Insurers have widely built data lakes – which often lack a semantic “understanding” of data points and their interconnections. Analytics and reporting require manual model definitions by data scientists – largely limiting capabilities and speed of analytics.
Establish a semantic core graph and scale it up with ontologies, covering business structures and models as well as external graphs, all engineered decentralized.
(e.g., lines of business, products, policy data)
Add clients, companies, risks, causes of loss, product recalls, and other up-to-date topics and their relationships based on data analytics.
(e.g., healthcare clients, autonomous driving, airbag issues)
Apply real-time graph and event analytics to derive actionable insights and business process support.
(e.g., emerging risks, trigger events, sentiment)
Apply Artificial Intelligence to data lakes and create a truly comprehensive, agile Knowledge Base ready to power pro-active analytics, business applications, and processes with reliable but dynamic models and up-to-date information.