Improve customer support in retail banking with AI-driven chat

Benefits of AI-driven chatbots in enhancing customer support

AI-powered chatbots could potentially reshape the customer experience by addressing some of the most stubborn problems with traditional service models. One of the biggest shifts is around availability. Unlike service centers tied to business hours, or rule-based chatbots that often hit dead ends, AI-driven chat offers nonstop support. Customers can get intelligent help whenever they need it, day or night, without being told their question falls outside the script.

AI chat can also respond faster, and in banking, delays can create real anxiety, whether someone is checking on a transaction, reporting suspected fraud, or seeking help with an account issue. Traditional chatbots often stumble on anything outside routine tasks, forcing customers to call in or wait for a human. AI-driven chat, by contrast, is designed to handle complex, nuanced inquiries in real time, resolving issues before frustration takes hold.

The experience goes deeper than speed alone. AI-powered chatbots personalize conversations based on customer history and real-time data, offering responses that feel more natural and helpful. Customers aren’t met with generic scripts or irrelevant options; instead, they receive answers that reflect their relationship with the bank. It feels more like talking to a personal banker who knows them, rather than navigating a rigid decision tree.

At the same time, automating these everyday interactions frees human agents to focus where they add the most value, such as handling complex problems, offering tailored advice, and building deeper customer relationships. Rather than replacing human support, AI allows banks to use it more strategically, making service both faster and more meaningful.

AI chatbots are transforming customer service in the real world

Banks investing in AI-driven chat are already seeing measurable gains, too.

Verizon uses AI to improve customer service

At Verizon, AI is helping customer service agents become better salespeople. Using an AI assistant built on Google’s Gemini large language model, Verizon’s customer service representatives receive real-time support during calls to quickly find the right answers to customer questions. The system, launched at full scale in January 2025 after months of deployment, has cut call times and freed agents to focus more on selling. Since the AI assistant rolled out, sales through Verizon’s 28,000-person service team have jumped nearly 40%. Rather than replacing agents, Verizon is using AI to reskill them in real time, turning traditional customer service roles into sales-driven positions.

Commonwealth Bank of Australia improves customer service speed

At Commonwealth Bank of Australia (CBA), AI is playing a central role in improving both customer safety and service speed. CBA has integrated GenAI into its customer-facing messaging services, making it one of the few banks globally to do so. The bank now processes more than 50,000 customer inquiries a day through messaging, and AI has helped improve the speed, quality, and precision of responses.

CBA’s use of AI also extends to fraud prevention: GenAI flags thousands of suspicious transactions daily and send tens of thousands of proactive alerts to customers through the app. As a result, customer-reported fraud has dropped by 30%, and scam losses have been cut in half.

NatWest Bank embeds AI deeper into its customer service model

NatWest Bank is using its partnership with OpenAI to push AI deeper into its customer service model. Through the collaboration, NatWest is enhancing its digital assistants. One major goal is to streamline the way customers report suspected fraud, shifting more activity to digital channels and reducing the load on call centers. Today, most customers prefer to report fraud by phone, which a time-consuming process NatWest aims to change by making its chatbot more effective.

The bank has already seen a 150% improvement in customer satisfaction tied to the chatbot’s new GenAI capabilities and a decrease in the need for human agents to complete service requests. With around 80% of its retail customers banking entirely online, NatWest views AI-powered chat as critical to delivering the kind of digital experience modern customers expect.

Address challenges and considerations

AI is not a plug-and-play technology. It requires careful training, integration with your operations, and monitoring. Without active management, AI can quickly drift off course—creating inconsistent experiences, exposing customer data to risk, and damaging the very trust banks are trying to rebuild. The following tips will help you succeed with AI.

Train AI with high-quality, domain-specific data

A chatbot’s ability to deliver precise, regulatory-compliant answers depends entirely on the quality of its training data. Broad language models trained on general consumer data introduce unacceptable risks in a banking context.

Fine-tuning with domain-specific scenarios, such as account servicing, fraud disputes, and loan inquiries, anchors AI in the operational realities of financial services. Continual retraining with updated workflows and compliance requirements keeps the system aligned with evolving business needs.

Secure AI architectures to match regulatory and threat environments

Embedding AI into customer support extends the bank’s security perimeter. Every interaction becomes a potential entry point for data leakage or manipulation. Deployments must embed multi-layered controls across access management, encryption, anomaly detection, and data handling policies. AI pipelines need to be designed for auditability from the outset, with mechanisms that can demonstrate compliance under scrutiny and respond quickly to threat intelligence.

Architect for dynamic escalation between AI and human agents

The failure point for many AI systems is not the initial customer response; it’s what happens when the conversation exceeds the model’s capacity.

Systems must dynamically assess confidence thresholds in real time and trigger escalations without customer friction. Architecting this bridge requires careful orchestration between AI interfaces, CRM platforms, and agent workflows so that context, intent, and history are transferred seamlessly.

Minimize bias through controlled data curation and live model audits

Bias in customer interactions undermines trust and can expose banks to regulatory penalties. Curating balanced datasets during training is critical, but it’s not sufficient. Live model monitoring must test outputs across demographic and behavioral segments to surface disparities.

Remediation processes, ranging from retraining on counterfactual data to recalibrating confidence scores, should be operationalized as part of standard model maintenance.

Integrate AI tightly with core banking systems and CRM platforms

Surface-level integrations produce brittle customer experiences. AI must have real-time access to transactional data, service histories, and contextual metadata to personalize responses accurately. Deep integration with core systems reduces the risk of information gaps that force customers to repeat themselves or escalate unnecessarily.

API orchestration layers should manage these connections securely, with rigorous version control and resiliency planning.

Implement continuous tuning based on interaction data

Static models degrade quickly in production environments. Continuous tuning, such as using supervised and reinforcement learning from real interaction logs, extends the useful life of AI and keeps it aligned with customer expectations. Deploying human-in-the-loop review processes on a percentage of interactions can help refine intent recognition, surface emerging intents, and maintain conversational quality.

Operationalize transparency and customer disclosures

Customers increasingly expect clarity around when they are interacting with AI versus human agents. Disclosure protocols should be built into the experience design, not left to ad hoc scripting. Designing transparent AI handoffs and limitations into customer journeys helps prevent confusion, manage expectations, and reinforce trust without sacrificing automation efficiency.

Govern AI with active monitoring and preemptive risk management

Production AI environments require real-time monitoring. Banks should deploy observability frameworks that track accuracy, latency, escalation rates, and anomalous behavior across interactions. Trigger thresholds should initiate automated alerts and human review processes to correct drift before customer harm occurs.

Governance should extend to third-party LLM components and external APIs, not just internally built models.

A frontier AI data foundry platform maximizes the value of AI

Banks that want AI-powered customer support to move from pilot to production face a fundamental challenge: the technology is only as strong as the data, training, and governance behind it. Precision, personalization, and security aren’t features you can add later. They have to be built in from the start.

That’s where the Centific frontier AI data foundry platform comes in. Developing an AI-powered chatbot that truly understands banking customers takes more than fine-tuning a general-purpose model. It requires access to high-quality, contextualized, and continuously refreshed data that reflects the complexities of real-world banking scenarios.

Centific’s frontier AI data foundry platform helps banks build stronger AI systems by transforming fragmented, inconsistent datasets into structured, operationally ready resources. It supports the entire model lifecycle, from initial training to continuous reinforcement learning, so that the AI-driven chat solutions stays accurate, adaptive, and aligned with business needs.

Learn more about Centific’s frontier AI data foundry platform.