How AI could restore trust and make NZ banking more human

AI in NZ Banking: What Customer Support Teams Need to Know

Customer trust in banks has slipped, but there's a clear path to earn it back. Andy Symons, financial services lead at Accenture in New Zealand, says AI can take care of routine transactions so people can focus on complex, personal needs-bringing more humanity back into banking.

Short term, AI will put better data and context at the fingertips of frontline teams. Long term, customers will interact directly with AI for planning, payments, and money coaching-tools that feel intuitive and proactive.

What the big banks are doing

Westpac says AI can improve the lending experience by speeding up access to quality data and freeing bankers for deeper conversations. It's already helping third-party and adviser teams assess applications faster.

ANZ is building bank-wide familiarity with AI and taking a careful approach to ensure safe, responsible use-supporting its focus on improving Kiwis' financial wellbeing.

Kiwibank's chief executive Steve Jurkovich says the bank is "people first" and using AI to reduce toil-those repetitive tasks that drain time but add little value. By partnering on leading tools, the goal is more personal, efficient, and secure banking, with simpler experiences for customers.

What this means for Customer Support

• Let AI handle the repetitive work: identity checks, document capture, application pre-screens, after-call notes, and case tagging.
• Use AI assistants to surface customer context, preferences, and risk flags in real time so agents can focus on empathy and precision.
• Build proactive support: alerts for payment issues, budgeting nudges, and timely outreach for upcoming life events.
• Keep humans on the complex cases-disputes, hardship, and sensitive conversations-and equip them with stronger data and scripts.
• Tighten fraud and security workflows with AI-driven anomaly detection and faster triage.

Guardrails that matter

A recent review by New Zealand's Financial Markets Authority notes a cautious approach across finance. Most firms are adopting or planning to adopt generative AI, with goals of better customer outcomes, improved efficiency, and stronger fraud detection.

• Create a clear AI-use policy: what data can and can't be used, and who approves new tools.
• Keep a human in the loop for high-stakes decisions and complaints.
• Audit prompts and outputs for bias, accuracy, and privacy risks.
• Track impact with hard metrics: resolution rate, wait times, NPS/CSAT, fraud catches, and complaint volumes.

See guidance and updates from New Zealand's Financial Markets Authority.

Quick wins this quarter

• Deploy AI summarization for calls and chats, and add auto-tagging to cut admin time.
• Use AI to pre-check lending documents and flag incomplete applications.
• Launch a support-side knowledge assistant trained on your policies and product docs.
• Pilot a money-coaching chatbot for budgeting tips and payment reminders, with easy handoff to agents.
• Stand up a fraud alert playbook with AI-based pattern detection and fast escalation paths.

Upskill your team

Give support teams hands-on training with AI tools and clear guidelines. Focus on prompt quality, safe data use, and working effectively with AI copilots.

Explore AI courses by job role at Complete AI Training to build practical skills for frontline teams.