Normalized claim
Time: 95%
Achieved 95% answer accuracy and response times under 10 seconds, greatly improving employee efficiency.
BankUnited Enhances Customer and Employee Satisfaction for SMB Banking Using AWS Generative AI
Use case typeCustomer support automationUpdated Jun 13, 2026
BankUnited, a regional bank headquartered in Miami Lakes, Florida, implemented an AI application called SAVI utilizing Amazon Bedrock and Anthropic's Claude 2 model to improve the retrieval of policy and procedure information for employees. The challenge was that employees struggled to quickly find accurate information, resulting in lengthy call times and inconsistent responses to SMB customers. SAVI enables employees to ask questions naturally and receive reliable, near-instant answers with 95% accuracy and response times under 10 seconds using Amazon Bedrock and intelligent enterprise search. The solution reduced training time for staff, empowered employees to provide better advisory services to clients, and lowered call and email volumes on procedural questions. BankUnited shifted towards a 24/7 self-service model at reduced cost and plans to expand generative AI use to other business areas as part of their OpportunitiAI program.
- Organization
- BankUnited
- Industry
- Finance
- Location
- United States
- Published
- April 2026
Reported outcomes
10 seconds
timeTime & speed
95%time
Strategic outcomes
New product / capabilityLaunched an AI policy retrieval appEmployee experienceEnabled natural language employee supportCustomer experience & trustImproved customer service qualityNew business modelShifted to self-service support model
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Time: 10 seconds
Achieved 95% answer accuracy and response times under 10 seconds, greatly improving employee efficiency.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- BankUnited
- Provider
- AWS
- Maturity
- Production
- Linked source
- AWS Customer Stories
Decreased volume of calls and emails regarding procedural questions, shifting to a 24/7 self-service support model at a lower operational cost
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 3
- 1Information Retrieval
- 2Employee Assistance
- 3Customer Support Automation
Employees struggled to locate accurate and timely policy information, causing delays and inconsistent customer service in SMB banking.
- Developed the SAVI AI application leveraging Amazon Bedrock with Anthropic's Claude 2 model and intelligent enterprise search for effective policy information retrieval.
- Implemented natural language processing capabilities to allow employees to ask questions naturally and get quick, reliable answers.
- Streamlined integration using AWS managed services enabling focus on functionality rather than technical complexities.
- Achieved 95% answer accuracy and response times under 10 seconds, greatly improving employee efficiency.
- Reduced employee training lifecycle and enabled frontline staff to provide higher quality advice to customers.
- Decreased volume of calls and emails regarding procedural questions, shifting to a 24/7 self-service support model at a lower operational cost.
- Positive customer and employee satisfaction improvements and groundwork for generative AI expansion across divisions.
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: Apr 29, 2026Publisher: AWS Customer StoriesEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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