Insurance

AI in insurance: less fraud, more active portfolio.

Insurers, brokers, and mutuals manage enormous volumes of documents, claims, and renewals with teams that do not scale. AI does not replace the actuary or claims adjuster — it reduces mechanical time so they apply their expertise where it truly matters.

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Serving insurers and brokers nationwide · Compliance by design

The Challenge

In insurance, margin is lost to friction, fraud, and silent portfolio attrition.

Every fraudulent claim detected late, every renewal lost because no one called in time, every repeated inquiry consuming an agent's hour — these are measurable losses that add up over the year. The data to prevent them already exists: in the CRM, claims history, and policy metadata.

  • Slow, manual underwriting

    Pricing that requires manually reviewing documents, cross-referencing multiple data sources, and waiting for internal validation. The customer waits days for a quote that could be ready in minutes.

  • Claims fraud that is hard to detect

    Fraud patterns only visible when analyzing hundreds of cases — anomalies in dates, amounts, recurring workshops or doctors. Without a model, each case is reviewed in isolation.

  • Renewals lost in silence

    Portfolio expiring without proactive contact, or with generic outreach that ignores the client's history. Attrition does not arrive as a wave — it arrives one by one.

  • Customer service that cannot scale at peaks

    Storm weeks or renewal campaigns: the call center collapses, wait times spike, NPS drops. More agents do not scale — automation does.

Use Cases

Where AI moves the combined ratio in an insurer

Five applications with documentable ROI at 12 months. In every case, professional judgment (actuary, adjuster, agent) is preserved where it adds value — AI handles mechanical volume.

  • 01

    Assisted underwriting and pricing

    The system automatically extracts applicant data from documents and external sources, pre-fills the underwriting form, applies the company's rules, and proposes a rate in seconds. The underwriter validates and binds — without starting from scratch.

    What changes

    Quote time for personal lines reduced from hours to minutes. Underwriting capacity expanded without adding headcount.

  • 02

    Claims fraud detection

    A model that scores each new claim against historical fraud patterns: frequency, atypical amounts, recurring repair shops or medical providers, inconsistencies in statements. Suspicious cases are flagged for investigation before payment.

    What changes

    Fraud detection rate improves 30-50% versus purely manual review. Avoided fraudulent payments cover the project cost within months.

  • 03

    Proactive renewal and portfolio retention

    A churn propensity model identifying policies at high risk of non-renewal 60-90 days before expiry. The sales team receives a daily prioritized list with the probable reason and recommended retention message.

    What changes

    Retention rate improves 4-8 percentage points on the portfolio where applied. Direct ROI in retained premium versus the cost of acquiring a new policy.

  • 04

    24/7 conversational customer service

    An assistant on web, app, and WhatsApp that handles coverage inquiries, opens claims, requests documents, and tracks status — without wait times or office hours. Transfers to an agent when judgment or negotiation is required.

    What changes

    50-65% reduction in repetitive call center contacts. Policyholder satisfaction rises from immediate response. Agents freed for complex claims.

  • 05

    Document extraction and classification

    Pipelines that automatically process policies, claim forms, medical reports, and assessments: extract structured data, classify claim type, and route to the correct department. No manual data entry, no manual sorting.

    What changes

    Claim opening and routing time reduced from hours to minutes. Classification errors and misfiled documents drop to near zero.

Our Approach

On top of your existing core. No migration.

We work with leading insurance platforms (Guidewire, Duck Creek, Salesforce Financial Services, proprietary systems) via API or direct integration. The AI layer connects on top — without touching certified actuarial or regulatory workflows.

  • Opportunity Diagnosis

    2-3 weeks

    Audit of key processes: underwriting, claims, renewal, and service. We identify the 2-3 use cases with the best ROI for your specific lines and volume.

    • Volume and time analysis by process
    • Data quality and accessibility review
    • Prioritized use case catalog by impact
    • 12-month ROI estimate

  • Single-Case Pilot

    8-12 weeks

    End-to-end implementation of fraud detection, assisted underwriting, or portfolio retention on a defined line or perimeter.

    • Data pipeline from existing sources
    • Model trained on proprietary history
    • Integration with claims/policy management system
    • Results dashboard for technical team and management
    • 8-week impact measurement post-go-live

  • Insurance Platform

    5-8 months

    For companies wanting AI consolidated across multiple processes: fraud, underwriting, retention, and service on a common architecture.

    • Unified data architecture by line
    • Multiple models with cross-feed learning
    • Integration with regulatory and mandatory reporting
    • Ongoing maintenance, retraining, and new use case development

Results

What changes at the insurer at 6-12 months

  • Fraud detection improved 30-50% versus manual review — with full traceability of every alert.
  • Quote time in personal lines reduced from hours to minutes without expanding underwriting team.
  • Retention rate improved 4-8 points — retained premium covering project cost in year one.
  • Call center handling 50-65% fewer repetitive contacts — agents focused on complex claims.
  • Automated claim opening and routing: fewer errors, faster processing, better policyholder experience.
  • Churn propensity model active 60-90 days before expiry — time to act, not to react.

FAQ

What technical and operations directors ask

  • Does it comply with Solvency II and GDPR?

    Compliance is a design constraint, not an afterthought. We work under signed data processing agreements, models that document their decisions (explainability required in credit and insurance), and without leaving the agreed data perimeter. Architecture is designed Solvency II-compatible from day one.

  • Can the fraud model be wrong?

    It can. That is why the output is a risk score with the factors explaining it — not a binary decision. The fraud investigator decides whether to investigate or pay. What the model does is prioritize what to review so no suspicious case slips through among hundreds of normal claims.

  • Will it work with our insurance core?

    We work with the leading platforms in the market (Guidewire, Duck Creek, proprietary systems, Salesforce FSC). Integration is part of the project scope — no system change required to start.

  • What does it cost to start?

    Diagnosis: €6k-€12k. Pilot on one use case (fraud, underwriting, or retention): €35k-€70k depending on line and volume. Multi-case platform: €100k-€200k+ over several months. ROI from detected fraud or retained premium typically covers the pilot within 12 months.

  • What if our historical data quality is poor?

    That is the most common situation. The initial audit tells us which data lever to fix first. In many insurance projects the first months are data consolidation and cleaning — and that is budgeted upfront, not as a surprise.

Want to see which use cases apply to your company?

A 30-minute call about your lines of business, claims volume, and distribution mix. We leave with 2-3 prioritized cases and a ballpark investment and expected ROI.

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