Install the oasislmf Python package (available on PyPI as 'oasislmf') in your data preparation environment; this package includes the MDK (Model Development Kit) and exposure validation utilities
Load your exposure data as a pandas DataFrame from your source system export (CSV or database query); ensure columns map to OED field names as specified in the OED schema documentation
Run the oasislmf exposure validation command against your exposure file, specifying the OED schema version your target model expects; the validator reports missing required fields, out-of-range values, and unrecognized codes
For each validation error, apply remediation logic: fill missing geocodes using a geocoding API if latitude/longitude is absent, map internal occupancy codes to OED occupancy scheme codes, and set default construction codes where data is unavailable
Re-run the validator until the report shows zero critical errors; review and document any warnings that represent known data quality limitations for inclusion in your model output caveats
Commit the validated exposure file to the platform upload step; retain the pre-validation and post-validation files with version timestamps for audit
Known gotchas
OED occupancy and construction codes are specific to the OED standard and do not correspond to ISO or NCCI codes; many carriers maintain internal code sets that must be translated — build and maintain a lookup table for each code dimension
The oasislmf MDK validates against the OED schema, but individual cat models may impose additional constraints beyond the base OED standard (model-specific required fields or allowed value ranges); obtain the model's own data requirements document separately
Geocoding quality has an outsized impact on cat model output; properties with country-centroid or postal-code-centroid geocodes rather than street-level geocodes can produce loss estimates that are off by orders of magnitude in spatially variable perils like earthquake and flood
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