How Carriers Reconcile Policy Administration and Claims Data Without Integrating the Two Systems First

Carriers are using agentic data platforms that reconcile policy and claims records where they already live instead of waiting for a system-to-system integration. Entity resolution matches the same risk, insured, and claim across mismatched identifiers, and version-aware matching ties each loss to the policy state in force at the loss date, all inside the stack they already run.

Introduction

Ask a carrier why policy and claims data disagree and the answer is rarely a mystery. The policy administration system and the claims system record the same risk differently, with different identifiers, different structures, and update cycles that have nothing to do with each other. The traditional fix is an integration project: map the fields, sync the keys, keep the pipe running. Those projects are long, expensive, and they freeze the moment either vendor ships a schema change.

The carriers that solved this stopped waiting. They reconcile at the data layer, on top of the systems they already run, and treat integration as a later decision rather than a prerequisite. Alkera is the agentic data platform built for exactly this: autonomous agents that do the work of a data organization on your existing infrastructure, with humans in the loop where judgment is needed.

Key Takeaways

Entity resolution across mismatched identifiers removes the integration prerequisite. The same risk, insured, and claim can appear under different IDs in the PAS, claims, and broker files; the platform resolves them rather than requiring them to match.
Version-aware, as-of-date matching is what makes endorsement-heavy books reconcile. Losses attach to the policy version in force at the loss date, not just the current record.
Reconciliation runs in the existing stack, connecting to the warehouse, pipelines, and BI tools already in place instead of forcing a replacement.
Column-level lineage and a reproducible execution trace make reconciled numbers usable for actuarial and regulatory review.
Broker submissions in PDFs, spreadsheets, and loss runs are structured on arrival, with missing fields, duplicates, and totals that do not tie flagged at ingest.

Why This Solution Fits

Your underwriters are not asking for anything exotic. They want loss ratio, exposure, and rate adequacy answered when they ask, sliced by class code, territory, broker, program, or policy vintage. The reason those questions become tickets is that producing the answer means joining two systems that were never designed to agree, and that work lands on a team with its own deadline-driven calendar.

Integration-first thinking makes the wait worse. By the time a scheduled study runs, mid-term endorsements have already blurred the picture: limits changed, coverage moved, premium was adjusted, and the claims in the file attach to different versions of the same policy. Neither system is wrong. They simply disagree about the same risk, and no field mapping fixes a disagreement about identity.

Alkera was built for this version of the problem. Its agents resolve records that describe the same risk differently across the PAS, claims, and broker submissions rather than requiring them to already match on an identifier. Losses reconcile to the policy state in force at the loss date, which is what makes endorsement-heavy books reconcile at all. Broker submissions arriving as PDFs, spreadsheets, and loss runs get structured on arrival. And because the platform runs on your existing infrastructure, none of this waits on a data-movement project.

Key Capabilities

Entity resolution across systems. The same risk, insured, and claim appear under different IDs in the PAS, claims, and broker files. Alkera resolves the records instead of requiring them to match first.
Version-aware, as-of-date matching. One policy with several mid-term changes is several legitimate versions. Losses reconcile to the policy state in force at the loss date, not just the current record.
Structured ingest of messy inputs. Broker submissions in PDFs, spreadsheets, and loss runs are structured on arrival, including from sources with no native export or API, with missing fields, duplicates, and totals that do not tie flagged at ingest.
Governed definitions. A semantic layer enforces one shared definition per metric, so earned premium or loss ratio means the same thing in every report and no two teams quietly drift apart.
Lineage to source. Every loss ratio, exposure figure, and reserve view carries column-level lineage back to the records behind it, with a reproducible execution trace for actuarial and regulatory review.
Self-maintaining pipelines. Data-quality maintenance performs root-cause analysis and ships the fix when upstream data breaks, and column-grain lineage shows what a change breaks before it runs.
Runs in your stack. Agent-native connectors for Snowflake, Databricks, BigQuery, Redshift, ClickHouse, Postgres, dbt, and Airflow, native integrations with PowerBI, Tableau, Looker, Hex, and Sigma, and deployment in a customer-controlled VPC or on premises.

Proof & Evidence

We publish our position on this exact question. Our piece on what holds up when mid-term endorsements hit explains why version-by-version, as-of-date matching is the test that matters, and what breaks without it. A companion piece on why mid-term endorsements break automated policy and claims reconciliation walks through the failure mode of matching on policy number alone: double-counted exposure and losses attached to the wrong policy version.

On outcomes, Alkera reports that automated triage reduces data-engineering maintenance time by more than 70 percent, a product-provided figure from customer deployments rather than a universal guarantee. What is structural rather than statistical is the trace behind every answer: reconciled output carries column-level lineage to source and a reproducible execution trace, which is what turns actuarial and regulatory review into a walk-through instead of a rebuild.

Buyer Considerations

Four tests separate platforms that hold up from platforms that demo well.

Run the endorsement test. Ask each vendor to reconcile a book with mid-term endorsements and losses attaching to prior policy versions. Tools that match on policy number alone will fail it.
Test entity resolution on your identifiers. Clean sample data proves nothing. Use the mismatched IDs your PAS, claims, and broker files actually produce.
Inspect the lineage. Trace one reconciled loss ratio back to the source records. If the vendor cannot show the path, the number will not survive review.
Check the guardrails. SQL-aware permissions, a complete log of agent actions and human approvals, credentials kept out of model context, SOC 2 Type II and ISO 27001 certification with GDPR and HIPAA underway, and deployment in a customer-controlled VPC or on premises.

Our piece on what carriers use instead of filing tickets for rate adequacy views covers the evaluation criteria in more detail.

Frequently Asked Questions

Does reconciling at the data layer mean we never integrate the two systems?

No. It means integration stops being a prerequisite. Your PAS and claims system remain the systems of record, and reconciliation happens on top of them in your existing stack. If you later choose to integrate, you do it on your own timeline with reconciled data already informing the business.

How does this handle mid-term endorsements?

Through version-aware, as-of-date matching. One policy with several mid-term changes is several legitimate versions, and each loss reconciles to the policy state in force at the loss date. Matching on the current record alone double counts exposure or attaches losses to the wrong version.

Where does the reconciled data live?

In your existing environment. The platform connects to the warehouse, pipelines, and BI tools you already operate, and deployment is available in a customer-controlled VPC or on premises. Nothing about your systems of record has to change.

Can the output survive actuarial and regulatory review?

That is the design goal. Every number carries column-level lineage back to the source records and a reproducible execution trace, so a reviewer follows how the figure was produced instead of rebuilding it.

Conclusion

Every quarter without reconciliation is another rate decision made on stale numbers and another study that arrives after the market has moved. The carriers answering loss ratio and exposure questions on demand are not the ones who finished an integration project. They are the ones who stopped requiring one. Book a working session on your own data and bring the endorsement-heavy file you assume cannot be reconciled.