In Focus Consumer Credit


Usage of ontology in financial services


A new way of defining and expressing procedures and data may help to promote clarity


Rajib Chakravorty Independent consultant, HSBC rajib32@hotmail.com


How do firms usually manage and specify the requirements for technical development? Typically the business terms and definitions are put into a spreadsheet, or in some cases they are recorded only in e-mails or meeting notes. There has been a growing realisation among business participants that what they needed first and foremost was some simple, non-technical, non-designed representation of the business terms and definitions. The financial industry needs expressive


global data standards for: l Identification of legal entities, jurisdictions and ownership control hierarchies. l Identification of financial contracts and instruments. l Classification and data linkage for aggregation. l Actionable risk intelligence. One of the most significant lessons learned


from the global financial crisis, that began in 2008, was banks’ information technology and data architectures were inadequate to support the broad management of financial risks. Many banks lacked the ability to aggregate risk exposures and concentrations quickly and accurately at bank-group level, across business lines and between legal


entities. This led to the principles for effective risk-data aggregation and risk reporting from the Basel Committee on Banking Supervision. The situation at that time resulted in two


main problems: l The first is that you cannot manage what you cannot measure. Assessing the risk of a collection of assets is impossible when the relationships between those assets are vague. Assessing the value is equally difficult. Basically, the banks had no way to really know what they had on hand, and they lacked a standard that would allow them to collect this understanding. If they did know internally what they had, they lacked a way to communicate that to other businesses. l The second, and more pervasive, problem is about regulations of the industry. Those regulations must be described somehow in terms that carry the same meaning for all the main players. Existing laws are made inconsistent by ambiguous category definitions, particularly when different organisations use terms differently. Some will purposefully manipulate the language to game the regulations, but many of the existing derivative instruments were simply so complicated it was not clear at all where they should fit and how the laws applied. All around the industry, financial


institutions – as well as the vendors that serve them and the authorities that regulate them – are talking about data issues: l How should financial data standards be defined? l How should the financial industry tackle these risk data management, aggregation, and reporting challenges? lWhat technologies should be employed to fulfil these requirements?


December 2017 www.CCRMagazine.co.uk


Existing laws are made inconsistent by ambiguous category definitions, particularly when different organisations use terms differently


The Enterprise Data Management (EDM)


Council and the Object Management Group (OMG) believe that semantic web technology: l Is a transformational technology for defining financial-data standards. l Can map to and supplement existing legacy financial data standards. l Is a prudent investment to better enable risk-data aggregation and analytics. l Can be implemented unobtrusively and incrementally with legacy data.


Data management One has to look no further than the Risk Data Aggregation principles report released by the Basel Committee on Banking Supervision as evidence of the importance of data management. It focuses on the importance of the underlying infrastructure for reference data and specifically calls out concepts like standard identifiers, consistent data definitions, integrated taxonomy, linked metadata, and harmonised data repositories, which is quite remarkable in a principles-based document written by a group of senior banking supervisors. And it talks about the role of data in


creating the links and relationships needed to understand about threats to financial stability and to respond


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