Supporting information for ServiceNow Otto for Financial Services Operations (FSO)

  • Release version: Australia
  • Updated March 12, 2026
  • 1 minute to read

    • Get a quick overview of the important information that is related to the ServiceNow Otto for Financial Services Operations (FSO) application.

    Supported user interfaces

    AI skills in FSO are supported in the following user interfaces.

    Table 1. Supported interfaces
    Interface Skill
    Financial Services Workspace
    • Case summarization
    • Disputes intake via Virtual Agent
    Core UI Case summarization
    Customer 360 page and Interaction page in Agentic Contact Center for Banking
    • Customer profile summarization
    • Customer interaction context summary
    Customer 360 and Interaction page in Agentic Contact Center for Insurance
    • Insurance customer profile summarization
    • Insurance interaction context summary
    Portal Disputes intake via Virtual Agent

    Financial Services Operations AI agent collection

    The FSO AI agent collection (sn_fso_ai_agents) provides a shared library of AI agents for Financial Services Operations. The agents understand user-defined goals, formulate strategic plans, help human agents resolve cases, and autonomously execute tasks.

    For a list of AI agents included in the collection, see AI capabilities in Financial Services Operations.

    Query Orchestrator

    Query Orchestrator (sn_qry_orchstr) provides a shared, reusable orchestration layer for AI agents across Financial Services Operations business domains. The Query Orchestrator breaks down complex questions into smaller, targeted sub-queries; routes each sub-query to the most appropriate data source; and aggregates the results into a single structured response for the AI agent to use.

    Query Orchestrator provides the following capabilities:

    Query decomposition
    Analyzes complex questions and breaks it into sub-queries that can each be answered independently.
    Intent classification
    Identifies each sub-query intent to determine how it should be handled and which data source is most appropriate.
    Source routing
    Maps each sub-query to the most appropriate data source based on the classified intent. Examples include Knowledge Base search for article lookups, or Knowledge Graph for entity and relationship data.
    Parallel execution
    Dispatches sub-queries to their assigned sources concurrently to minimize overall response latency.
    Response aggregation
    Collects results from all sources and merges them into a single structured payload for the AI agent.
    Structured output
    Returns results in a consistent, schema-defined format that the AI agent uses to generate a comprehensive answer in the ServiceNow Otto panel.