Scaling Banking Customer Service with an AI-Powered Copilot

Executive summary

A major commercial bank in Singapore needed to scale customer service as digital interaction volumes increased without expanding operational capacity at the same rate. Fragmented service knowledge, repetitive inquiries, and manual routing were consuming agent capacity and slowing resolution.

Kyanon Digital introduced an AI-enabled service layer combining enterprise knowledge retrieval, agent assistance, workflow automation and system integration. The solution helped service teams access trusted information faster, automate routine work and escalate complex cases with relevant context preserved, creating a more scalable operating model for digital customer service.

Client background

Client: A Singapore-based commercial bank

Industry: Banking & Financial Services

Project type: AI-enabled customer service transformation

Business model: Retail and digital banking

Digital maturity: Established digital customer channels with growing service automation requirements

Objective: Scale customer service capacity, reduce repetitive operational work, and improve service consistency without proportionally increasing headcount

As digital adoption grew, the bank faced a corresponding increase in customer-service interactions. Continuing to scale primarily through additional staffing or isolated chatbot flows would increase cost and operational complexity.

The bank needed a more integrated service model that could make better use of enterprise knowledge, automate repeatable processes and help agents resolve complex requests more efficiently.

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The challenges

The bank needed to increase service capacity while maintaining response quality, operational control and appropriate human oversight.

Key challenges included:

• Rising cost-to-serve: Growing digital interaction volumes increased workload and made linear headcount expansion difficult to sustain.
• Fragmented service knowledge: Product information, policies, procedures and service guidance were distributed across multiple sources.
• High repetitive workload: Skilled agents spent significant time handling common inquiries, searching for information and routing requests.
• Limited contextual automation: Rule-based chatbots could handle predefined journeys but struggled with natural-language inquiries and exceptions.
• Inconsistent responses: Fragmented knowledge increased the risk of variation across agents and customer-service channels.
• Need for human judgment: Sensitive, ambiguous or complex interactions still required controlled escalation to service teams.
• Integration complexity: AI needed to operate within existing service processes rather than become another disconnected customer-service tool.

Our solution

Kyanon Digital structured the solution around four pillars designed to improve service scalability without requiring replacement of the bank’s existing core platforms.

Pillar 1 – Establish a governed enterprise knowledge foundation

Kyanon Digital created a structured knowledge layer that enabled the AI service to retrieve information from approved enterprise sources, including relevant product information, procedures and service guidance.

Using contextual retrieval rather than relying on users to know where information was stored gave service teams a more consistent way to access trusted knowledge.

Key outcome: Faster access to relevant service information with less manual searching across fragmented sources.

Pillar 2 – Introduce an AI copilot for service teams

The AI copilot supported agents during customer interactions by retrieving relevant information, summarizing available context, preparing responses and surfacing appropriate next steps for defined service scenarios.

The solution was designed around bounded use cases, keeping AI assistance connected to approved enterprise knowledge and operational processes rather than allowing unrestricted automated responses.

Key outcome: Agents spent less time searching and consolidating information and more time resolving customer needs. 

Pillar 3 – Automate routine service workflows and escalation

Kyanon Digital connected AI assistance with workflow automation to classify eligible requests, initiate service workflows and route inquiries to the appropriate team.

Cases requiring additional judgment were escalated to human agents with relevant interaction context preserved.

Key outcome: Routine interactions required less manual coordination, while complex cases could move to the appropriate service owner more efficiently. 

Pillar 4 – Integrate, monitor and continuously improve the service layer

The AI layer was integrated with relevant customer-service applications and enterprise systems rather than operating as a standalone chatbot.

Monitoring and analytics provided visibility into usage, escalation patterns, response performance and areas where knowledge or workflows could be improved.

Key outcome: The bank established an AI capability that could evolve alongside its customer-service operations and expand into additional use cases over time.

Tech stack

The solution used a modular AI architecture designed to integrate with the bank’s existing service environment without replacing core systems.

This modular approach allowed AI models, enterprise knowledge and service workflows to evolve independently while remaining connected to existing business applications. 

Results & business impact

The AI-powered service model helped the bank scale customer service more efficiently by improving agent productivity, reducing repetitive work and accelerating customer resolution. 

• Faster customer resolution: Average handling time for AI-assisted enquiries was estimated to decrease by approximately 25%, as agents could retrieve relevant information and prepare responses with fewer manual search steps.
• 40% less knowledge search effort: Time spent locating service procedures, product information and approved guidance was estimated to decrease by approximately 40%, reducing one of the most repetitive activities in the agent workflow.
• 20% higher service capacity: The AI-assisted operating model was designed to enable teams to handle approximately 20% more eligible enquiries without proportional headcount growth, improving service scalability as digital demand increased.
• 30% less repetitive manual work: Routine information retrieval, request classification and routing activities were estimated to require approximately 30% less manual effort, freeing agent capacity for more complex customer interactions.
• More consistent service responses: Grounding AI assistance in approved enterprise knowledge created a more consistent information base across supported service journeys, reducing variation between agents and channels.
• More efficient human escalation: Automated classification and contextual handoff reduced repeated information gathering when cases required human intervention, helping complex enquiries reach the appropriate service team faster.
• A scalable foundation for further automation: The reusable AI, knowledge and integration layer created a foundation for expanding into additional customer-service and employee-assistance journeys without rebuilding the underlying architecture for each new use case.

Kyanon Digital helps financial institutions combine generative AI, enterprise knowledge, system integration and workflow automation to improve service efficiency while working with existing business systems.

Explore Kyanon Digital’s AI Application & Copilot Development Services.