# How an Asian neo-bank automated customer support

URL: https://www.getdynamiq.ai/case-studies/automating-customer-support-at-scale-how-a-neo-bank-saves-1-5m-a-year-with-ai

> How a digital bank in Asia built a support agent on Dynamiq that answers routine requests, acts on backend systems, and hands off to a person when needed.

A digital bank in Asia needed to answer a fast-growing volume of support requests without losing accuracy or control. It built a support agent on Dynamiq that answers from its own knowledge base, acts on backend systems, and hands off to a person when a request needs one.

Support automation

An Asian neo-bank automated about 85% of support inquiries and went live in 30 days.

Annual savings

The support agent now saves an estimated $1.5M a year in support costs.

Customer base

The bank serves more than 4 million customers across Asia.

Model flexibility

The team tests and switches between Anthropic and OpenAI models without risking production quality, checked by an evaluation suite before every release.

## The challenge

The bank serves more than 4 million customers across Asia. It is a digital-first lender whose support queue was growing faster than its team could hire. Every day brought thousands of the same kinds of requests: balance checks, card issues, password resets and policy questions, mixed in with harder cases that genuinely needed a person's judgment. A basic chatbot could not carry that load. The team needed an agent that understood a wide range of requests, stayed grounded in the bank's own policies and product details, could safely act on backend systems such as checking a balance or resetting a password, and left a record a compliance team could review after the fact.

## What we built

One of our engineers worked inside the bank's own environment, alongside its team, to design a support agent on Dynamiq: a single agent with tools, memory and a knowledge base, rather than a maze of separate bots handling separate intents. Success criteria and evaluation checks were agreed before the first version was built, so "working" had a definition everyone shared from day one. The agent answers from the bank's internal knowledge bases, calls the bank's own APIs to perform account actions, and escalates to a person with the full conversation attached whenever a request needs a human decision. Because the workflow made it easy to test and compare models from Anthropic and OpenAI side by side, the team picked whichever option answered accurately at the lowest cost, and kept the option open to switch again later without rebuilding the agent.

## How it runs

A customer's question arrives from the bank's app or website. The agent checks its knowledge base for the relevant policy or product answer, calls a backend API when the request needs account data or an action such as a password reset, and hands off to a support agent when the request is a dispute, an edge case, or anything outside what it is allowed to resolve alone, carrying the full transcript into that handoff so nothing gets repeated. Every run is traced and replayable, so support leads can see exactly what the agent looked up and said. An evaluation suite scores every candidate change against real conversations before it reaches customers, so the team can adjust prompts or swap models without risking a regression in production.

## Results

-   An Asian neo-bank automated about 85% of support inquiries and went live in 30 days.
-   The support agent now saves an estimated $1.5M a year in support costs.
-   The bank serves more than 4 million customers across Asia.

The team is now working to extend automation to a larger share of its inbound support volume, with the same evaluation suite gating every change before it ships.

## Built with

-   [Agent Builder](https://www.getdynamiq.ai/product/agents)
-   [Knowledge](https://www.getdynamiq.ai/product/knowledge-rag)
-   [Evals](https://www.getdynamiq.ai/product/evaluations)
-   [Observability](https://www.getdynamiq.ai/product/observability)

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