ExposureHUB is now available in the U.S. Read the announcement →

Trust the numbers before they are used.

Exposure data quality is the foundation of accurate underwriting. The DQ Module detects and corrects data issues before they flow through to analytics, reporting or regulatory submissions.

  • Every record checked against your rules
  • Problems flagged before they reach analytics
  • An audit trail for every correction
Request a demoSee the checks

Catch it on arrival, not after the decision.

Policy · a schedule arrives
ExposureHub Policy tab: a schedule of 1,000 locations with a map of the Massachusetts portfolio
Checks on arrivalIllustrative
  • Latitude: 42.37686Passed
  • Zip: (blank)Missing
  • TIV: 2,514,500Passed
  • Latitude: 242.37686Out of range

Data quality issues surface after decisions are made. This catches them on arrival.

Three problems exposure teams know well, and what the DQ Module does about each.

Data quality issues surface after decisions have already been made

Automated validation against configurable quality rules, as each record arrives.

Multi-source data from different carriers and regions arrives in incompatible formats

Missing, inconsistent and out-of-range values flagged before they reach analytics.

Regulatory pressure from market bodies demands more rigorous, timely exposure reporting

Correction workflows with an audit trail per record, to support regulatory compliance.

exposurehub / data-qualityIllustrative
RECORDFIELDVALUESTATUSLOC-10421PostcodeSW1A 2AAVALIDLOC-10422Postcode(blank)MISSINGLOC-10423Insured value4,250,000VALIDLOC-10424Insured value4,250,000,000OUT OF RANGELOC-10425Year built1987VALIDLOC-10426Inception31/02BAD DATELOC-10427Storeys6VALIDLOC-10428ConstructionReinforced concreteVALIDAUDIT TRAIL3 records flagged · awaiting reviewcorrected by m.alvarez · 14:02LOC-10422 · postcode → EC2N 4AYcorrected by m.alvarez · 14:02LOC-10424 · insured value → 42,500,000corrected by m.alvarez · 14:03LOC-10426 · inception → 28/02QUALITY SCORE0%3 RECORDS FLAGGEDFLAGGED3CORRECTED0VALID5SAMPLE BATCH · 8 RECORDS

IllustrativeDrawn from the module description with fictional sample data. Not a screenshot.

Five checks, shown on example records.

Each line of the DQ Module description, drawn as a concrete example. The values are fictional; the column names are the ones on the real Policy schedule.

Automated validation against configurable quality rules

Every record checked against the rules you set, as it arrives.

DQ Module · rulesIllustrative

A record arrives

Limit Group
All, mass
Inception
01/01
Expiry
31/12
Country
United States of America
State
Massachusetts
Zip
01002
Latitude
42.37686
Longitude
-72.46914
TIV
2,514,500

Your configured rules

  • Country is present
  • Expiry is after Inception
  • Latitude is between -90 and 90
  • Longitude is between -180 and 180
  • TIV is greater than zero

IllustrativeFictional records on the real Policy schedule column names.

Before third-party data reaches the model: validate, normalise, monitor.

Satellite imagery, climate data and social media data can give a more complete understanding of risk, if it is checked first. This is how Ebix Europe describes good practice.

Validate

Validating third-party data sources and checking for inconsistencies or errors in the data is critical to ensure accuracy and completeness.

Inconsistencies and errors caught

Normalise

Standardising third-party data makes it compatible with the platform's data model: high quality data from many sources in a standard format aligned to the platform's schema.

Aligned to the platform's schema

Monitor

Watch third-party sources for changes or updates so the data stays accurate, secure, compliant and up to date, usually with alerts to platform users.

Alerts when a source changes

Source: Ebix Europe, on the Lloyd's syndicate bulletin. Securely feeding third-party data also involves encryption, access controls and vendor management.

From record to proof, in four moves.

Received, tested, corrected and recorded, with the history kept for whoever asks.

Receive

Records arrive from files, systems and entry.

Validate

Each record is tested against your rules.

Correct

Flagged records go to an owner for correction.

Prove

The audit trail shows what changed and why.

Keep going: the rest of the platform.

Every module works on the same validated portfolio, so each one you add builds on the last.

ConfigHub

Every client's exposure management process is different. ConfigHub gives your team complete control over validation rules, data models, workflows and export formats, fully integrated within your governance structure.

Customise validation rules without IT dependencyConfigure workflows to match your team's processesDefine data models and field mappings per client or program

Also in the platform

Or go back to the overview.

Insight ModuleEnergy ModulePlatform overview

See DQ Module in action.

Book a walk-through of this module on your own data, with your own questions.

Request a demoExplore the platform