of data and infrastructure.

A frontier AI data foundry platform provides that foundation by:

Fine-tuning models on domain-specific banking data to maximize relevance and accuracy.
Building unified, annotated datasets that allow AI to see the full customer experience across all channels.
Embedding human-in-the-loop workflows that allow frontline teams to guide, refine, and supervise AI outputs.
Implementing governance and monitoring tools to ensure responsible use of AI and continuous improvement.

With the right data foundry in place, retail banks can move from reactive feedback management to predictive, proactive customer experience leadership—creating more loyal customers, stronger brands, and smarter organizations.

Learn more about the Centific frontier AI data foundry platform.