How do I set up a reinsurance data warehouse for validating ceded and assumed data feeds?

To set up a reinsurance data warehouse for validating ceded and assumed data feeds, design it as a control and reconciliation platform first, and a reporting platform second. The key is to preserve source detail, standardize the data model, and build repeatable validation checks that compare what was sent, received, and booked.

1) Define the scope and use cases

Start by being explicit about what the warehouse must validate:

• Ceded feeds: outbound data to reinsurers/brokers
• Assumed feeds: inbound data received from cedents/brokers
• Accounting validation:
  • premium
  • commissions
  • losses
  • reserves
  • cessions
  • recoveries
• Operational validation:
  • record counts
  • missing fields
  • duplicate transactions
  • mismatched contract terms
  • late or out-of-period postings

Typical questions the warehouse should answer:

• Did we receive all expected transactions?
• Do source totals match downstream booking totals?
• Are treaty terms applied correctly?
• Are ceded and assumed results reconcilable by contract, period, and currency?

2) Build a canonical reinsurance data model

Use a layered model with a raw landing zone and a standardized warehouse layer.

Recommended layers

1. Landing / Raw

   • Store each source file or API payload as received
   • Preserve original format and timestamps
   • Never overwrite raw data

2. Staging / Standardization

   • Normalize field names, dates, currencies, and codes
   • Validate required columns and data types
   • Map source-specific values to standard reference tables

3. Core warehouse

   • Structured business entities:
     • policy / treaty
     • certificate
     • account period
     • claim
     • transaction
     • cash / premium / loss movement
     • reinsurer / cedent / broker
   • Use surrogate keys and slowly changing dimensions where needed

4. Reconciliation / Control layer

   • Load control totals
   • Store comparison results
   • Track exceptions and sign-offs

5. Reporting / Analytics

   • dashboards, BI extracts, audit reports, aging, exception queues

3) Standardize the key business entities

At minimum, define consistent master data for:

• Counterparty
  • cedent, reinsurer, broker, intermediary
• Contract
  • treaty, facultative, layer, term, attachment, limit, share
• Accounting period
  • as-of date, booking period, valuation date
• Currency
  • transaction currency, functional currency, reporting currency
• Exposure
  • policy, insured, location, class of business
• Claims
  • claim number, loss date, report date, paid, case reserve, incurred
• Transactions
  • premium, commission, brokerage, loss, reinstatement premium, adjustments

Create reference tables for:

• code mappings
• reason codes
• transaction types
• status values
• currency exchange rates
• treaty term definitions

4) Preserve source-to-target traceability

Validation depends on traceability. For every record, keep:

• source system
• source file name / batch ID
• record number
• load timestamp
• source extract timestamp
• transaction ID from source
• transformation version
• reconciliation status

This lets you drill from a mismatch in the warehouse back to the exact source record.

5) Create control totals and reconciliation rules

Build controls at multiple levels.

File-level controls

• expected vs received file count
• hash totals
• record counts
• total premium/loss amounts
• min/max transaction dates
• duplicate file detection

Record-level controls

• required fields populated
• referential integrity checks
• valid code values
• numeric range checks
• date logic checks
• duplicate transaction detection

Business-level controls

• ceded premium by treaty and period matches GL or subledger
• assumed premium received equals expected bordereau totals
• loss recoveries align with claims data and contract terms
• commission calculations match treaty rates
• limit and attachment logic applied correctly

6) Design the validation workflow

A practical validation process looks like this:

1. Ingest source feed
2. Validate schema
   • column presence, type, formatting
3. Run data quality checks
   • missing values, duplicates, referential errors
4. Map to canonical model
5. Compute control totals
6. Reconcile source vs target
7. Classify exceptions
   • hard fail, warning, informational
8. Route exceptions for review
9. Store resolution and sign-off
10. Reprocess corrected batches if needed

7) Handle ceded and assumed separately but consistently

Ceded and assumed data often have different source structures, but the warehouse should normalize them into common structures.

Ceded-specific checks

• treaty selection and allocation rules
• cession percentage
• aggregate and per-risk limits
• reinstatement premium calculations
• retentions and exclusions
• outbound file completeness

Assumed-specific checks

• bordereau completeness
• cedent/broker identifiers
• contract and layer matching
• inbound premium and loss allocations
• unknown contract mapping
• timing differences between reported and booked data

Use a shared transaction model with a direction flag:

• CEDED
• ASSUMED

8) Build exception management

Do not just identify mismatches; track them like work items.

Each exception should have:

• exception ID
• batch ID
• business rule violated
• severity
• source and target values
• root cause category
• owner
• status
• resolution notes
• resolution date

Common categories:

• missing transaction
• unmatched contract
• amount difference
• currency mismatch
• date mismatch
• duplicate
• invalid mapping
• calculation error

9) Use audit-friendly architecture

Because reinsurance data is often subject to audit and regulatory review:

• keep immutable raw data
• version transformation logic
• log every load and validation run
• store who approved corrections
• retain historical snapshots
• support reproducibility of reported results

10) Choose the right technology stack

The stack depends on volume and latency, but a common pattern is:

• Ingestion: ETL/ELT tools, APIs, SFTP, streaming if needed
• Storage: cloud data lake + relational warehouse
• Processing: SQL, Python, dbt, Spark if scale requires it
• Orchestration: Airflow, Dagster, Azure Data Factory, etc.
• Data quality: Great Expectations, Deequ, custom SQL checks
• BI / reporting: Power BI, Tableau, Looker
• Metadata / lineage: catalog tool or at least documented lineage tables

11) Suggested warehouse tables

A practical minimum set:

Reference

• dim_counterparty
• dim_contract
• dim_currency
• dim_accounting_period
• dim_transaction_type
• dim_code_mapping

Facts

• fact_ceded_transaction
• fact_assumed_transaction
• fact_claim
• fact_cash
• fact_reconciliation_control
• fact_exception

Audit / lineage

• audit_file_load
• audit_record_load
• audit_transformation
• audit_validation_run

12) Start with a narrow MVP

Don’t try to model every treaty nuance immediately. Begin with:

• one or two source systems
• one accounting period
• a small number of transaction types
• core validations:
  • counts
  • totals
  • duplicates
  • missing keys
  • source-to-target matching

Then expand to:

• treaty calculations
• claims triangles
• reserve movements
• multi-currency conversion
• broker bordereaux
• downstream GL reconciliation

13) Define success metrics

Track whether the warehouse is actually working:

• % of feeds received on time
• % of records auto-reconciled
• exception rate by source
• average time to resolution
• number of reprocessing cycles
• amount of unmatched premium/loss
• data quality score by feed

14) Practical implementation tip

A very effective pattern is:

• raw tables = exact source copy
• staging tables = cleaned and standardized
• recon tables = source-vs-target comparisons at batch/transaction level
• exception tables = actionable issues
• gold tables = validated business-ready data

This keeps validation separate from reporting logic and makes troubleshooting much easier.

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If you want, I can also provide:

1. a sample logical data model,
2. a set of validation rules for ceded/assumed feeds, or
3. a SQL table design / schema for the warehouse.