What challenges did NASDAQ face before AI implementation?

Financial institutions need to scale mission-critical workloads on secure, resilient cloud infrastructure while deploying AI agents to improve trading, fraud detection, claims processing, compliance, and customer service in regulated environments.

What was the impact of the AI solution?

Reduced operational costs and latency, enhanced revenue streams, improved resilience, and accelerated innovation in financial services. Processing time for insurance claims reduced by 80% using multi-agent AI workflow automation.

Source

Scaled productionEvidence: Medium50/100

Financial institutions advance mission-critical workloads and Agentic AI at re:Invent 2025

Use case typeFraud detectionUpdated Jun 13, 2026

At AWS re:Invent 2025, multiple financial institutions including NASDAQ, Visa, National Australia Bank, BlackRock, and Allianz Technology SE showcased advanced implementations of AI agents and mission-critical cloud workloads using AWS technologies. Financial institutions are rapidly shifting from debating AI adoption to deploying agentic AI for competitive advantage, with developer productivity significantly improved. Use cases showcased include real-time trading assistants, fraud detection, claims processing, compliance automation, and customer service enhancements using Amazon Bedrock AgentCore and other AWS AI tools. AWS provided enhanced infrastructure and service capabilities such as multi-region resiliency, data masking, catalog federation, and Amazon Nova Forge for custom frontier models. Several financial institutions demonstrated multi-agent architectures to automate insurance claims and customer service workflows, reducing processing times by up to 80%. Advanced AI agents are integrated with policy controls for regulatory compliance and auditability, supporting trading, payments, and wealth management applications. AWS innovations like Amazon Connect enhancements and AI-powered recommendations enable personalized customer experiences and operational efficiencies.

Organization: NASDAQ
Industry: Finance
Location: United States
Published: January 2026

Reported outcomes

Time: −80%

Time & speed

Catalog median for time & speed deployments: −50% across 295 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 80% decrease

AWS Machine Learning BlogJan 14, 2026Blog postInferred claimMedium evidence strength

Processing time for insurance claims reduced by 80% using multi-agent AI workflow automation.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: NASDAQ, Visa, National Australia Bank, BlackRock, Allianz
Provider: AWS
Maturity: Scaled Production
Linked source: AWS Machine Learning Blog

Enhanced data foundations on AWS like Amazon Aurora PostgreSQL dynamic masking and AWS Glue Catalog Federation enable secure, cost-efficient AI at scale

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 5

1AI Agents
2Fraud Detection
3Claims Processing

Institutions have migrated legacy systems and workloads to AWS maintaining high availability and low latency.
They are deploying multi-agent AI architectures using Amazon Bedrock AgentCore with real-time policy control and monitoring to ensure security and compliance.
Enhanced data foundations on AWS like Amazon Aurora PostgreSQL dynamic masking and AWS Glue Catalog Federation enable secure, cost-efficient AI at scale.
The use of Amazon Nova Forge enables building custom frontier AI models trained on proprietary data for specialized financial applications.

Technologies

Amazon Bedrock AgentCore Amazon Route 53 Amazon Aurora AWS Glue Catalog Federation Amazon S3 Tables Amazon Nova Forge AWS Security Agent AWS DevOps Agent Amazon Connect

Reduced operational costs and latency, enhanced revenue streams, improved resilience, and accelerated innovation in financial services.
Processing time for insurance claims reduced by 80% using multi-agent AI workflow automation.

Architecture

The architecture includes multi-agent AI systems orchestrated with Amazon Bedrock AgentCore, AI governance via policy controls, multi-region disaster recovery and resiliency mechanisms, and data infrastructure with Amazon Aurora dynamic masking and AWS Glue Catalog Federation.

Sources & evidence1

Evidence: Medium50/100Evidence strength