Asian neo bank saves $1.5M annually by automating 85% of customer support with AI agents
“Asian neo bank saves $1.5M annually by automating 85% of customer support with AI agents” documents a Customer Service & Virtual Assistants deployment in Digital & Neo at Unnamed Neo Bank (Asia). www.getdynamiq.ai reports annual cost savings: $1.5M; this directory has not independently verified that result.
Evidence at a glance
- Evidence status:
- Automated evidence gate passed
- Deployment timeframe:
- Not reported by source
- Reported outcome metrics:
- 3 cited below
- Directory entry published:
- Source link checked:
The source-link check confirms reachability, not independent re-verification of every claim.
Source-reported figures — cited source: www.getdynamiq.ai
The Challenge
A fast-growing digital bank serving a large customer base in Asia struggled to manually handle millions of support queries at scale while maintaining responsiveness, accuracy, and compliance. A simple chatbot was insufficient — the bank needed an AI agent that could understand diverse requests, connect to internal knowledge bases, safely interact with backend systems, and provide real-time monitoring.
The Solution
The bank partnered with Dynamiq to build a fully autonomous customer support AI agent using Dynamiq's low-code agentic platform. The agent answers questions from internal knowledge bases, escalates edge cases to humans with full context, calls backend APIs to perform transactions and account actions, and adapts based on performance evaluations. The team tested multiple LLMs (Anthropic Claude, OpenAI GPT) to select the most effective option.
Results
The AI agent now autonomously handles approximately 85% of all incoming support inquiries, generating an estimated $1.5M in annual savings. The solution was built and deployed in just 30 days with full testing and evaluation coverage. The bank plans to push toward higher automation of customer inquiries.
Key Takeaways
- Agentic AI with tool use, memory, and multi-step reasoning can resolve the vast majority of banking support queries without human intervention.
- Low-code platforms with built-in LLM benchmarking allow rapid iteration and safe model switching without production regressions.
- A production-grade AI customer support agent for a major digital bank can be deployed in as little as one month when using the right platform.
Explore Related
Details
- Industry
- Digital & Neo
- AI Technology
- Agentic AI & Autonomous Workflows
- Company Size
- MidMarket
- Company
- Unnamed Neo Bank (Asia)
- Evidence status
- Automated evidence gate passed
- Deployment timeframe
- Not reported by source
- Directory entry published
- Source link checked
Cited source
www.getdynamiq.aiHave a similar implementation?
Share your customer's AI results and link it to your vendor profile.
Submit a case study →