Insurance Data Analytics That Reconciles to the Ledger and Survives the Regulator
MinervaDB engineers the data platforms insurers run on: policy, premium, claims, and reinsurance data unified across legacy and modern core systems, actuarial and finance datasets that reconcile to the general ledger and the IFRS 17 sub-ledger, real-time views for fraud, pricing, and claims triage, and the lineage and access controls regulators expect. Delivered by the team that operates the Db2, Oracle, SQL Server, PostgreSQL, ClickHouse, and cloud warehouse platforms beneath them, including the mainframe estates most carriers still depend on.
Insurance Data Analytics Built on Database-Grade Engineering
Most insurers run a modern core beside a legacy one, an actuarial estate that does its own extracts, and a finance function that trusts none of them. The analytics platform is where those views have to agree.
Insurance data analytics at MinervaDB starts from a unified policy and claims record that reconciles to money: every policy, coverage, endorsement, premium transaction, claim, reserve movement, and payment across every core system and every conversion, joined to customer, product, agent, and reinsurance masters, and reconciled to the general ledger and the IFRS 17 sub-ledger per period. Pricing, loss ratios, reserving, fraud, and customer analytics are queries on that record once it exists, and a reconciliation project until it does.
The practice sits within our data engineering and data governance work and feeds our decision intelligence, MLOps, and enterprise generative AI practices for insurers. Our engineers operate the Db2 for z/OS and LUW, Oracle, SQL Server, and PostgreSQL systems behind policy administration, claims, and billing, and the ClickHouse, Snowflake, BigQuery, and Databricks platforms insurance analytics is served from, so an insurance data analytics platform from us is built by the people who also carry the operational accountability for the sources, mainframe included.
Insurance Data Analytics Services, End to End
Six workstreams, delivered as a bounded programme, embedded engineering, or 24×7 managed data operations.
Core-System Data Unification
Insurance data analytics begins with the cores: policy administration, claims, billing, and reinsurance systems on Db2, Oracle, SQL Server, and PostgreSQL, legacy and modern, unified into one policy and claims record with full history and effective dating, including the conversions where policy numbers changed and history was truncated. CDC from the mainframe where latency matters, reconciled extracts where it does not.
Actuarial & Finance Data Platforms
Insurance data analytics for the actuaries: premium, exposure, claims triangles, reserve movements, and expense allocations delivered as governed datasets that reconcile to the general ledger and the IFRS 17 or LDTI sub-ledger per period, with lineage from every reported number to the source transaction and reproducible snapshots at close.
Pricing, Underwriting & Risk Analytics
Insurance data analytics at the quote: rating factors, external data enrichment, telematics and IoT streams, and portfolio exposure served into pricing and underwriting decisions inside the quote window; models governed through our MLOps consulting practice so rate changes are reproducible and defensible to the regulator.
Claims Analytics, Fraud & Triage
Insurance data analytics for claims: intake streams scored in real time for fraud, severity, and complexity with human-review routing, litigation and subrogation propensity, and leakage analytics, all traceable to the claim record and the decision that was taken. Delivered with our decision intelligence practice.
Regulatory, Privacy & Model Risk Controls
Lineage for Solvency II, IFRS 17, and statutory reporting; column-level classification and consent for health and financial attributes; row-level security by entity and jurisdiction; model inventory and audit evidence for model-risk management; controls mapped to GDPR, HIPAA where health data is present, and local insurance regulation. Delivered with our data governance consulting practice.
Platform Engineering & Managed Operations
Lakehouse core, warehouse and real-time tiers, semantic layer, and FinOps for the whole estate, then 24×7 operations under SLOs, with mainframe extraction windows, period-close freezes, and regulatory calendars built into the operating model.
How Insurance Data Analytics Unifies Legacy and Modern Cores
The hardest data in insurance is not the newest. It is the policy written twenty years ago on a system that has been converted twice and still pays claims.
History survives conversions. In our insurance data analytics platforms every core migration leaves crosswalks between old and new policy and claim identifiers, and most analytics estates lose them. We maintain the crosswalks as master data with effective dating, so a loss triangle spanning three core systems is one query and the regulator's lookback request is answered from the platform.
Money reconciles every period. Premium written, earned, and collected, claims paid and reserved, and reinsurance recoveries are reconciled to the general ledger and the IFRS 17 sub-ledger per entity per period, with variance outside tolerance treated as an incident.
The mainframe is a source, not an obstacle. Db2 for z/OS behind policy and claims administration is read through CDC or scheduled unloads engineered around batch windows and MIPS budgets, by a team that operates Db2 in production.
- Policy, claim, and customer identifiers crosswalked across every conversion, effective-dated
- Coverage, endorsement, and reserve history kept at transaction grain, never summarised on load
- Premium and claims reconciled to GL and sub-ledger per entity per period, tolerances agreed with finance
- Reinsurance treaties and cessions modelled so net and gross views come from one fact
- Mainframe extraction sized against batch windows and MIPS, with replay and reconciliation
- Close-period snapshots locked and reproducible for statutory and regulatory reporting
The Insurance Data Analytics Platform We Engineer
A lakehouse core with the unified record, warehouse and real-time serving tiers, and a control plane that knows the period-close and regulatory calendars.
Db2 for z/OS and the Mainframe Estate
Insurance data analytics has to include the mainframe. Most carriers still administer a large share of in-force business on Db2 for z/OS, and analytics has to read it without disturbing nightly batch or blowing the MIPS budget. We engineer log-based CDC where latency matters and scheduled unloads where it does not, both reconciled to source, and we operate Db2 through our Db2 consulting practice so extraction is designed by people who own the subsystem.
ClickHouse for Fraud, Triage, and Pricing Views
Insurance data analytics has a real-time side. Claim intake, quote, and telematics streams need scoring and dashboards in seconds, at high concurrency, on top of years of history. ClickHouse serves them from the same unified record, with sort keys following policy and claim identifiers and materialized views precomputing the aggregates fraud and claims teams read. Delivered through our ClickHouse partner practice at ChistaDATA.
Warehouses and Lakehouse for Actuarial, Finance, and Regulatory Work
Snowflake or BigQuery serve actuarial, finance, and regulatory reporting with reproducible close snapshots; Databricks or Spark on Iceberg serve pricing, reserving, and claims science; one lakehouse core holds the unified record so all of them read the same history. Platform choice is scored on measured workload and operated through our cloud FinOps practice.
Where Insurance Data Analytics Pays Back First
Sequenced by what each one needs from the platform, because a fraud model on an unreconciled claims feed produces confident, wrong alerts.
| Use case | What it needs from the platform | How we measure it |
|---|---|---|
| Loss ratio and portfolio performance | Unified premium and claims record, reinsurance modelled, reconciled to the ledger per period | Loss and combined ratios by product, segment, and channel that finance and underwriting agree on |
| Reserving and IFRS 17 reporting | Claims triangles at transaction grain, reserve movements, cohorts and contract groups, reproducible close snapshots | Close cycle time, restatements avoided, lineage answered from the platform in audit |
| Pricing and underwriting decisions | Rating factors, exposure, external enrichment, governed models served inside the quote window | Quote latency, conversion and retention by segment, rate adequacy against actual loss experience |
| Claims fraud and triage | Real-time claim intake, history on the unified record, scored with human-review routing | Fraud detected per referral, false-positive rate, cycle time for straightforward claims |
| Customer 360 and retention | Resolved customer identity across policies and channels with consent, engagement and service history | Retention by segment, cross-holding, complaint and service outcomes tied to the customer record |
| Regulatory and conduct reporting | Lineage, classification, close snapshots, model inventory and evidence | Time to answer a regulator's data request, findings per examination |
Insurance Data Analytics Operated to the Standard of the Close
A reserving dataset that quietly diverged from the ledger is a restatement waiting to happen. We run insurance data platforms with the operating discipline of our 24×7 Remote DBA practice and the calendar discipline of a finance function.
Under managed insurance data analytics operations, every dataset carries SLOs on freshness (age of the newest policy, claim, or premium transaction in each tier), completeness (transactions reconciled to source systems and to the ledger per entity per period), and lineage integrity (every reported number traceable to its source transactions at the time of reporting). Mainframe extraction windows and period-close freezes are built into the change calendar.
Incidents follow the same severity model as our database support: a claims intake feed stalling or a fraud-scoring path failing is an S1 with a 15-minute response target; a late actuarial load ahead of close is an S2. Every incident closes with a root-cause analysis. Quarterly, we rehearse what regulators and auditors ask for: a lookback across a core conversion, a lineage walk from a reported reserve to its transactions, a subject access request, and a deletion across every copy.
- 24×7 monitoring of CDC lag, mainframe unload completion, schema drift, and reconciliation variance
- Ledger and sub-ledger reconciliation per entity per period with tolerances agreed with finance
- Close-period snapshots locked, versioned, and reproducible for statutory reporting
- Model inventory, monitoring, and evidence maintained for model-risk management
- Access recertification and audit-evidence packs generated from platform logs
- Runbooks with verification before and validation after every pipeline, model, or crosswalk change
Why Insurers Choose MinervaDB for Insurance Data Analytics
We operate the core databases, mainframe included
Most insurance data analytics consulting stops at the extract. Our engineers operate Db2 for z/OS and LUW, Oracle, SQL Server, and PostgreSQL behind policy, claims, and billing systems and the ClickHouse, Snowflake, BigQuery, and Databricks platforms the analytics is served from, so there is one accountable team from policy transaction to regulatory return.
Vendor-neutral, measurement-driven
We sell no platform licences and earn no referral fees. Every recommendation names the reconciliation, workload measurement, or regulation that justifies it, and we will say when the estate you already own is the right answer.
Close-calendar posture from day one
Pipelines, crosswalks, and models are treated as production systems with a finance calendar. Changes are staged and reversible, freezes protect the close, and every procedure states its blast radius and rollback path before it runs.
Knowledge transfer by default
The unified record, crosswalks, reconciliation logic, model inventory, and runbooks are documented and handed over. Your team should be able to run what we build; if they choose to have us keep running it, that is a decision, not a dependency.
"An insurer's data is only as trustworthy as its oldest policy. If the platform cannot follow a claim back through two core conversions to the money, it is not an analytics platform yet."
— The MinervaDB Data Engineering TeamHow an Insurance Data Analytics Engagement Runs
Discover
Inventory of core, billing, reinsurance, actuarial, and finance systems; conversion history and lost crosswalks; measured extraction windows and volumes; reconciliation gaps; regulatory and reporting calendar.
Design
Reference architecture, unified record and crosswalk design, reconciliation framework, semantic layer, platform scorecard, cost model, and a staged plan starting with the dataset finance trusts least.
Build & Validate
Extraction, unified record, reconciliation, semantic layer, and serving tiers delivered as code; reconciled to ledger for a closed period; first use case live; evidence and runbooks before cut-over.
Operate & Improve
SLO-governed operations across the close and regulatory calendar, model governance through MLOps, quarterly rehearsals, and knowledge transfer until your team owns the platform.
Insurance Data Analytics: Frequently Asked Questions
What does MinervaDB's insurance data analytics service include?
Core-system data unification across legacy and modern policy, claims, billing, and reinsurance systems; actuarial and finance data platforms reconciled to the ledger and IFRS 17 sub-ledger; pricing, underwriting, and risk analytics; claims analytics, fraud, and triage; regulatory, privacy, and model-risk controls; and platform engineering with 24×7 managed operations.
Can you work with mainframe policy and claims systems?
Yes. Insurance data analytics without the mainframe is incomplete, and our Db2 practice operates Db2 for z/OS and LUW in production, and insurance data analytics from us treats the mainframe as a first-class source: log-based CDC where latency matters, scheduled unloads sized against batch windows and MIPS where it does not, both reconciled to source, and crosswalks maintained across every core conversion so history is not lost.
How do you make actuarial and finance numbers agree?
Insurance data analytics from us is reconciled by design. Premium, claims, reserves, and reinsurance are held at transaction grain on one unified record and reconciled to the general ledger and the IFRS 17 or LDTI sub-ledger per entity per period, with tolerances agreed with finance. Actuarial datasets are derived from that reconciled record with lineage, so a reserve in the accounts and a triangle in the actuarial model are built from the same transactions.
How are health and financial attributes protected?
Attributes are classified at column level; health and sensitive financial data are tokenised or de-identified for analytics with keys held separately; consent and purpose limitations travel with the customer record and are enforced at retrieval; row-level security separates entities and jurisdictions; and every access to identifiable data is logged and recertified. Controls are mapped to GDPR, HIPAA where applicable, and local insurance regulation.
Which platforms do you recommend for insurance data analytics?
Whichever fits the measured workload: Snowflake or BigQuery for actuarial, finance, and regulatory reporting; Databricks or Spark on Iceberg for pricing and claims science; ClickHouse for fraud, triage, and pricing views in real time; Apache Iceberg or Delta Lake as the lakehouse core; Kafka and Debezium with mainframe CDC for movement. We are vendor-neutral and often recommend keeping the platform an insurer already owns.
How does insurance data analytics relate to your AI services?
The reconciled, unified record is what MLOps for pricing and fraud models, decision intelligence for claims triage and underwriting, and generative AI assistants over policy documents and claims files all depend on. Model-risk management expects that lineage, which is why we build the foundation before the model.
Let's Build a Data Platform Your Actuaries, Finance, and Regulator All Trust
Talk to a MinervaDB principal consultant about the core conversion, the reconciliation gap, or the claims analytics platform in front of you. The first conversation is always with an engineer, never a salesperson.
Schedule a Consultation → Download the MinervaDB Corporate Flyer (PDF) →Insurance and Financial Services Data Resources from MinervaDB
- Db2 Consulting, Support, and Managed Services
- Data Engineering and Analytics in Banking and FinTech
- Data Governance Consulting
- Data Engineering Consulting and Managed Pipelines
- Oracle Database Consulting
- SQL Server Support
- Decision Intelligence Consulting
- MLOps Consulting
- IFRS 17 Insurance Contracts
- Solvency II (Wikipedia)