Loss costs and catastrophe exposure climb, distribution spreads across agents, partners and embedded journeys, customers judge you on the claim, and regulators ask how every complaint, reserve and AI decision was handled. The answer runs across the whole insurer — from how a risk is priced to how a claim is paid and the ledger closes. DaasLabs is the data and AI services team that helps insurers make that shift, one value-chain domain at a time.
Insurers organised around lines and systems — a policy platform, a claims platform, a billing system, a finance ledger — now compete on decisions made across all of them. The insurers pulling ahead treat data as a product and AI as an operating capability, not a set of pilots. These are the pressures they are responding to.
Claims inflation, repair and medical costs and catastrophe losses rise faster than annual rate cycles can follow. Combined ratios are won on pricing precision and expense discipline.
Data & AI: loss-cost models on governed data, faster rate evidence and agents that take cost out of operations.
Agents, brokers, bank partners, retailers and embedded journeys each own a slice of the customer. Price comparison makes switching easy at every renewal.
Data & AI: channel and partner analytics, retention signals and next-best-action for producers.
Customers judge the insurer on how fast an approved claim is paid. Documents, estimates and hand-offs slow settlement, and fraud hides in the volume.
Data & AI: FNOL triage, document AI, fraud scoring and agents that assemble the claim file.
Calls, surveys and complaints arrive by the million, in many languages. Keyword tools miss nuance, sampling misses the complaint that matters, and regulators expect fast identification.
Data & AI: GenAI that classifies intent, sentiment and complaint severity on every interaction.
Read the case studyIFRS 17 and Solvency II reporting — and in India, IRDAI returns, the move to Ind AS 117 and risk-based solvency — plus complaint-handling and fair-treatment rules and privacy law (DPDP Act, GDPR, CCPA, GLBA) all ask for granular, traceable data, delivered faster.
Data & AI: lineage from policy and claim to the return, PII protection built in, and AI-assisted reporting.
Several policy and claims platforms from past acquisitions, and critical information locked in PDFs, emails and transcripts. Every new report, model or agent starts with another extract.
Data & AI: one governed data fabric, with GenAI turning documents and conversations into structured data.
Pricing, underwriting and claims decisions affect customers directly. Regulators expect AI to be inventoried, tested for bias, supervised and explained.
Data & AI: agent governance — autonomy levels, approvals, evaluations and a kill switch.
See agent governanceThe response isn't one platform or one model — it is data and AI applied domain by domain, from pricing to claims to the ledger, on a shared, governed foundation.
See where, domain by domain ↓Three domains that win and price the risk, two that serve the customer, two that handle the claim, and the group functions underneath them. Select a domain to see the data it runs on, the AI opportunities, and how DaasLabs adds value there.
Loss trends, inflation and catastrophe exposure move faster than annual rate reviews. Actuaries spend more time assembling data than modelling, and new products take quarters to file and launch.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Insurance is sold through agents, brokers, bank partners, retailers and embedded journeys at once. Each channel reports differently, partner programmes demand their own insight, and agent productivity is hard to see.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Submissions arrive as emails, PDFs and spreadsheets. Underwriters re-key data, chase missing information and check guidelines by hand, so good risks wait and the portfolio drifts from appetite.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Policies live on several admin systems from past acquisitions. Endorsements, renewals and cancellations run on hand-offs, and billing or document errors turn into calls and complaints.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Millions of calls, surveys and complaints hold the reasons customers stay or leave — but they are unstructured, multilingual and full of personal data. Regulators expect complaints to be found and resolved fast.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Claims is where the promise is kept or broken. First notice of loss arrives through every channel, adjusters juggle documents and repair estimates, and payment delays turn approved claims into complaints.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Organised rings, inflated claims and application fraud hide across policies, claims and third parties. Rules flag too much or too little, and investigators spend their time gathering evidence.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
IFRS 17, Solvency II and, in India, IRDAI returns and the move to Ind AS 117 demand granular, traceable data and faster closes. Reserving, reinsurance recoveries and premium and claims reconciliations still lean on spreadsheets and manual matching.
Data it runs on
Data & AI opportunities
How DaasLabs adds value
Opportunities and approaches are described qualitatively. Shaded chips are DaasLabs service lines; the others open accelerators and the case study. See every service line mapped to these eight domains
Nine service lines that advise, build, transform and run — delivered on three pieces of DaasLabs IP, so insurers start from working components rather than a blank page.
In our AI & Agentic Engineering and Managed Services work, agents do the routine work end to end — in every domain above. Policy decides what goes straight through; people approve everything else. Nothing happens off the record.
Agents read the documents, check coverage and fraud signals and draft the settlement. Anything above the authority limit, or with a fraud flag, waits for a named adjuster or SIU investigator.
Watch an agent work a caseAgents plan, call tools and gather evidence on the governed Data Fabric — the same policy, claims and customer data your teams use.
Each agent has an autonomy level. A case goes straight through only if confidence, authority limits and fraud and coverage checks pass.
Everything else lands in a named owner's queue with the agent's draft, rationale and evidence — approve, edit or reject.
Every plan, tool call, guardrail check and decision is logged; overrides feed evaluation, and a kill switch halts all agents.
Pre-built, configurable solutions on the Data Fabric Framework. Four were built for a Fortune 500 insurer; the rest are cross-industry accelerators our teams configure to insurance data, rules and controls.
GenAI accelerators from our specialty-insurer case study, built on Azure OpenAI, Databricks and Delta Lake.
Every call analysed: intent (category, sub-category, reason), sentiment with emotional context, L0–L3 complaint detection, claims detection, call summaries and five-stage agent quality scoring.
Frustration-intensity detection, in/out-of-scope classification and L0–L3 impact prioritisation, with same-day escalation for critical complaints and a human-in-the-loop validation interface.
Automatic language detection, regional prompt tuning for Spanish variants and Brazilian Portuguese, cultural context in sentiment, theme and trend extraction, and PII masking with Microsoft Presidio.
The same architecture applies to claims feedback, agent performance and regulatory complaint monitoring.
About Distribution, Customer & VoCCross-industry accelerators below are configured to insurance data in an engagement; their demo pages run on banking sample data.
Four GenAI solutions for a specialty insurer protecting 300+ million consumers (client anonymised), from our specialty-insurer case study. The figures below describe the scale of what we built and run — not measured business outcomes.
A 10-phase GenAI pipeline that classifies intent, sentiment and complaint level, detects claims, summarises every call and scores agent quality.
Surveys from Argentina, Brazil, Chile, Colombia and Mexico analysed with regional language models that catch sarcasm, idiom and tone.
Thousands of daily complaints classified by scope and impact, with critical issues routed for analyst review and an override feedback loop.
Verbatims tagged across post-claim, IVR, web, chat, onboarding, renewal and cancellation surveys for partner programmes, on one platform.
Microsoft Presidio and spaCy detect SSNs, phones, emails, addresses and account numbers, redacting them before any LLM call.
The drivers set out in our case study — qualitative, not market statistics.
Regulators and partner audits demand faster complaint identification and resolution tracking.
Millions of calls, surveys and complaints; keyword matching misses nuance and context.
Models now understand insurance terminology and customer intent well enough for production use.
Enterprise LLM pricing makes processing thousands of records a day viable, with predictable costs.
Millions of customer interactions a day — calls, surveys and complaints — held critical insight that was buried in noise. We built an enterprise GenAI platform that turns that unstructured feedback into actionable intelligence, on Azure OpenAI, Databricks and a unified data architecture.
Client anonymised. Figures describe the scale of the build — volumes processed, coverage and speed — not measured business outcomes.
Consumer-protection regulators, state regulators and partner audits demand faster complaint identification and resolution tracking. Manual processes can't keep pace.
Millions of interactions across calls, surveys and complaints. Keyword matching and traditional NLP miss nuance and context.
GPT-4-class reasoning now matches human understanding of complex insurance terminology and customer intent.
Enterprise LLM pricing makes processing thousands of records a day viable, with predictable cost.
The challenge
Contact centres were drowning in data and starving for insight.
What we built
AI that listens to every conversation and extracts every signal.
The challenge
Five countries, two languages, countless lost nuances.
What we built
A language engine that understands culture, not just words.
The challenge
4,000+ complaints a day, with regulators watching.
What we built
AI that triages like your best analyst — and never sleeps.
The challenge
Every platform, every programme, no single view.
What we built
One pipeline, any source, three-tier intelligence.
Daily batch orchestration with near-real-time triggers for critical complaints (L0 / L1).
A customer calls about two approved auto claims — vehicle damage and medical bills — still unpaid after three weeks. The pipeline returns:
{
"category": "Claims / Payment delay – multiple claims",
"complaint_level": "L3 – High",
"quality_score": 80,
"claims": ["#456789 vehicle damage", "#456790 medical bills"],
"summary": "Two approved claims stuck in an internal verification
queue; customer frustrated after vague updates. Agent raised a
level-two escalation and promised an emailed reference."
}
From the case study's simulation, on a sample transcript.
Microsoft Presidio detects SSNs, phone numbers, emails, addresses and account numbers across transcripts, surveys and complaints. spaCy NER plus the LLM add context-aware detection, with redaction before any text reaches the model.
Designed for
Technology stack
The same architecture applies to any high-volume customer feedback — in banking, healthcare and telecom as well as insurance.
Enter your own volumes. The estimate compares today's manual handling with agents working the cases and people reviewing only the exceptions.
Estimate only, not a quote or a DaasLabs result. Manual hours = cases × minutes ÷ 60. Supervised hours = cases × (1 − STP share) × review minutes ÷ 60. Hours saved = manual − supervised. FTE = hours saved ÷ 150. Cost saved = hours saved × cost per hour (× 12 for a year). Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Tell us the problem — a complaints backlog, slow claims payments, submissions that wait, an IFRS 17 close that runs late, a platform to modernise. We'll propose an assessment or a 30-45 day pilot, delivered by DaasLabs teams on our framework and accelerators.