01
Underwriting At Quote Speed
Enrich a submission with third-party, geospatial, and historical loss data inside the quote window — so risk selection happens before binding, not at renewal.
Solutions · InsuranceGENEDATA / 01
A complete pictureof every risk.
Connect policy, claims, and actuarial data so underwriting, reserving, and reporting work from the same record.
Shared context · Lineage · Governance
Connected
Your sources
Policy records
Claims history
Exposure data
Connected intelligenceInsurance intelligence
Governance
Business impactUnderwriting & reserving
Shared contextLineageGovernance
02
Claims & Fraud
Score every claim at first notice of loss against network, behavioural, and historical signals — routing suspicious claims to SIU while fast-tracking the clean majority.
03
IFRS 17 & Solvency II
Cohort-level measurement, CSM roll-forward, and capital reporting computed from the same policy and claims record the business runs on.
Built On
The products behind this solution.
Underwriting, claims, and reserving each lean on a different product, which is precisely why they disagree today when those products are separate systems.
GeneFlow
Ingests policy administration, claims, telematics, and third-party enrichment, landing one policy and loss record rather than four extracts.
Learn moreGeneCatalog
Classifies PII and claims data on arrival and holds the lineage IFRS 17 disclosures and subject requests depend on.
Learn moreCortex SQL
Runs reserving, IFRS 17 measurement, exposure accumulation, and the reporting bases off that shared record.
Learn moreCortex AI
Scores fraud at first notice of loss, prices risk at quote time, and predicts lapse while a renewal is still winnable.
Learn moreIndustry use-case map
Insurance & Actuarial workflows across one governed operating model.
These reference workflows connect the business decision to the data, controls, platform surfaces, and people required to operate it in production.
Insurance & ActuarialReference workflow
Risk pricing and underwriting
Enrich a submission with current loss, exposure, geospatial, and third-party context while the quote is still open, then preserve the exact basis of the decision.
How the work moves
- 01
Connect the signal
Policy and claims systems · Telematics and geospatial data · Third-party risk and catastrophe data
- 02
Apply control
Data quality contracts · End-to-end lineage · Human approval
- 03
Build and deliver
GeneFlow Engineering · Data Science & MLOps · Operational Database
- 04
Decide and act
Faster quote decisions with reproducible risk selection and pricing evidence.
Participating roles
Platform surfaces
Business outcome
Faster quote decisions with reproducible risk selection and pricing evidence.
Explore the full industry solutionThe Challenge
The loss ratio depends on who you ask.
Underwriting prices off one view of historical loss, claims operates on another, and actuarial reserves against a third — reconciled quarterly, by hand, into a number that satisfies the regulator but informs nobody's next decision.
Insurance data is unusually long-lived. A policy written today generates claims for years and development triangles for a decade, so a change to how loss is coded in 2026 quietly invalidates comparisons back to 2015. Most carriers discover this during an IFRS 17 transition rather than before it.
Meanwhile the pricing signal that matters most — actual claims experience on business written under current guidelines — reaches the underwriting model slowest, because it travels through the reconciliation cycle rather than directly. Carriers routinely price a book on experience that is eighteen months stale.
Putting policy, claims, and exposure on one substrate means the reserving run, the pricing model, and the regulatory return read the same records. Agreement stops being something produced at quarter-end and becomes a property of the data.
- Experience-to-pricing lag
- 12–18mo
- Through the reconciliation cycle
- Typical claims leakage
- 5–9%
- Overpayment against entitlement
- Development triangle depth
- 10+ yrs
- Long-tail lines
- Reporting bases in parallel
- 4
- IFRS 17, Solvency II, NAIC, management
Reference Architecture
One policy and loss record, read by every function.
Policy administration, claims, telematics, and third-party enrichment land on a shared substrate. Underwriting, actuarial, and claims operations each read committed state rather than a periodic extract taken on a different day for a different purpose.
The exposure model sits alongside ingestion rather than downstream of it, because accumulation has to be answerable before binding — not reconstructed after a catastrophe event.
Portfolio Flow
Where the book's premium and loss actually sit.
A representative multi-line carrier. The same committed records drive pricing, reserving, and the regulatory return — which is the point: none of these functions is working from its own extract.
| Source | Target | Value |
|---|---|---|
| Motor | Pricing + selection | 520 |
| Motor | Reserving + capital | 460 |
| Motor | Regulatory reporting | 260 |
| Property | Pricing + selection | 360 |
| Property | Reserving + capital | 320 |
| Property | Regulatory reporting | 180 |
| Liability | Pricing + selection | 200 |
| Liability | Reserving + capital | 210 |
| Liability | Regulatory reporting | 130 |
| Specialty | Pricing + selection | 100 |
| Specialty | Reserving + capital | 100 |
| Specialty | Regulatory reporting | 110 |
Claims Lifecycle
Who touches a claim, and when.
A claim passes through four functions between first notice and closure. Historically each handoff meant re-keying into another system; here each function annotates the same record, so the reserve an actuary sees reflects the adjuster's latest assessment rather than last night's extract.
The fraud lane runs in parallel with adjusting rather than after it. Scoring at triage is what allows the clean majority to be fast-tracked while suspicious claims are held before payment rather than recovered afterwards.
Use Cases In Depth
Five problems, in the detail they actually have.
These are the workloads that justify a platform decision for a carrier. Each covers the situation, what the platform does about it, the architecture involved, and what changes measurably.
Risk selection inside the quote window, not at renewal
A submission arrives with the minimum the broker had to hand. Enriching it with prior loss history, geospatial peril data, and credit or vehicle data usually takes longer than the quote turnaround allows, so the price goes out on thin information. The risk is properly understood a year later, at renewal, after the claims have happened.
What the platform does- Third-party, geospatial, and internal loss data are pre-joined against the exposure model, so enrichment at quote time is a lookup rather than a series of external calls.
- Pricing models read the same in-force and claims record actuarial reserves against, removing the gap between the priced loss ratio and the reserved one.
- Declination and referral rules evaluate on enriched attributes, so marginal risks route to a referral queue instead of being bound at a standard rate.
- Every quote retains the exact feature values used, so a disputed price or a regulatory challenge can be reconstructed rather than argued.
Architecture · Data Management (third-party enrichment, catalogued on write) + Data Science (pricing models, feature store) + Database (sub-second lookup at quote time).
| Category | Value | Note |
|---|---|---|
| Prior loss history | 34% | |
| Geospatial peril | 22% | |
| Telematics score | 19% | |
| Vehicle / property attr. | 14% | |
| Credit-based score | 11% |
- <800ms
- Enrichment at quote
- At bind
- Risk selection point
- Reproducible
- Quote-time features
- 1
- Loss view, priced and reserved
Fraud caught before payment, not recovered after it
Most fraud detection runs on a sample of claims after settlement, which means recovery rather than prevention — and recovery rates on paid fraudulent claims are poor. Meanwhile the clean majority of claims sit in the same queue as the suspicious minority, so honest customers wait while adjusters work through everything at the same pace.
What the platform does- Every claim is scored at first notice of loss against network, behavioural, and historical signals rather than a post-settlement sample.
- Claims below the risk threshold fast-track to automated settlement, which is what frees adjuster capacity for the claims that need judgement.
- Suspicious claims route to SIU before payment authorisation, with the contributing signals attached rather than referenced.
- Confirmed outcomes feed back into the model, so ring behaviour that shifts tactics is picked up in weeks rather than at the next annual model refresh.
Architecture · Artificial Intelligence (scoring at FNOL, network analysis) + Data Engineering (streaming claim capture) + Governance (decision audit for every referral).
| Stage | Volume | % of entry | Note |
|---|---|---|---|
| FNOL received | 82.4k | 100% | Monthly volume |
| Scored at intake | 82.4k | 100% | 100% — no sampling |
| Fast-tracked clean | 51.1k | 62% | 62% auto-settled |
| Adjuster review | 27.9k | 34% | 34% needing judgement |
| Held for SIU | 3,400 | 4% | 4.1% referred pre-payment |
- At FNOL
- Scoring point
- −38%
- Fraud paid out
- 62%
- Claims fast-tracked
- Attached
- Evidence per referral
IFRS 17 measurement that ties back to the policy record
IFRS 17 requires measurement at cohort level with a contractual service margin rolled forward each period. Carriers typically assemble this in a separate actuarial environment fed by extracts, which means the CSM roll-forward cannot be traced to individual contracts without a manual exercise — precisely what auditors ask for.
What the platform does- Cohorts are derived from the policy record itself, so a contract's cohort assignment is a property of the contract rather than a mapping table maintained alongside it.
- The CSM roll-forward computes from committed policy and claims state, making each movement traceable to the contracts that caused it.
- Actual-versus-expected variances decompose by driver, so an unfavourable movement is attributable rather than merely reported.
- Prior-period figures reproduce exactly under the assumptions in force then, which is what makes a restatement explainable.
Architecture · Data Warehousing (full policy history, time travel) + Governance (versioned assumptions, immutable audit) + Business Intelligence (regulator-grade disclosure output).
| Step | Change | Running total |
|---|---|---|
| Opening CSM | 420m | 420m |
| New business | +86m | 86m |
| Interest accretion | +14m | 100m |
| Release to P&L | -68m | 32m |
| Experience var. | -22m | 10m |
| Closing CSM | 430m | 430m |
- Contract-level
- CSM traceability
- Point-in-time
- Prior-period reproduction
- By driver
- Variance decomposition
- −72%
- Reserving close effort
Accumulation answerable before binding, not after the event
Catastrophe accumulation is often recalculated overnight or weekly, so an underwriter binding a risk in a concentrated postcode cannot see the marginal impact on the portfolio at the moment of the decision. The true accumulation position becomes clear when an event happens and the reinsurance recovery does not cover what everyone assumed it would.
What the platform does- Exposure aggregates by peril, geography, and treaty layer are maintained incrementally as policies bind rather than rebuilt on a schedule.
- Marginal impact of a prospective risk is queryable at quote time, so concentration limits bind at the point of decision.
- Reinsurance treaty structures are modelled against the same exposure record, making net position visible alongside gross rather than in a separate exercise.
- Post-event, actual losses reconcile against the modelled position, so the accumulation model is corrected by experience.
Architecture · Data Engineering (incremental aggregation) + Database (geospatial access paths) + Observability (concentration threshold alerting).
| North | Coastal | Central | South | |
|---|---|---|---|---|
| Windstorm | 40% | 100% | 60% | 80% |
| Flood | 60% | 80% | 40% | 60% |
| Wildfire | 20% | 20% | 80% | 100% |
| Hail | 80% | 40% | 100% | 60% |
| Earthquake | 20% | 60% | 20% | 40% |
- At quote
- Marginal accumulation
- Gross + net
- Position visibility
- Incremental
- Aggregate maintenance
- Post-event
- Model reconciliation
Renewal intervention while the policy is still winnable
Retention teams typically learn a policy has lapsed after it has lapsed. Renewal propensity is modelled on a quarterly refresh, so a customer whose circumstances changed in month two is contacted — if at all — after they have already shopped the market and bound elsewhere.
What the platform does- Renewal propensity scores update continuously against claims experience, service interactions, and pricing movement rather than on a quarterly cycle.
- Customers whose risk of lapse rises are surfaced to the retention team while the renewal window is still open.
- Intervention outcomes are recorded against the policy, so the retention model learns which action worked on which segment.
- Broker and agent performance reads the same record, making channel profitability comparable to direct on identical definitions.
Architecture · Data Science (propensity models, continuous scoring) + Business Intelligence (retention workbench) + Sharing (governed broker performance views).
| Continuous scoring | Quarterly refresh | |
|---|---|---|
| Q1 | 71% | 71% |
| Q2 | 74% | 71% |
| Q3 | 77% | 72% |
| Q4 | 79% | 73% |
- Continuous
- Propensity refresh
- +6.4pp
- Retention on at-risk book
- In-window
- Intervention timing
- One basis
- Channel comparison
Framework Coverage
Which functions each reporting basis actually touches.
Carriers report on several bases in parallel, and each pulls on different functions for evidence. Darker cells indicate a heavier evidentiary burden on that function for that framework.
| Underwriting | Claims | Actuarial | Finance | |
|---|---|---|---|---|
| IFRS 17 | 40% | 60% | 100% | 100% |
| Solvency II | 60% | 60% | 100% | 80% |
| NAIC / statutory | 40% | 80% | 80% | 100% |
| ORSA | 80% | 60% | 100% | 60% |
| GDPR | 80% | 100% | 20% | 40% |
Because every basis reads the same policy and loss record, adding a reporting requirement is a measurement definition rather than another extract with its own reconciliation.
Measured Change
What moves after consolidation.
Comparative figures from carrier consolidation programmes. As in banking, the largest gains are in activities that existed only to make separate systems agree with each other.
| Category | Before | After |
|---|---|---|
| Quote turnaround | 48 | 3 |
| Claims cycle | 21 | 9 |
| Reserving close | 18 | 5 |
| Pricing refresh | 90 | 14 |
Quote turnaround is measured in seconds; claims cycle, reserving close, and pricing refresh in days. Mixing units in one chart would be misleading, so treat each category as its own comparison rather than reading across them.
Adoption Path
Carriers do not consolidate the whole book at once.
The sequence below is what deployments actually follow. Each stage stands on its own, which matters for a programme that has to survive more than one planning cycle.
- 1
Policy + claims
Land the in-force book and claims history on one record. The first reconciliations disappear and loss experience becomes queryable at contract level.
- 2
Underwriting
Add enrichment and pricing at quote time. Risk selection moves to the point of binding rather than the point of renewal.
- 3
Actuarial
Point reserving and IFRS 17 measurement at the shared record. CSM traceability and point-in-time reproduction arrive together.
- 4
Exposure + capital
Accumulation and treaty modelling join. Concentration becomes answerable before binding and capital reporting reads the same state as the business.
Most carriers begin at stage one. The ladder marks stage two as the common position once a first production deployment is live.
At point of quoteQuote Enrichment
At FNOLClaims Scored
IFRS 17 cohortReserving Basis
IFRS 17 · Solvency II · NAICReporting Workflows
The next step
Pricing, claims, and capital that agree with each other.
When underwriting, claims, and actuarial read the same policy and loss record, the loss ratio in a pricing model matches the one in the reserving run and the one in the regulatory return — without a quarterly reconciliation to make them agree.
FAQ
Insurance, answered.
What actuarial, claims, and underwriting leaders ask before committing.
Can we keep our existing policy administration system?
Yes. GeneFlow ingests from the policy admin system you already run — the platform becomes the analytical and reporting substrate rather than a core system replacement, which is a far shorter path to value.
How does IFRS 17 measurement stay auditable?
Cohorts derive from the policy record itself rather than a mapping table maintained alongside it, and the CSM roll-forward computes from committed state. Each movement traces to the contracts that caused it, which is what auditors ask for.
Is fraud scoring explainable?
Yes. Every referral carries the contributing signals attached rather than referenced, and the full decision trace is retained. That matters both for SIU triage and for demonstrating the model is not making protected-characteristic inferences.
Can accumulation really be answered at quote time?
Exposure aggregates by peril, geography, and treaty layer are maintained incrementally as policies bind rather than rebuilt overnight, so the marginal impact of a prospective risk is a query rather than tomorrow's report.
How do you handle long-tail development triangles?
Full history is retained with schema versioning, so a change to how loss is coded today does not invalidate comparisons to a decade ago — you can always ask what a triangle looked like under the coding in force at the time.
Related content
Architecture
Insurance Reference Architecture
Policy and claims ingestion, exposure model, and IFRS 17 measurement.
ComplianceIFRS 17 & Solvency II
Measurement model, CSM roll-forward, and disclosure output.
GuideClaims Fraud Scoring
Signals, thresholds, and the explainability record per referral.
RelatedFinancial Services
The same substrate applied to trading, risk, and regulatory reporting.
Take the next step
Start with one line of business.
Walk a book from policy and claims through reserving and exposure with a solutions engineer.