How P&C Insurers Unlock Data Governance Funding

A P&C Insurance Perspective

If data governance were easy to fund in P&C insurance, we wouldn’t still be explaining it after the third data incident review.

Most P&C data leaders already understand the stakes. Sensitive claims data. Regulatory scrutiny across jurisdictions. Increasing use of AI in underwriting, claims triage, fraud detection, and pricing. The problem isn’t awareness. It’s traction.

Governance keeps losing budget battles not because it lacks importance, but because it’s framed in a way that doesn’t map cleanly to how P&C executives think about risk and capital.

The Real Data Governance Funding Problem in P&C

In insurance, funding follows exposure. Loss ratios, litigation risk, regulatory findings, operational disruption. Governance proposals often fail because they are positioned as “data hygiene” instead of loss prevention.

When governance is described as another exercise in compliance, it gets deprioritized behind initiatives with clearer impact on underwriting margin, claims leakage, customer retention, or regulatory exposure.

Why Asking for “Governance” Budget Rarely Works

P&C executives are not allergic to governance. They are allergic to:

• Open-ended programs

• Benefits that can’t be quantified

• Spend that doesn’t map to a risk scenario

They will fund:

• Reducing the likelihood of a class action tied to exposed PII

• Limiting regulatory penalties tied to improper data handling

• Protecting pricing models and underwriting IP

• Ensuring AI does not create discoverability risks in claims or underwriting

Governance supports all of these. It just needs to say so explicitly.

The Reframe That Works: Data Risk in Insurance Terms

The most effective funding conversations start by defining Data Risk in P&C terms.

• Exposure of claims data containing PII, PHI, or financial information

• Improper access to underwriting or pricing models

• Retention of stale claims data beyond regulatory or business need

• Use of sensitive data in AI tools without controls

  
  

These are not abstract concerns. They already show up in audits, regulatory exams, and litigation.

The Funding Wedge: AI-Powered Data Classification

For P&C insurers who have struggled to get traction with funding their governance department, AI-powered data classification offers a practical entry point because it:

• Creates immediate visibility into risk concentration

• Easily scales across structured policy systems and unstructured claims files

• Identifies sensitive data in environments previously difficult to parse, such as adjuster notes, attachments, photos, and third-party feeds

Classification can then be positioned as a risk mitigation pilot, not a governance overhaul, to:

• Identify overexposed claims files

• Surface unknown sensitive data in shared environments

• Reduce the likelihood of accidental disclosure or misuse

That’s a story executives understand.

How to Build an Executive-Grade Business Case

Effective business cases define quantifiable actions which are anchored to real world insurance scenarios:

• “We will reduce the number of claims files with broad access that contain sensitive data by X% in 90 days.”

• “We will identify how many pricing and underwriting models are currently accessible beyond these X intended roles.”

·         We will reduce the number of sensitive files over 10 years old that serve no business purpose by X%

Executives don’t need perfection. They need measurable and actionable risk reduction.

How This Unlocks Long-Term Governance Funding

Once classification is in place, governance becomes measurable:

• Clear ownership of high-risk data assets

• Prioritized remediation aligned to exposure

• Metrics that show risk trending down over time

At that point, governance stops being a theoretical need and becomes part of the insurer’s risk management discipline.

In P&C, the strongest governance programs don’t argue for budget. They demonstrate loss avoidance and get expanded.