Financial Services

Build and Manage Shared Semantics

Data is the lifeblood for many financial services companies. But it can be difficult to find the right data, or reuse it without running afoul of regulations (see: Meta’s recent $1.3 billion GDPR fine).

Semantics is the perfect solution for this complexity. By using knowledge graphs for data governance, financial and transactional data can be modelled together with other sources like policy and access rules. Intrinsically tying the data together this way makes it much faster and easier to combine and reuse while remaining compliant with rules and policies.

Knowledge graphs published in RDF, as with TopBraid EDG, make the data machine readable and enables new AI initiatives, while also making it more available and consumable for self-service analytics.

Semantic Building Blocks

Controlled vocabularies as semantic building blocks lets data flow freely through your organization, despite differences in technologies or terminologies across divisions. Data dictionaries, policies, access rules and governance, lineage and tag sets can all be made into building blocks that improve business processes but also clean and publish data sets in a more consumable format.

Semantic Data Products

With building blocks in place, financial services companies can combine different data sets, including reference data and policy rules, to create engines that automate automated GDPR & privacy enforcement policies.

Enterprise Semantic Layer

With a semantic data catalog, financial services companies can bring catalog initiatives to the finish line. By enhancing an existing data catalog with semantics, the data can be much more findable and reusable, while also tied to policies and procedures. This data-centric approach enables data agility and innovation, while still ensuring compliance.

Likewise, semantic approaches to master data management (MDM) are more flexible and powerful than traditional ETL methods. Semantics also enables a more complete and dynamic view of any entity. For example, your customers are not static – their financial behavior can be gleaned from their economic history, and this information can provide a complex portrait that can be used to anticipate their financial needs, warn about potentially risky decisions, and even recommend (and ultimately implement) investment strategies.

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