Semantic Enabled Data Fabric Powers Trade Management Application

A global fintech with over $8 billion market capitalization that handles millions of trades each day involving trillions of dollars, supports communications that reach 75% of North American households, and manages shareholder voting in 90 countries, leveraged Semaphore's Semantic AI platform as part of their Data Fabric, which regulates and supports the processing, analysis, and management of enterprise data.

The project - a global post trade management application – supports the organization’s information initiatives to:

• Provide a single view into a client’s global trade positions - enhancing key client services
• Reduce complexity by consolidating systems and eliminating redundancy
• Enable regulatory compliance through holistic governance
• Integrate with a client’s complex IT ecosystem and/or third-party systems
• Provide an extensible, component-based system that can meet client demands
• Establish a unified user interface that integrates multiple applications for an enhanced user experience.

Semaphore is a key component in the Data Fabric. Semaphore model management and visualization tools allow them to tune, streamline, simplify, and improve the model and enable them to deploy and integrate information universes.

The Data Fabric platform uses a document-centric NoSQL database. It combines data, search style indexing, and application server behaviours previously only available through discrete entities (i.e. databases, data warehouses, data lakes, or ETL tools).

The data fabric:

• Uses Semaphore’s model-driven approach to process both structured and unstructured data and employs a data access standard of ontologies that support multiple data types.
• Engages an event-driven data pipeline tool that supports a wide variety of functions and services.
• Is driven by a knowledge graph containing all the concepts in a specific domain and how those concepts are used to support various business operations.
• Scales different services/components independently to meet the functional and performance needs of individual clients/users/applications.

Using Semaphore’s Knowledge Model Management (KMM) model creation and governance capabilities, model developers, subject matter experts and other stakeholders iteratively created a common and standardized set of taxonomies using industry standards such as FIBO, SWIFT. Semaphore’s task-based model governance capabilities allowed them to improve model security, manage versioning, and eliminate manual model, term, and relationship tracking via spreadsheet.

Semaphore’s Model Mapping Tool (MMT) enabled them to automatically create relationships between proprietary and industry-standard concepts. This allows them to integrate Semaphore’s model index into the data fabric to identify and capture the sequence of information for financial transactions from the clearing house.