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A comparison of methodologies with minimal hyper-parameter tuning for anomaly detection on transactional data
Thesis (MCom)--Stellenbosch University, 2023.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2023
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| _version_ | 1867614099324534784 |
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| access_status_str | Open Access |
| author | Alderslade, James William |
| author2 | Visagie, Stephan Esterhuyse |
| author_browse | Alderslade, James William Visagie, Stephan Esterhuyse |
| author_facet | Visagie, Stephan Esterhuyse Alderslade, James William |
| author_sort | Alderslade, James William |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MCom)--Stellenbosch University, 2023. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/127133 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:46:39.009Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/127133 A comparison of methodologies with minimal hyper-parameter tuning for anomaly detection on transactional data Alderslade, James William Visagie, Stephan Esterhuyse Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics. Anomaly detection (Computer security) Machine learning -- Security measures Computer networks -- Reliability UCTD Thesis (MCom)--Stellenbosch University, 2023. ENGLISH SUMMARY: In the information technology era where data has become as valuable as gold, being able to process and use it effectively is vital. Many large industries ranging from healthcare and insurance to banking utilise some form of transactional data in order to track and process many of the services and products which they offer to their clients. These processes are moving too fast for any human to keep up, due to the ever increasing rates of service and convenience which are core drivers in this technological era. Under these circumstances, anomaly detection is required. Fraud detection is the process whereby these transactions are identified and so are the items which could be related to any fraudulent, wasteful or abusive behaviour. A variety of anomaly detection methodologies are investigated to illustrate and compare their ability to quickly and accurately detect anomalous transactions without being given a set of rules. The methods used range from traditional statistical methods, through to machine learning and deep learning. Isolation forest performs best on the evaluation criteria used in this study. AFRIKAANSE OPSOMMING: In die inligtingstegnologie-era waar data so waardevol soos goud geword het, is dit noodsaaklik om dit doeltreffend te verwerk en te gebruik. Baie groot nywerhede wat wissel van gesondheidsorg en versekering tot bankwese gebruik een of ander vorm van transaksiedata om baie van die dienste en produkte wat hulle aan hul kliente bied, op te spoor en te verwerk. Hierdie prosesse beweeg te vinnig vir enige mens om by te hou, as gevolg van die steeds toenemende tempo van diens en gerieflewering wat die kerndrywers in hierdie tegnologiese era is. Omstandighede vereis anomalie opsporing. Bedrogopsporing is die proses waardeur hierdie transaksies uitgelig word en so ook die items wat verband kan hou met enige bedrieglike, verkwistende of beledigende gedrag. ’n Verskeidenheid anomalie-opsporingsmetodologiee word ondersoek om hul vermoe om vinnig en akkuraat afwykende transaksies op te tel, te illustreer en te vergelyk. Die metodes wat gebruik word wissel van tradisionele statistiese metodes tot masjienleer en diep leer. Isolasie woude presteer die beste met die evalueringskriteria kriteria wat in hierdie studie gebruik word. Masters 2023-03-03T14:08:53Z 2023-05-18T07:05:54Z 2023-03-03T14:08:53Z 2023-05-18T07:05:54Z 2023-03 Thesis http://hdl.handle.net/10019.1/127133 en_ZA Stellenbosch University xii, 84 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Anomaly detection (Computer security) Machine learning -- Security measures Computer networks -- Reliability UCTD Alderslade, James William A comparison of methodologies with minimal hyper-parameter tuning for anomaly detection on transactional data |
| title | A comparison of methodologies with minimal hyper-parameter tuning for anomaly detection on transactional data |
| title_full | A comparison of methodologies with minimal hyper-parameter tuning for anomaly detection on transactional data |
| title_fullStr | A comparison of methodologies with minimal hyper-parameter tuning for anomaly detection on transactional data |
| title_full_unstemmed | A comparison of methodologies with minimal hyper-parameter tuning for anomaly detection on transactional data |
| title_short | A comparison of methodologies with minimal hyper-parameter tuning for anomaly detection on transactional data |
| title_sort | comparison of methodologies with minimal hyper parameter tuning for anomaly detection on transactional data |
| topic | Anomaly detection (Computer security) Machine learning -- Security measures Computer networks -- Reliability UCTD |
| url | http://hdl.handle.net/10019.1/127133 |
| work_keys_str_mv | AT aldersladejameswilliam acomparisonofmethodologieswithminimalhyperparametertuningforanomalydetectionontransactionaldata AT aldersladejameswilliam comparisonofmethodologieswithminimalhyperparametertuningforanomalydetectionontransactionaldata |