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in near real-time to systemic risk profiles based on a multitude of ad
hoc economic scenarios. Thus the collaboration of EDM and OMG
gave rise to the formation of a common financial industry language – FIBO (Financial Industry Business Ontology). FIBO is a common financial industry
language and conceptual model developed to precisely define and harmonise data about financial contracts. The goal is to support integration of data from multiple sources and align content across silos and organisations. Aligned, comparable and harmonised
data is essential to: l Reduce data reconciliation errors and transformation processes. l Aggregate data across multiple lines of businesses and organisations. l Automate business processes. l Improve risk analysis. The overall objective of FIBO, is to provide
consistent data and reporting to business users, executive management, regulators and market authorities There are many benefits to defining an
ontology of business terms and relationships, not least being that it is the best way to fully capture the actual business requirements against which any modelling is carried out. Modelling efforts which try to use other
formats such as unified modelling language (UML) or extensible markup language (XML) to capture business requirements often get bogged down or become dependent on the technical expertise (and business knowledge) of one or two people to maintain what is intended to be a business requirements model. In addition, business requirements (whether for data or process) are not the same as something that is designed to meet those requirements, and combining these
two into one format leads to a loss of the maintainability of the design. All you have to understand is that data
is organised into groups of three (hence triples) that contain subjects and objects that are linked together by predicates and verbs. And these concepts are all precisely defined based on the terms, conditions, and obligations of the contract. And once you define these concepts, you can link them together like tinker toys. That is facilitated by an ontology which captures the meaning of information elements and their relationship in a form that can be automatically processed by computers.
Taxonomy of terms In order to create a usable ontology, one has to first define a taxonomy of terms. These two options are not an ‘either/or’ choice, but a choice of what level of detail to apply when modelling the business reality. Breaking the business facts down into a taxonomic hierarchy of real ‘things’ allows for the creation of robust data models, regardless of whether or not an ontology is needed. It allows for reference to existing taxonomies of real-world things, which are already standardised (such as the classification of financial instrument, or CFI, taxonomy). Adding business relationships and other
logic functions to the taxonomy to make it an ontology, allows for a richer variety of business facts to be represented. Many of these may not be needed in a data model, but may, for example, be referred to in programme or messaging design. They must, however, be business relationships. Other kinds of relationship, or optimisation of business relationships into a design, can be carried out during design, but should not be reflected in the business-semantics model. Once a taxonomy, or ontology, is created,
this can be imported into a UML editing tool to form the basis for development of a data model. It is also possible to maintain an ontology directly in a UML editor using standard UML extensions which have been developed for web ontology language (OWL) modelling. This model should be maintained in a
separate UML package or as a separate model, in order to make clear that it is of a different nature to any data model designs elsewhere in the UML universe.
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Breaking the business facts down into a taxonomic hierarchy of real ‘things’ allows for the creation of robust data models
Harmonising data across the financial
industry helps support analytical processes, illuminates the links and relationships among instruments and business entities, informs data quality and verification procedures, and empowers data integration from multiple sources and across linked processes. With FIBO, firms will be able to use
machine intelligence to make inferences that can mitigate regulatory and operational risk. FIBO can map to, and supplement, existing legacy financial-data standards, and can be implemented unobtrusively and incrementally with legacy data.
Summary An ontology extends a taxonomy which is organised according to some classification principles. An ontology is not another sort of data model. FIBO is the Common Semantic Model that can link and unify data across multiple financial standards and enterprise silos. The ways to leverage FIBO are:
l Scaffolding for industry alignment on common meaning of financial concepts. l Harmonise data for regulatory reporting and macroprudential oversight. l Provide semantic mapping from disparate siloed data to a common business data standard for integration. l Enable visualisations for taxonomies, financial instruments, and all forms of data relationships. l Enable risk data aggregations across multiple dimensions and taxonomies. Regulators and industry players are
paying attention to the evolutution of FIBO. There are benefits for regulators to drive and enforce policies, and additionally it facilitates a collective response from the financial institutions. CCR
December 2017
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