Insurance Data Quality AI Readiness Assessment: A Practitioner’s Checklist

I’ve seen claims teams burn months of runway because they treated data quality as an afterthought. One mid-size P&C; carrier kicked off a fraud detection pilot with 12% missing claimant addresses. The model’s precision tanked, and the project was shelved. That’s avoidable. This guide walks through a repeatable framework to assess whether your data is fit for AI—before you write a single line of code.

Why a checklist is non-negotiable

Insurance data is messy by design: policy inception dates, loss runs, bordereaux from TPAs, and unstructured adjuster notes. AI models chew through this raw material. If your data has a 20% duplicate rate or 30% missing values in key rating fields, your combined ratio on AI projects will climb fast.

Phase 1: Inventory & Criticality Mapping

Start with a data catalog that answers: what exists, where it lives, who owns it, and how it’s used. Without this, you’re flying blind.

Step 1: Define your AI use case boundary

• Goal: Narrow the scope to something bounded like “predictive underwriting for small commercial auto” or “fraud signal extraction from SIU reports.”
• Trade-off: If you try to boil the ocean (e.g., “transform claims end-to-end”), your checklist becomes a sprawling spreadsheet that no one updates.

Example: A regional carrier targeting a 5% uplift in loss ratio through telematics underwriting limited its AI scope to renewal auto policies with ≤$500K premium.

Step 2: Map data sources to use case

Create a table like the one below. Include the data’s origin (TPA, MGA, internal system), format (CSV, JSON, PDF), and update cadence.

Step 3: Identify key fields and business rules

For each use case, list the 10–15 fields that directly impact outcomes. For example:

• Underwriting: Driver age, vehicle make/model, ZIP code, prior losses.
• Claims: Date of loss, incurred loss amount, cause of loss, adjuster notes.

Trade-off: Including “nice-to-have” fields like policyholder’s favorite color will dilute model performance and inflate data cleaning costs.

Resource estimate (Phase 1)

• Analyst time: 2–3 weeks full-time.
• Tooling: Collibra or Alation (if you have budget); otherwise, a shared Confluence page with manual updates.

Phase 2: Data Profiling & Quality Metrics

Step 4: Run automated profiling

Use open-source tools like pandas-profiling (Python) or commercial suites like Informatica Data Quality. Focus on:

• Completeness: % missing values per field.
• Uniqueness: Duplicate policy numbers or claim IDs.
• Consistency: Inconsistent date formats (MM/DD/YYYY vs. YYYY-MM-DD).
• Validity: ZIP codes outside 5-digit ranges or ICD-10 codes that don’t exist.

Example snippet:

import pandas as pd
from ydata_profiling import ProfileReport

# Load loss run data
df = pd.read_csv("loss_runs_2023.csv")

# Generate profile
profile = ProfileReport(df, title="Loss Runs 2023")
profile.to_file("loss_runs_profile.html")

Trade-off: Profiling tools can flag 100+ issues. Prioritize those that violate business rules (e.g., negative incurred loss amounts) over cosmetic outliers.

Step 5: Calculate downstream impact

Quantify the cost of poor quality. For example:

• If 15% of claims are missing “cause of loss,” your fraud model’s precision drops by ~12% (based on a 2022 study by ISO ClaimSearch).
• If ZIP code is missing for 20% of policies, your territorial ratemaking error could exceed 8% of premium (per NAIC data).

Step 6: Validate against regulatory & actuarial standards

Check against:

• NAIC Model Laws: For example, Schedule P requires loss development patterns to be auditable. If your data lacks transaction IDs for each loss payment, you’re non-compliant.
• IFRS 17: For reserving models, ensure granularity for each cash-flow component (e.g., claims handling expenses broken down by adjuster).