“A straightforward claim still takes weeks and five hand-offs.”
SCIKIQOne governed FNOL-to-payout flow; an agent fast-tracks clean claims to settlement.
Industry · Insurance
One live view of risk, claims and the policyholder
Unify policy admin, claims, underwriting and CRM into one governed view — then explain why the loss ratio, cycle time or persistency moved, trace it to the claim and the rule, and act before it hits the book.
Your systems
Policy adminClaimsUnderwritingCRMActuarialDocuments
SCIKIQ
one governed data fabric360GraphCopilotAgents
Outcomes
Faster claimsLower loss ratioHigher retentionLive in weeks
01A day in the role
What the underwriting and claims desk sees
on a Monday morning
One governed brief instead of nine dashboards — every number traced to its source system, every recommendation showing its reasoning.
Good morning, NehaMonday · 8:30 AM
Book health87▲ 3AI confidence93%Agents working6liveDecisions need you2
Today’s brief
The combined ratio improved 1.6 points on stable new business.
Claims cycle time fell after policy, claims and risk data were resolved into one view, and persistency held. A cluster of claims in one region shows an aligned fraud pattern.
If only one decision is made today, reviewing that claims cluster is expected to prevent the most leakage.
Confidence93%
Book health
- Loss ratio− 1.6ptImproving
- Claims cycle time− 3.2 daysOne view
- Fraud signal+1 clusterOne region
- Persistency+0.4ptHolding
- New business+7.2%Underwriting faster
- Reserve adequacy+1.1ptComfortable
What changed
- Fraud cluster 34 claimsProvider, claimant and timing signals aligned
- Claims cycle time − 3.2 daysPolicy and claims resolve to one record
- Straight-through processing +14ptCleaner data at first notification
- Reserve volatility − 21%Consistent definitions across the book
- Renewal offers accepted +5ptRetention targeted on governed segments
- Duplicate policyholders − 22,800Entity resolution across product lines
Decisions
Review the flagged claims cluster
Prevents$1.1M leakage
Confidence94%
WindowWithin 5 days
Launch retention on the lapse-risk cohort
Protects$640K premium
Confidence86%
WindowThis month
AI reasoning — why this cluster?
- Thirty-four claims share provider, claimant and timing patterns
- The pattern matches three previously confirmed fraud rings
- Assessor capacity is free before the next settlement run
- 17 comparable reviews recovered 64% of flagged leakage
Recommendation Review today.
AI has been working
- Resolved 22,800 duplicate policyholders
- Scored the claims book for fraud patterns
- Reconciled policy, claims and reserve data
- Modelled persistency across the renewal cohort
- Traced every reserve figure to its source
- Generated the underwriting briefing
WhyA cluster of 34 claims shares an established fraud pattern
Why nowReview must land before the next settlement run
Evidence17 comparable reviews recovered 64% of leakage
Confidence94% · based on 6 years of claims history
AlternativeSettle and recover later — models 22% recovery
Ask me anything — or tell me the outcome you’re trying to achieve
Illustrative insurer view · sample data
02Where it hurts
The questions underwriting and claims teams can't answer fast
Each is a question that today means days of manual work across policy admin, claims and documents. SCIKIQ answers it from governed data — with lineage an auditor will accept — then acts on it.
“We pay the fraudulent claim first and investigate it later.”
SCIKIQFraud and linked networks surface at FNOL; an agent assembles the SIU case.
“Good applicants wait days while we chase medical evidence.”
SCIKIQEvidence is fused at intake; an agent straight-through underwrites clean cases.
“Policies lapse quietly and we see it only after the grace period.”
SCIKIQLapse signals surface early; a win-back agent makes the save offer.
“We can't see which agents and products actually retain business.”
SCIKIQOne view of agent, product and persistency; the next-best action reaches the field.
“Manual servicing and re-keying keep the expense ratio too high.”
SCIKIQPolicy servicing automated on governed data, as a measurable data product.
Three kinds of data, one governed graph — SCIKIQ fuses live signals, systems of record, and the documents in between.
Real-time signals
live signals & telemetry
Telematics & usageWearables & healthConnected-home IoTPayment & billing eventsDigital session data
Structured
systems of record
Policy adminClaims systemBillingCRMActuarial & reserving
Unstructured
text, images, audio
Claim documentsMedical reportsAdjuster notesCall transcriptsUnderwriting files
One governed 360 & knowledge graphfused, contextualised and AI-ready
03The four layers
Four layers. Each answers a harder question.
Enterprise 360 tells you what happened. The knowledge graph tells you why. The copilot explains it in plain language. The agent factory does something about it — on your insurance data.
01
Layer 1 · Enterprise 360
What is happening?
Unify policyholder, policy, claims and risk data across policy admin, claims and CRM into one real-time view — so underwriting, claims and actuarial share the same numbers.
OutcomeLoss ratio & claims
02
Layer 2 · Knowledge Graph
Why is it happening?
Trace the relationships between policyholders, policies, claims and risk to find why the loss ratio or persistency moved — and prove exactly where every fact came from.
OutcomeRoot cause & risk
03
Layer 3 · AI Copilot
Tell me, in plain language
Let an underwriter, claims lead or actuary ask in plain language — answered from governed data with the lineage an auditor will accept.
OutcomeCompetitive advantage
04
Layer 4 · Agent Factory
Don’t just tell me — fix it
Turn answers into action. Agents triage new claims, detect fraud networks, straight-through underwrite clean applications and win back lapsing policyholders — every step logged and auditable.
OutcomeLoss & leakage protection
04The outcome
What insurers get
One governed view of risk, claims and the policyholder.
FasterClaims cycle time — weeks to days
LowerLoss ratio and fraud leakage
HigherPersistency and policyholder lifetime value
WeeksTo a live insurance 360
04In the field · Voice of the Customer
Turning every call and survey into signal
A Fortune 500 insurance & risk-management leader runs a GenAI Voice-of-Customer and survey-intelligence programme on SCIKIQ — listening to every customer call, unifying survey feedback across markets and languages, and triaging complaints under regulatory scrutiny. Client and program names withheld.
Every call, every signal
An AI pipeline listens to every customer call and extracts the signal: multi-dimensional intent (category, sub-category and reason), real-time sentiment with emotional context, four-level complaint detection and escalation (L0–L3), automated agent quality scoring from intro to resolution to close, plus claims detection and call summarisation. Audio transcripts flow through a 10-phase PySpark and Delta Lake ETL to a GPT-4-class model, into the enterprise warehouse and executive dashboards — daily batch with near-real-time triggers for critical L0/L1 escalation.
EveryCall scored, not a sample
3-levelIntent — category, sub-category, reason
L0–L3Complaint detection & escalation
Intro→CloseAutomated agent quality scoring
One view across five LATAM markets
Survey feedback across Argentina, Brazil, Chile, Colombia and Mexico is unified into one view. Models tuned for regional Spanish variants and Brazilian Portuguese recognise local idiom and cultural context, with automatic language detection, regional sentiment, theme extraction and trend detection — and PII detection and masking built in for compliance.
5Markets unified in one view
2Languages — regional ES & BR-PT
AutoLanguage detection & regional sentiment
PIIDetection & masking for compliance
4,000+ complaints a day, triaged same-day
Under regulatory scrutiny, an NLP engine triages more than 4,000 complaints a day: frustration-intensity detection, automated in- and out-of-scope classification, impact-based prioritisation (L0–L3) and same-day escalation for critical issues — with a human-in-the-loop interface to validate every decision before it moves.
4,000+Complaints triaged daily
L0–L3Impact-based prioritisation
Same-dayEscalation for critical issues
Human-in-loopValidation interface on every call
One pipeline for every survey source
A single platform-agnostic pipeline ingests verbatims from many sources — survey platforms, CX platforms, IVR, web and chat — across multiple insurance programs. Three-tier tagging combines an enterprise taxonomy, program-specific tags and AI-discovered emergent themes, with Day-1 processing for every new survey and a completed 13-month historical backfill for one unified source of truth.
5+Sources — survey, CX, IVR, web, chat
3-tierTaxonomy, program & emergent themes
Day-1Processing for every new survey
13-monthHistorical backfill, unified
Azure-native — Azure Blob, Data Factory and Databricks (Delta Lake, PySpark), OpenAI, Presidio for PII, an enterprise data warehouse and Power BI — deployed across greenfield and brownfield environments.
4,000+Complaints triaged daily
5 · 2Markets and languages unified
L0–L3Automated complaint escalation
13-monthHistorical backfill completed
Illustrative of a real SCIKIQ engagement with a Fortune 500 insurance & risk-management leader; client and program names withheld.
04Skill your team
The engineers who deploy this
SCIKIQ Certified Data and AI Engineer runs a specialisation for this sector — the same value chain, data landscape and constraints as this page, taught as hands-on labs with timed assessments and two end-to-end capstones.
Which policies will lapse on you this quarter?
We would love to think through it with you — no pitch, no form maze.
Frequently asked questions
How does SCIKIQ improve loss-ratio visibility?
It resolves policy, claims and risk data into one governed knowledge graph, so loss ratio is calculated from a single agreed definition and can be traced back to the underlying policies and claims.
Can SCIKIQ help detect claims fraud?
Agents run continuously against the governed layer to flag anomalous claims patterns, with lineage showing which records triggered the signal so an assessor can review the reasoning.
Does it support underwriting and retention decisions?
Yes. Because policy, claims and customer data resolve to one record, underwriting and retention teams query the same governed definitions rather than reconciling separate extracts.