Linked Data adoption and application within financial business processes

Linked Data adoption and application within financial business processes

ISBN 9783954899760
Publisher ANCHOR ACADEMIC PUBLISHING
Year 2015
Number of pages 90
Categories Social Sciences, Management, Business
By Desislava Kalcheva
Languages English

41.08 USD

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Linked Data (hence LD) is a concept for efficient handling of data, which could be used for dealing with complex data and data structures. It operates on RDF triples, promotes keeping data at the source instead of copying it and allows for (semi-)automated reasoning. Therefore the interest towards applying it in different contexts and case studies increases, as well as the number of initiatives around it on a global scale rises accordingly. LD is framed in the context of the financial services domain as an enabler of a lot of opportunities for optimizing (financial) product rankings, as well as cross-country, cross-domain and cross-company benchmarks, without the need for special templates or enormous manual efforts. The LD adoption depends on its perceived ease of use and most heavily on the perceived usefulness. Several factors fall within this category, which could significantly influence it. Such factors are increasing the data re-use potential and facilitating (financial) product comparisons, recommendation applications, and product rankings. Next to that, legislative issues and the limited expressiveness of LD must be taken care of for the specific case. An equivalent of a "mandatory field" must be provided and relevancy of encountered resources argued and ensured. Furthermore, LD adoption can be enhanced by a higher availability of complementary products, such as ontologies and vocabularies, and increase in the perceived installed base. In the aspect of business reporting, LD has the potential to solve some of the main issues of XBRL and thereby help to gain the benefits, expected from its inauguration. Furthermore, solutions of these issues, based on LD are expected to be more dynamic than current considerations, such as enforcing a global standard or prohibiting private extensions of XBRL ontologies. Some of the main potentials for LD in this area are seen in solving semantic heterogeneity, enabling cross-country, cross-domain and cross-company benchmarks of business information without the need for extensive human manual efforts. Next to that, LD and XBRL are seen as very familiar to each other, possibly the next step in the system to system data exchange. Therefore, LD technology providers are advised to assure the availability of complementary products, such as ontologies and vocabularies for specific domains and across domains, and ensure maximal reuse of existing ontologies.