SCNET · Enterprise IT · Ankara, Türkiye

Sanal Çekirdek

Improve decision speed from policy to claim without losing context or control.

Insurance technology services automate processes that combine documents, rules, expert judgment, and many external data sources. Sanal Çekirdek measures the value of that automation not only in speed, but in making missing information visible, routing exceptions to the right expert, and keeping decisions explainable.

Document- and knowledge-intensive workflows

Policies, endorsements, claim files, adjuster reports and correspondence are connected to workflows through classification, extraction and validation. Low-confidence outputs are routed for human review.

  • Document classification and field extraction
  • Cross-checking against rules and system data
  • Missing-document and inconsistency handling
  • Source-attributed knowledge assistant

Claims and fraud analytics

Claim context can be connected to policy, customer, history, documents and external signals to support prioritization and review. Model output becomes evidence in an expert workflow rather than an unchallengeable decision.

  • Claim severity and complexity estimation
  • Anomaly and relationship analysis
  • Case routing and investigation queues
  • Model explanation and outcome tracking

Broker and customer experience

Quotation, policy, renewal and claim status are made consistent across channels. AI assistants can accelerate product and process knowledge, while personal information and transactions remain behind strong identity and permission controls.

  • Broker portals and API integration
  • Quote and policy status visibility
  • Grounded product and coverage knowledge
  • Agent assist and customer handoff

Resilient core and data governance

Core policy and claims systems are managed with visibility into integration, data quality, continuity and change risk. Sensitive personal and health data is governed through separate classification and access rules.

  • Critical transaction and integration monitoring
  • Data quality and master-data controls
  • Backup, DR and cyber recovery
  • Access, logging and change evidence

How we work

  1. Measure the real variety of policy and claim documents
  2. Map the manual checks and the decision points
  3. Design extraction, cross-checking and the exception flow
  4. Pilot in one line of business with a limited document set
  5. Add lines and channels as accuracy is proven

How success is measured

  • Case cycle time and work in progress
  • Extraction accuracy and human correction
  • Claims-routing quality and rework
  • Core-system continuity and data quality

Frequently asked questions

Can an LLM interpret policy terms?

It can support source-attributed retrieval and summarization, but binding coverage or payment decisions require the current policy, endorsements, law and expert judgment. The system must preserve that distinction.

Can document intelligence work with poor scans?

It may, but image quality, document type, language and handwriting affect performance. Pre-processing, confidence scores and human validation establish an acceptable error boundary.

How is model bias controlled?

Representativeness, outcome disparities, explainability and error impact are tested for the use case. Direct and indirect effects of protected or sensitive attributes are reviewed with legal and risk teams.

Design document flow, decision points and expert judgment as one process.

Assess your insurance workflows