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Fraud detection using operational risk modelling with incomplete data
Dissertation (MSc (Actuarial Science))--University of Pretoria, 2019.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2025
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| _version_ | 1867613515379900416 |
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| access_status_str | Open Access |
| author2 | Kijko, Andrzej |
| author_browse | Kijko, Andrzej |
| author_facet | Kijko, Andrzej |
| collection | Thesis |
| dc_rights_str_mv | © 2024 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc (Actuarial Science))--University of Pretoria, 2019. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/107113 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:22.574Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/107113 Fraud detection using operational risk modelling with incomplete data Kijko, Andrzej u12235947@up.ac.za Beyers, Conrad Muzerengwa, Kudzai Calvin UCTD Sustainable Development Goals (SDGs) Loss data analysis Operational risk Level of completion Frau detection Gutenburg-Richter b-value Dissertation (MSc (Actuarial Science))--University of Pretoria, 2019. Systems and processes may fail and employees can engage in fraudulent ac-tivities that can go unnoticed for a very long time and the resulting losses can be very high and catastrophic to an institution. Setting a minimum threshold or a level of completeness will not guarantee that all losses above this point will be reported. In order to model operational risk data, a method that does not depend on the level of completeness is suggested. This can be done by introducing a de-tection probability that is combined with the underlying loss distribution to give a 3-parameter gamma distribution and fitted to a simulated dataset. It is found that the methodology is able to accurately estimate parameters when the data is incomplete. Insurance and Actuarial Science MSc (Actuarial Science) Unrestricted Faculty of Natural and Agricultural Sciences SDG-16: Peace,justice and strong institutions 2025-12-08T07:13:27Z 2025-12-08T07:13:27Z 2019-04 2019-02 Dissertation * A2019 http://hdl.handle.net/2263/107113 en © 2024 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sustainable Development Goals (SDGs) Loss data analysis Operational risk Level of completion Frau detection Gutenburg-Richter b-value Fraud detection using operational risk modelling with incomplete data |
| title | Fraud detection using operational risk modelling with incomplete data |
| title_full | Fraud detection using operational risk modelling with incomplete data |
| title_fullStr | Fraud detection using operational risk modelling with incomplete data |
| title_full_unstemmed | Fraud detection using operational risk modelling with incomplete data |
| title_short | Fraud detection using operational risk modelling with incomplete data |
| title_sort | fraud detection using operational risk modelling with incomplete data |
| topic | UCTD Sustainable Development Goals (SDGs) Loss data analysis Operational risk Level of completion Frau detection Gutenburg-Richter b-value |
| url | http://hdl.handle.net/2263/107113 |