Speeding Up ‘First Notice of Loss’: Why Trusted Data Is the Real Differentiator in Modern Claims

AI adoption in insurance has moved well past the pilot phase. It’s now delivering clear, quantifiable improvements. By 2025, insurers using AI‑driven automation saw claim settlement times improve by up to 59%, with processing speeds increasing by as much as 75% and risk‑assessment accuracy reaching 99%. These gains are raising the bar for operational performance – and reshaping what customers and regulators expect from modern claims organisations.

Yet, beneath the headline gains, a structural challenge still remains. More than 60% of organisations are failing to realise the full value of AI initiatives due to insufficient data governance maturity.

For insurers modernising claims performance, this gap becomes instantly visible in day-to-day operations, often surfacing at the First Notice of Loss (FNOL), where accuracy of initial data determines whether automation drives confident decisions or introduces avoidable risk.

What is FNOL? The Moment Shaping the Claim

First Notice of Loss (FNOL) typically captures core details such as policy numbers, dates and times, location, third-party involvement, injuries, and supporting evidence like photos or written descriptions. This information feeds directly into case management systems, fraud detection tools, and AI-driven severity scoring models.

If FNOL data is missing, inconsistent, or lacks clear lineage, the claims process becomes slow and full of potential risk, as adjusters often need to manually transfer data between different systems.

In modern claims operations, FNOL is the critical control point where data accuracy must be established to enable scalable automation and faster, more reliable service delivery.

The Governance Gap in Insurance Artificial Intelligence

Insurance organisations often struggle to scale AI because their data remains fragmented across legacy policy systems, claims platforms, and cloud analytics environments. When policy, incident, and claimant information is siloed, AI initiatives are far more likely to fail – in fact, around 65% of AI projects launched in companies with heavily-siloed environments do not succeed. The issue is rarely the algorithm; it is the absence of consistent, connected data.

How do you reduce this risk? The answer is simple – strong data governance. Organisations with mature governance frameworks report 25% faster decision-making in 72% of cases, demonstrating that governance is not simply about oversight – it enables a shared, trusted view of the truth that accelerates action across the business.it enables a shared, trusted view of the truth that accelerates action across the business.

When FNOL data is clean, classified, and fully traceable, your claims teams and AI systems operate with far greater accuracy and predictability – driving smarter automation, faster resolution, and AI that performs reliably at scale rather than in isolation.

How to Embed Trusted Data into FNOL Architecture with Microsoft Purview

Ultimately, the only way to ensure only trusted data is making its way into your architecture is to embed data governance at the point of ingestion and not treat it as a downstream compliance exercise.

Platforms such as Microsoft Purview provide a unified framework covering discovery, classification, lineage tracking, and policy enforcement across hybrid environments.

Purview enables insurers to automatically identify and classify claims and policy data across distributed systems. Business glossaries can be standardised so that critical operational terms maintain consistent meaning across underwriting, claims, and analytics domains.

A 2025 Forrester Total Economic Impact™ analysis of Purview deployments reported:

• 30% reduction in likelihood of data breaches.
• 75% faster data search and classification.
• 60% reduction in manual compliance and audit effort.

In FNOL processes, this governance layer helps to ensure that adjusters review current, traceable policy and incident information and compliance teams can demonstrate end-to-end lineage from intake to decision.

Strengthening governance at intake also allows insurers to apply advanced analytics right from the start. That means potentially suspicious and high-risk signals or fraudulent claims can be indentified and flagged before they gain operational momentum.

Proactivity becomes the priority, allowing routine claims to move through automated pathways more efficiently, while complex or higher-risk cases receive the attention of experienced adjusters – improving claims processing accuracy and reinforcing the value of trusted data at FNOL.

AI-Driven Claims Accuracy: Why You Need Data Governance

As noted, AI-led claims transformation hinges on accuracy. Automation without trusted data simply scales inconsistency, erodes confidence in decision-making, and increases downstream rework. The economic benefit of AI in insurance is realised only when the data feeding models and workflows is reliable from the outset.

FNOL shapes every subsequent action of the claim. When governance is embedded directly into intake through platforms like Microsoft Purview, insurers strengthen claims processing accuracy at the source. Clean classification, lineage visibility, and policy enforcement reduce errors before they compound.

Accuracy at first notice determines whether AI enhances performance or quietly amplifies risk across the claim lifecycle.

The Strategic Direction for Modern Insurance

The future of claims processing will be defined by trust as much as it is by technology. AI and automation only deliver sustained value when they operate on clean, governed data.

FNOL is the natural starting point for building that foundation. By embedding governance, metadata intelligence, and lineage visibility into claims intake, insurers can improve processing accuracy, reduce operational risk, and increase confidence in automated decision-making.

If you are investing in AI or modernising claims operations, trusted data at FNOL is the control point that determines whether your transformation delivers measurable advantage or quietly erodes value at scale.

How can Simpson Associates Help You?

We help insurance firms, like yours, harness the power of their data to create secure, well-governed data environments that excel claims efficiency and accuracy. As a data transformation partner with over 3 decades of deep, technical expertise – and a Microsoft Fabric Featured Partner, alongside established Microsoft, Databricks and IBM partnerships – we support insurers in unifying complex data estates, strengthening governance, and unlocking trusted insight.

Whether you’re improving your FNOL system, implementing data governance principles with Microsoft Purview or exploring AI, we help insurers eliminate siloes, reduce regulatory risk, and deliver consistent, customer-centric outcomes at scale. Explore how we work with insurance organisations or get in touch with us via email or live chat.