Description-Logic Reasoner for Insurance-Policy Coverage Checks
Overview
What this challenge is about.
Model 50 insurance rules in OWL, run HermiT on 1,000 claims, compare results, and export justification traces. Earn a verifiable certificate.
The scenario
The insurtech (around 220 people, around 600,000 UK motor-insurance customers) faces increasing complaints about opaque coverage denials and is exploring whether a logic-based system can both reduce maintenance burden and improve customer-facing explanations.
The Brief
What you'll do, and what you'll demonstrate.
Replace an if/else coverage engine with a description-logic ontology that produces auditable, explainable coverage decisions.
Earning criteria — what you'll demonstrate
- Model real business rules as description-logic axioms
- Use a reasoner to derive coverage outcomes
- Extract and format reasoner justification traces
- Compare a logic-based system with a procedural baseline
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Description Logics
Apply description logics to solve real industry problems and demonstrate production-level capability.
- Owl
Apply owl to solve real industry problems and demonstrate production-level capability.
- Reasoning
Apply reasoning to solve real industry problems and demonstrate production-level capability.
- Knowledge Representation
Apply knowledge representation to solve real industry problems and demonstrate production-level capability.
- Rule Engines
Apply rule engines to solve real industry problems and demonstrate production-level capability.
- Explainability
Apply explainability to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Solutions Architect
Replacing procedural rule engines with description-logic ontologies is exactly the kind of architecture work AI solutions architects ship at insurance and regulated-services firms.
This challenge sharpens
- description-logics
- owl
- knowledge-representation
AI Engineer
Wiring a reasoner into a production pipeline and exposing justification traces is core AI-engineer work in regulated industries.
This challenge sharpens
- reasoning
- rule-engines
- explainability
Data Engineer
Designing ontology-backed rule systems with versioned classification harnesses transfers to data-engineering work on knowledge-pipeline platforms.
This challenge sharpens
- owl
- knowledge-representation
- rule-engines