<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Nowadays information and communication technologies are playing a decisive role in helping the financial institutions to deal with the management of credit risk. There have been significant advances in scorecard model for credit risk management. Practitioners and policy makers have invested in implementing and exploring a variety of new models individually. Coordinating and sharing information groups, however, achieved less progress. One of several causes of the 2008 financial crisis was in data architecture and information technology infrastructure. To remedy this problem the Basel Committee on Banking Supervision (BCBS) outlined a set of principles called BCBS 239. Using Ontology Design Patterns (ODPs) and BCBS 239, credit risk scorecard and applicant ontologies are proposed to improve the decision making process in credit loan. Both ontologies were validated, distributed in Ontology Web Language (OWL) files and checked in the test cases using SPARQL. Thus, making their (re)usability and expandability easier in financial institutions. These ontologies will also make sharing data more effective and less costly.</p></abstract>

Nowadays information and communication technologies are playing a decisive role in helping the financial institutions to deal with the management of credit risk. There have been significant advances in scorecard model for credit risk management. Practitioners and policy makers have invested in implementing and exploring a variety of new models individually. Coordinating and sharing information groups, however, achieved less progress. One of several causes of the 2008 financial crisis was in data architecture and information technology infrastructure. To remedy this problem the Basel Committee on Banking Supervision (BCBS) outlined a set of principles called BCBS 239. Using Ontology Design Patterns (ODPs) and BCBS 239, credit risk scorecard and applicant ontologies are proposed to improve the decision making process in credit loan. Both ontologies were validated, distributed in Ontology Web Language (OWL) files and checked in the test cases using SPARQL. Thus, making their (re)usability and expandability easier in financial institutions. These ontologies will also make sharing data more effective and less costly.

The Implementation of Credit Risk Scorecard Using Ontology Design Patterns and BCBS 239

LRIT-CNRST (URAC’29), Faculty of Sciences, Rabat IT Center

High School of Technology in Sale, LCS, Faculty of Science

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Nowadays information and communication technologies are playing a decisive role in helping the financial institutions to deal with the management of credit...

The Implementation of Credit Risk Scorecard Using Ontology Design Patterns and BCBS 239

Published Online: Jun 12, 2020

Page range: 93 - 104

Received: Feb 10, 2020

Accepted: Mar 20, 2020

DOI: https://doi.org/10.2478/cait-2020-0019

Keywords
Ontology design patterns, OWL, credit risk scorecard, decision support, BCBS 239

© 2020 Jalil Elhassouni et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The Implementation of Credit Risk Scorecard Using Ontology Design Patterns and BCBS 239

Published Online: Jun 12, 2020

Page range: 93 - 104

Received: Feb 10, 2020

Accepted: Mar 20, 2020

DOI: https://doi.org/10.2478/cait-2020-0019

KeywordsOntology design patterns, OWL, credit risk scorecard, decision support, BCBS 239

© 2020 Jalil Elhassouni et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Ontology design patterns, OWL, credit risk scorecard, decision support, BCBS 239