Normalized claim
Efficiency gain in trade operations: 30% increase
Achieved 30% efficiency gain in trade operations by eliminating manual entry and human errors.
Hong Leong Bank accelerates trade finance document processing with Document AI and deploys Gemini agents on Vertex AI
Use case typeCustomer support automationUpdated Jun 13, 2026
Hong Leong Bank (HLB) built its AI ecosystem on Google Cloud to improve digital banking and employee experiences. The bank uses Document AI for trade operations to automatically extract and digitize relevant fields from high-volume trade documents in more than 200 languages. HLB also runs Gemini 2.5 Flash as a private instance on Vertex AI and uses Agent Development Kit-based agents for customer and internal workflows.
- Organization
- Hong Leong Bank
- Industry
- Finance
- Location
- Malaysia
- Published
- June 2026
Reported outcomes
3x
monthly chat transactionsCustomer experience
+30%efficiency gain in trade operations75-99%chatbot accuracy+70%chat volume handled by chatbot
Strategic outcomes
Risk & complianceImproved sanctions screening and document accuracyCustomer experience & trustImproved customer and employee experiencesNew product / capabilityDeployed private AI chatbot agentsScale & capacityHandled high-volume chat interactions
Catalog median for customer experience deployments: +25% across 53 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Chatbot accuracy: 75-99% increase
Chatbot accuracy increasing from 75% to 99%.
Normalized claim
Monthly chat transactions: 3 x increase
Driving a 3x increase in digital engagement of monthly chat transactions.
Normalized claim
Chat volume handled by chatbot: 70% increase
The chatbot handled 70% of chat volume.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Hong Leong Bank
- Provider
- GCP
- Maturity
- Production
- Linked source
- Google Cloud Customer Story
Deployed Gemini 2
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 3
- 1Trade Finance Automation
- 2Customer Service Automation
- 3Employee Productivity
- Slow, manual processing of high-volume trade documents such as invoices and shipping forms.
- Risk of errors and sanctions-screening delays in trade operations and underwriting.
- Need to improve customer and employee experiences while preserving data governance and security.
- Built an AI ecosystem on Google Cloud centered on Vertex AI and Document AI.
- Used Document AI Trade Operations OCR to extract and classify document fields and support sanction screening.
- Deployed Gemini 2.5 Flash as a private instance on Vertex AI for chatbot and agent workflows.
- Used the Agent Development Kit and secure API calls to connect Gemini agents to backend systems.
- Added Cloud Run, Cloud SQL, Google Cloud Search, Compute Engine, and Gemini Code Assist as supporting services.
- Document AI delivered a 30% efficiency gain in trade operations by removing manual entry and human errors.
- The Marina chatbot handled 70% of chat volume.
- Chatbot accuracy improved from 75% to 99%.
- Monthly chat transactions increased 3x.
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jun 7, 2026Publisher: Google CloudEvidence: VendorConfidence: Medium
AI-generated summary. Verify important details with the linked sources before relying on this case.
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