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    Home»Fintech»Experian and ServiceNow Launch ‘Agent Operating System’ to Bring Regulated Agentic AI to Lending Workflows
    Fintech

    Experian and ServiceNow Launch ‘Agent Operating System’ to Bring Regulated Agentic AI to Lending Workflows

    币安计划官方By 币安计划官方June 14, 2026No Comments4 Mins Read
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    Experian and ServiceNow Launch ‘Agent Operating System’ to Bring Regulated Agentic AI to Lending Workflows
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    In one of the most significant artificial intelligence infrastructure rollouts at this year’s Money20/20 Europe conference, global information services giant Experian has launched the Experian Agent Operating System™ (AOS).

    Unveiled on the Horizon Stage in Amsterdam, the platform arrives as a new core capability within Experian’s established Ascend Platform. To accelerate enterprise deployment, software heavyweight ServiceNow has signed on as the foundational launch partner under a multi-year global agreement, embedding Experian’s underlying data and risk governance frameworks directly into core corporate workflows.

    The launch marks a transition for enterprise AI within regulated financial markets, shifting the industry focus away from single-prompt generative chatbots and toward networks of autonomous software agents authorized to execute high-stakes lending decisions.

    Dismantling the Enterprise AI Bottleneck

    Despite massive industry-wide budgets flowing into artificial intelligence research, financial institutions continue to encounter severe operational bottlenecks when attempting to scale autonomous models. The hurdle rarely stems from the algorithms themselves, but rather from the underlying data management systems.

    New global research published by Experian alongside the launch highlights these structural vulnerabilities:

    • Workflow Integration: Nearly half of global financial organizations (48 per cent) state that integrating complex data layers into automated AI workflows remains heavily problematic.

    • Lineage Obscurity: Approximately one-third of risk executives report poor or unverified data lineage within their legacy models.

    • Siloed Infrastructure: An additional third identify fragmented data silos across isolated internal teams and vendor systems as a primary roadblock to production.

    Experian’s Agent Operating System is engineered to bypass these roadblocks. It establishes a unified semantic, trust, and orchestration layer that connects software agents developed by Experian, the financial institutions themselves, and third-party technology providers into one controlled operational ecosystem.

    Capitalizing on a Shift in Consumer Trust

    The urgency for financial firms to deploy production-grade agentic layers is further reinforced by a rapid evolution in consumer comfort with machine autonomy. A HarrisX poll conducted between March and April 2026 revealed that 55 per cent of global consumers are now willing to authorize an AI agent to execute autonomous commercial purchases on their behalf.

    This consumer appetite spikes dramatically among younger demographics, climbing to 70 per cent for individuals aged between 25 and 39. As consumers prepare to delegate real-world financial actions to software models, banks require a heavily auditable backend system to verify, track, and protect those automated pipelines.

    An Overview of the Multi-Agent Architecture

    The architecture of the Agent Operating System is structured around five core engineering pillars designed to cover the entire lending cycle, from initial customer acquisition and fraud mitigation to risk decisioning, portfolio monitoring, and regulatory reporting:

    • The Trusted Operating Layer: Establishes centralized identity verification, rigorous access controls, and systemic compliance guardrails across all automated workflows.

    • Ecosystem Composability: Allows multi-vendor AI models and internal client-built software tools to collaborate natively inside existing technology stacks without forcing expensive legacy core migrations.

    • Agent-Native Decisioning: Deploys goal-directed models specifically trained to investigate anomalies and optimize workflows autonomously rather than passively reacting to human text prompts.

    • Embedded Governance by Design: Hardcodes model risk management, clear explainability protocols, and unalterable audit trails right into the transactional layer to satisfy strict UK and European regulatory oversight.

    • Human-in-the-Loop Safeguards: Automates complex high-velocity validation checks while systematically routing edge cases and high-impact decisions back to human compliance officers.

    The ServiceNow Integration Advantage
    Vijay Mehta, general manager AI at Experian Software Solutions

    The multi-year alliance with ServiceNow places this agentic system right inside the operating cockpit of global enterprise operations. By routing Experian’s credit intelligence through ServiceNow’s workflows, institutions can execute autonomous actions at a significantly higher velocity.

    The initial joint deployments will focus explicitly on high-scrutiny operational use cases, including rapid employee onboarding, automated third-party corporate risk assessments, and model lifecycle compliance governance.

    “Agentic AI will reshape financial services, changing how decisions are made, how customers are served and how organisations operate at scale,” stated Vijay Mehta, general manager AI at Experian Software Solutions. “This is no longer about experimentation. The winners will be those who turn AI into trusted operational reality, built on high quality data, strong governance and transparency in every decision. The Experian Agent Operating System is built for that moment.”

    The platform is currently undergoing live testing with a select pool of early adopters and is slated for a broader international rollout later this year, eventually scaling to optimize more than 2,300 enterprise client solutions globally. As global regulators place tightening transparency rules around autonomous financial software, Experian’s latest layout demonstrates that the scaling of enterprise AI will depend on deep data pedigree and verifiable governance.



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