Start Date
12-13-2015
Description
The volume, velocity, variety and complexity of regulations are causing major information-related problems for the financial industry. The industry is increasingly looking to semantic technologies to help solve the many problems it faces—all of which are, in one way or another, data-, information- and knowledge-related. This design research (DR) study applies several semantic technologies to create an innovative regulatory compliance change management system prototype (RCMS). This ontology-based information system enables ground-breaking semantic tagging of lengthy and complex regulatory and legal texts, which are then loaded into a triple store for semantic querying and analysis. The findings of this DR study illustrate how practitioners can easily and efficiently identify and extract compliance obligations and prohibitions in regulatory and legal texts. This will enable them to meet the significant information-based regulatory compliance challenges they face. Initial reviews from practitioners across the financial industry highlight the relevance of our research.
Recommended Citation
Butler, Tom; Abi-Lahoud, Elie; and Espinoza, Angelina, "Designing Semantic Technologies for Regulatory Change Management in the Financial Industry" (2015). ICIS 2015 Proceedings. 8.
https://aisel.aisnet.org/icis2015/proceedings/ISdesign/8
Designing Semantic Technologies for Regulatory Change Management in the Financial Industry
The volume, velocity, variety and complexity of regulations are causing major information-related problems for the financial industry. The industry is increasingly looking to semantic technologies to help solve the many problems it faces—all of which are, in one way or another, data-, information- and knowledge-related. This design research (DR) study applies several semantic technologies to create an innovative regulatory compliance change management system prototype (RCMS). This ontology-based information system enables ground-breaking semantic tagging of lengthy and complex regulatory and legal texts, which are then loaded into a triple store for semantic querying and analysis. The findings of this DR study illustrate how practitioners can easily and efficiently identify and extract compliance obligations and prohibitions in regulatory and legal texts. This will enable them to meet the significant information-based regulatory compliance challenges they face. Initial reviews from practitioners across the financial industry highlight the relevance of our research.