Overview
What this challenge is about.
You receive 500 sample customer records across CRM, payments core, and KYC systems, plus a 50-record entity-resolution benchmark (pairs labelled same/different). Design an OWL ontology covering Customer, LegalEntity, Account, Transaction, RelatedParty, and KYCRecord. Implement entity resolution with a deterministic + fuzzy hybrid (Splink or RecordLinkage library), model the 500 customers in RDF, and write 10 SPARQL queries the compliance team would run weekly. Deliver: ontology, RDF data, entity-resolution notebook, query notebook, and a 6-page specification.
The Brief
What you'll do, and what you'll demonstrate.
Specify a customer-360 knowledge graph with entity resolution and weekly compliance queries that a platform team can execute.
Earning criteria — what you'll demonstrate
- Design an OWL ontology for a regulated-industry customer domain
- Apply hybrid deterministic + fuzzy entity resolution
- Author SPARQL queries that support compliance reviews
- Communicate KG design in a platform-executable specification
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
AI Solutions Architect
Designing a customer-360 KG spec for a regulated client is the AI solutions architect's daily output in fintech engagements.
This challenge sharpens
- customer-360
- knowledge-graphs
- spec-writing
Data Engineer
Implementing entity resolution and RDF modeling is core data-engineering work at any fintech with a unified-customer ambition.
This challenge sharpens
- entity-resolution
- owl-ontology
- sparql
AI Engineer
Building the compliance query layer on top of the KG is the AI-engineer skillset that fintech compliance teams hire for.
This challenge sharpens
- sparql
- knowledge-graphs
- entity-resolution