Insurance

Automate claims. Sharpen underwriting. Catch fraud before it costs you.

We build claims automation systems, underwriting decision engines, fraud detection models, and policyholder self-service portals in 12 weeks. Software that cuts cycle time, improves loss ratios, and keeps policyholders from leaving.

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14+

Insurance products

70%

Claims automation

12

Weeks to launch

Overview

Claims still take weeks. Underwriting still misses risk.

Insurance software development at RaftLabs focuses on the three biggest operational drains - claims cycle time, underwriting accuracy, and fraud leakage. We bring patterns from 100+ products across adjacent industries to build claims automation systems, underwriting decision engines, fraud detection models, and policyholder portals - each engineered to handle regulatory requirements while cutting operational costs.

Insurance still runs on manual processes designed for paper files. Claims adjusters spend 60% of their time on administrative tasks - chasing documents, re-keying data, and routing files between departments. The result is 15-30 day cycle times, 12-18% leakage on indemnity payments, and policyholders who leave because the experience is slow and opaque.

Our claims triage systems auto-route 40% of claims to straight-through processing. Underwriting engines pull credit, telematics, and property data to price risk in seconds instead of days. Fraud models flag suspicious patterns before payments go out.

Every product integrates with existing policy admin systems, claims platforms, and data warehouses. We work with Guidewire, Duck Creek, Majesco, and custom legacy stacks. No rip-and-replace required.

Experience Signal

14+ insurance products shipped with claims automation systems, underwriting decision engines, fraud detection models, and policyholder portals for P&C carriers, health plans, and specialty MGAs. 12 weeks is our default, not our stretch goal.

14+

Insurance products

70%

Claims automation

See case study

Industry Pain Points

What's broken in insurance

01

Claims cycle time averages 15-30 days because adjusters manually review documents, request information, and re-key data across disconnected systems

02

Underwriting decisions rely on limited application data and static rating tables, missing risk signals from telematics, credit, property, and behavioral data

03

Fraud detection catches less than 20% of fraudulent claims because rules-based systems can't spot complex patterns across claimant history and provider networks

04

Policyholder self-service stops at PDF downloads and phone calls - driving call center volumes up and renewal rates down

05

Policy administration changes take 6-12 months to roll out because legacy systems demand mainframe-era development cycles

06

Adjusters and underwriters juggle 6-10 tools per case and spend half their day context-switching instead of making decisions

Solutions

Problems we solve in insurance

Each solution is built from patterns we've validated across 100+ products. No experiments on your budget.

01

Intelligent claims triage and automation

AI reads FNOL submissions, extracts structured data from photos, documents, and adjuster notes, scores complexity, and routes simple claims to straight-through processing. Complex claims get enriched files and recommended reserves.

02

Augmented underwriting

Pulls credit, property condition, telematics, weather exposure, and claims history to produce a risk score with explainable factors. Underwriters review AI recommendations instead of building cases from scratch.

03

Real-time fraud detection

Graph-based models analyze relationships between claimants, providers, attorneys, and repair shops. Flags anomalies in billing patterns, timing, and claim narratives before payments get issued.

04

Policyholder self-service portal

Digital-first experience for quotes, policy changes, claims filing, and document access. AI chatbot handles routine questions - coverage details, payment status, certificate requests - cutting call center volume.

05

Document intelligence and data extraction

Pulls structured data from loss runs, medical records, repair estimates, and policy documents. Kills manual data entry and speeds every downstream process that depends on accurate document data.

06

Agent and broker intelligence

Unified dashboards with submission status, bind-to-issue timelines, retention risk, and cross-sell triggers. Agents stop juggling systems and start working the highest-value accounts.

Use Cases

Real-world use cases

Claims automation for a regional P&C carrier

Problem

A mid-market P&C carrier processed 42,000 claims annually with a 23-day cycle time. Adjusters spent 55% of their time on admin work - document collection, data entry, and status updates.

What we built

We built a claims triage engine that ingests FNOL data, extracts information from photos and documents with computer vision and NLP, scores complexity, and routes low-complexity claims to automated processing. High-complexity claims get pre-built adjuster files with recommended reserves.

Result

38% of claims processed straight-through without adjuster intervention. Average cycle time dropped to 11 days. Adjuster capacity climbed 45% - handling 60,000 claims annually with the same team. Leakage on automated claims decreased 8%.

Underwriting decision engine for a specialty MGA

Problem

A specialty MGA writing commercial property took 5-7 days to quote because underwriters manually gathered property data, loss history, and exposure information from multiple sources.

What we built

We built an underwriting workbench that auto-populates submissions with property data, satellite imagery analysis, claims history, and local hazard data. AI produces a preliminary risk score with explainable factors. Underwriters review and adjust instead of building from scratch.

Result

Quote turnaround dropped from 5 days to 8 hours. Submission-to-bind ratio climbed 22%. Loss ratio improved 4 points in the first year as better data surfaced risks that manual review missed.

Fraud detection for a health insurance plan

Problem

A health plan identified fraudulent claims only during retrospective audits, recovering less than $2M of an estimated $18M annual fraud exposure. Rules-based flags generated 70% false positives.

What we built

We built a graph-based fraud detection model that analyzes provider billing patterns, member utilization, referral networks, and claim timing. The system scores every claim in real time and flags high-risk ones for SIU review with evidence packages.

Result

Pre-payment fraud identification climbed from $2M to $9.4M annually. False positive rate dropped to 18%. SIU focused investigations on high-confidence cases, improving recovery rate 3.2x.

Proof

How we've solved insurance problems

Insurance

Insurance customer retention loyalty platform

18% improvement in policy renewal rates within the first year.

18%

Renewal rate improvement

$2.4M

Referral revenue

“We spent two years talking about loyalty. RaftLabs shipped it in ten weeks and the renewal numbers moved in the first quarter. The referral engine alone paid for the entire project.”

Patricia Nolan, SVP, Customer Retention
Read case study

Our Approach

How we approach insurance projects

1
Phase 1· Week 1-2

Insurance operations audit

We analyze claims workflows, underwriting process, loss ratios, and policyholder experience. Dollar losses from cycle time, leakage, fraud, and attrition get quantified using your actual numbers.

Deliverables

  • Claims workflow analysis with cycle time and leakage quantification
  • Underwriting process map with bottleneck identification
  • Prioritized opportunity list ranked by financial impact
2
Phase 2· Week 2-4

Product design and compliance planning

We design the product with your claims, underwriting, and IT teams. Every integration point - policy admin, claims system, data warehouse, regulatory requirements - gets mapped before build.

Deliverables

  • Product design validated by business and compliance leads
  • Integration specifications for policy admin and claims systems
  • Regulatory compliance plan covering state filing and data privacy requirements
3
Phase 3· Week 4-10

Build, integrate, and validate

We build in sprints with continuous integration to your production systems. AI models train on your historical data and validate against known outcomes before going live.

Deliverables

  • Working product integrated with existing insurance systems
  • Model validation results against historical claims and underwriting data
  • Performance benchmarks for accuracy, speed, and business impact
4
Phase 4· Week 10-12

Production rollout and model optimization

We deploy to production with your operations team trained on the system. AI models improve continuously as new claims, policies, and outcomes feed back into training data.

Deliverables

  • Full production deployment with operations team self-sufficient
  • Impact dashboard tracking cycle time, loss ratio, and fraud savings
  • Quarterly model retraining and optimization schedule

Outcomes

Measurable outcomes

30-45% reduction in claims cycle time through intelligent triage and straight-through processing
50-65% drop in call center volume through policyholder self-service portals
3-5x increase in pre-payment fraud identification using graph-based detection models
60-80% faster underwriting quote turnaround through automated data enrichment and risk scoring
4-point improvement in loss ratio as enriched data surfaces risks that manual review missed

Free Tools

Free tools for insurance

Calculators and assessments built from real project data. No signup required.

See all free tools

Pattern Transfer

RaftLabs built document processing pipelines for fintech KYC before applying the same extraction and classification approach to insurance claims. Both problems need structured data pulled from unstructured documents at speed. The compliance wrapper changed. The core engineering didn't.

Services

Services for insurance

From the blog

Articles for insurance teams

Frequently asked questions

Projects range from $50K-$200K. A claims triage and automation system starts around $60K. A full underwriting decision engine with data integrations runs $100K-$180K. Fraud detection models typically fall in the $80K-$150K range. Fixed estimate after a strategy session.

Next Step

Every week of manual claims processing is another week your policyholders wait.

One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.

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