Actuarial Data Quality Checks
Pricing and reserving models run on large extracts of claims and exposure data pulled from multiple source systems, and that data routinely carries issues that don't announce themselves — duplicate claim records from a system migration, a coverage code that changed meaning between policy years, missing loss-date fields defaulted to a placeholder, exposure units recorded inconsistently across regions. An actuary running a pricing or reserving model on data with these issues gets an output that looks precise and is quietly wrong, and by the time a downstream result looks off enough to investigate, the flawed data has often already informed a rate filing or reserve booking.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 6-10 hrs/week of manual data validation ahead of pricing and reserving cycles.
How the automation works
We run a data quality pass on claims and exposure extracts before they reach a pricing or reserving model — checking for duplicate records, field completeness, internally inconsistent values like a claim date before the policy inception date, coding scheme changes between periods, and outlier values against historical distributions — and produce a quality report flagging exactly what's wrong and where, rather than a single pass/fail. An actuary reviews flagged issues and decides whether to correct, exclude, or proceed with a documented caveat; the check never silently drops or edits records on its own, because an actuarial dataset with records quietly removed is its own kind of data quality problem.
Process flow
- 01
Data extract pulled for modeling trigger
The check runs automatically on claims and exposure data extracted for an upcoming pricing or reserving run.
- 02
Detect duplicate records ai
Records are checked for exact and near-duplicates that can arise from system migrations or overlapping extract windows.
- 03
Check internal consistency ai
Fields are cross-checked against each other — loss date before policy inception, coverage codes inconsistent with the stated line of business — and against historical distributions for outlier values.
- 04
Check field completeness ai
Required fields for the model are checked for missing or placeholder-defaulted values that could otherwise pass through silently.
- 05
Generate quality report output
A report itemizing every flagged issue, its location and its likely cause is generated for the actuary — not a single aggregate score.
- 06
Actuary reviews and resolves output
The actuary decides whether to correct, exclude or proceed with each flagged issue; nothing is dropped or altered automatically before the data reaches the model.
Inputs
- Claims and exposure data extracts
- Historical data distributions
- Coverage and coding scheme documentation by period
- Prior data quality exception logs
Outputs
- Itemized data quality report
- Flagged duplicate and outlier records
- Field completeness summary
- Actuary resolution log
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- A coverage or peril code that changed meaning between policy years is one of the hardest data quality issues to catch, because the values look valid on their own — the check needs a coding-scheme mapping by effective period, not a single current-year schema applied retroactively across all historical data.
- Automatically dropping or correcting records flagged as duplicates or outliers before an actuary reviews them removes information a human might have judged genuinely valid, and an actuarial dataset silently missing records is a data quality problem in its own right — every automated flag needs a human decision, not an automated fix.
- Outlier detection tuned against the wrong historical baseline — say, comparing current claim severity to a pre-inflation-adjusted distribution — will flag normal recent claims as anomalies and miss actual data errors that happen to fall within an outdated 'normal' range; the baseline needs to be refreshed on the same cadence as the model itself.
- Missing loss-date or exposure fields defaulted to a placeholder value by an upstream system look complete and pass a naive completeness check — the check needs to specifically test for known placeholder patterns, not just flag genuinely blank fields.
Frequently asked questions
Does this correct or remove bad data automatically?
No. It flags issues with an explanation of what's wrong and where, and an actuary decides whether to correct, exclude or proceed with a caveat — nothing is altered without that review.
Can it catch coding scheme changes between policy years?
Yes, checks are run against a coding-scheme mapping specific to each effective period rather than assuming current definitions apply retroactively, which is where naive validation usually breaks.
Does this replace an actuary's own data review?
No, it surfaces issues at volume that would take hours to find manually, but the actuary still makes every judgment call on what to do with a flagged issue before the data feeds a model.
What kind of issues does it typically catch?
Duplicate records, internally inconsistent fields like a loss date before the policy start date, missing or placeholder-defaulted required fields, and outlier values against historical distributions.