Vanguard has reduced the time financial advisors wait for data by 60 percent after deploying its Virtual Analyst, an internal AI system that now resolves 30 percent of all internal queries without human intervention.
The project followed eight AI-ready data principles set by Vanguard’s data engineering team. These principles included standardized formats, clear metadata, and consistent labeling across all data sources. The team used Amazon SageMaker for model training and Amazon QuickSight for dashboards. Data pipelines ran on AWS Glue and Amazon Athena, while Amazon Kinesis streamed real-time updates to keep models current.
Measurable results came quickly. Advisors previously spent up to two days waiting for answers to complex data requests. Now 85 percent of those requests are completed within one hour. The system also flagged 15 percent more compliance issues than manual reviews did in the same period, according to internal metrics shared with regulators.
The initiative began two years ago under Vanguard’s chief data officer. Early prototypes failed because data sources were inconsistent and labels were missing. After six months of cleanup, the team rebuilt pipelines to enforce the eight principles. By the end of the first year, the Virtual Analyst handled 12 percent of queries. That climbed to 30 percent after the second year as more teams adopted the tool.
Vanguard did not disclose the total cost of the project. A spokesperson said the main challenge was aligning legacy systems with new data standards, not the AI models themselves.
Source: aws.amazon.com