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International GI and Re Group · Commercial insurance and reinsurance · London Market

How an international GI and Re group automated post-bind MRC extraction.

mea extracts data from Market Reform Contracts across nine lines of business, applies the group’s own policy administration data entry rules and proves the output against human quality-assured data. Post-bind processing cost is down. Data quality in every end system is up.

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Client Snapshot

International GI and Re group at a glance

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Client
International general insurance and reinsurance group (anonymized)
Company type
London-headquartered commercial insurance and reinsurance specialist
Lines of business
Property, cyber, hull, professional indemnity, personal accident, nuclear, energy, liability and contingency
Scope
Post-bind MRC data extraction and transformation
Deployment
Configured to the group’s policy administration data entry rules
Validation
Machine output compared against human quality-assured data
Core systems replaced
None

The context

Post-bind processing still ran on manual extraction.

In the London Market, post-bind processing of MRCs still runs on manual extraction and manual quality assurance. The group, a London-headquartered commercial insurance and reinsurance specialist, had set out to be the most efficient and technically enabled operator in its market. Manual post-bind work sat directly in the way.

Extracted data had to be transformed to meet the policy administration system’s data entry rules before it could be used. Across nine lines of business, from property and cyber to nuclear and energy, manual post-bind processing carried a structural cost that grew with volume.

The challenge

One of the market’s most complex documents, processed by hand.

Manual extraction

MRC data extracted line by line, across nine lines of business.

Manual QA

Every extraction checked by hand before it could be trusted.

Manual transformation

Data reshaped by hand to the policy administration system’s entry rules before it could be loaded.

Specialist classes

Nuclear, energy, hull and contingency each carry their own terms and structure.

Static rules

Data entry rules were fixed, with no mechanism to test or improve them.

What mea executes

Extract, transform, prove. Then improve.

MRC extraction

Defined data sets extracted from MRCs across all nine lines of business, tested against agreed fields. Extraction built for London Market documents, not adapted from generic tooling.

Rules-based transformation

The group’s data entry rules applied automatically, so data arrives in the format the policy administration system requires.

QA comparison

Machine output compared directly against human quality-assured data, across all nine lines including nuclear and energy.

Accuracy proven

The comparison proved a very high level of machine accuracy, improving progressively over time.

Rules refined

The same comparison showed where the group’s own data entry rules could improve, lifting data quality throughout.

The operating shift

The paradigm of execution: then vs now

The operating shift: before mea against with mea
Before mea With mea
MRC data extracted manually, line by line Extracted by mea across nine lines of business
Every extraction checked by hand in QA Machine accuracy proven against human QA data
Transformation applied by hand to meet entry rules Rules applied automatically to extracted data
Data entry rules static Rules refined and improved using extraction comparison
Post-bind cost grows with volume Post-bind processing cost significantly reduced

The business result

Lower post-bind cost. Better data in every end system.

Cost down

Significantly reduced cost of post-bind submission processing.

Accuracy proven

Machine output measured against human QA data rather than assumed.

Data quality up

Improved data accuracy and quality across the group’s end systems.

Better downstream data

Cleaner data feeding underwriting and future pricing.

Rules that improve

A feedback loop on the group’s own data entry rules, not only on the extraction.

Proof over pilots.

“Through using mea Ingestion we have progressively improved our data extraction accuracy, this saves us costs and enables us to improve our business data entry rules.”

Operations Manager, international GI and Re group

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