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A framework combining quantitative analytical methods to detect anomalies in financial statements of organisations
Thesis (MEng)--Stellenbosch University, 2024.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2025
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| _version_ | 1867614035061506048 |
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| access_status_str | Open Access |
| author | Kapenda, Deborah |
| author2 | Vlok, P. J. |
| author_browse | Kapenda, Deborah Vlok, P. J. |
| author_facet | Vlok, P. J. Kapenda, Deborah |
| author_sort | Kapenda, Deborah |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description |
Thesis (MEng)--Stellenbosch University, 2024. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/131692 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:45:37.487Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/131692 A framework combining quantitative analytical methods to detect anomalies in financial statements of organisations Kapenda, Deborah Vlok, P. J. Schutte, C. L. S. Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Forensic accounting Financial analysis of business enterprises Fraud Financial statements UCTD Thesis (MEng)--Stellenbosch University, 2024. ENGLISH ABSTRACT: This thesis develops the Annual Financial Statement Risk Alert Signal (AFS RAS) framework to detect financial statement fraud and anomalies, integrating the Altman Z–Score, Beneish M–Score, and Benford’s Law. The framework aims to identify fraudulent activities within organisations by assessing financial health and detecting anomalies in reported data. The thesis evaluates a selection of major corporations across various industries in the United States of America, highlighting the effectiveness of each method in detecting potential fraud. The Altman Z–Score provides insights into financial distress, the Beneish M–Score identifies possible earnings manipulation, and Benford’s Law detects anomalies in numerical data. The results indicate that the framework achieves a True Positive Rate (TPR) of 80%, successfully identifying 20 out of 25 fraudulent financial statements, with a False Positive Rate (FPR) of 20%. The analysis shows that revenue manipulation is the most common type of fraud, followed by earnings manipulation and accounting issues. While the AFS RAS framework demonstrates strong potential in fraud detection, further refinement is needed to reduce false positives and enhance accuracy. This thesis highlights the importance of comprehensive financial analysis in improving fraud prevention strategies and safeguarding organisational integrity. Future research should focus on integrating additional machine learning techniques and expanding the dataset to include broader data sources and international contexts to enhance the framework’s efficacy further. AFRIKAANSE OPSOMMING: Hierdie studie bied ’n ontleding van die opsporing van finansiële state–bedrog deur gebruik te maak van die Jaarlikse Finansiële Staatsrisikowaarskuwingsein (AFS RAS)–raamwerk, wat die Altman Z–telling, Beneish M–telling en Benford se wet integreer. Die raamwerk het ten doel om bedrieglike aktiwiteite binne organisasies te identifiseer deur die finansiële gesondheid te assesseer en anomalieë in gerapporteerde data op te spoor. Die studie evalueer ’n seleksie van groot korporasies oor verskeie industrieë, en beklemtoon die doeltreffendheid van elke metode om potensiële bedrog op te spoor. Die Altman Z–telling verskaf insigte oor finansiële nood, die Beneish M–telling identifiseer moontlike verdienste–manipulasie, en Benford’s Law bespeur anomalieë in numeriese data. Die resultate dui daarop dat die raamwerk ’n hoë Ware Positiewe Tarief (TPR) van 80% behaal, wat 20 uit 25 bedrieglike gevalle suksesvol identifiseer, met ’n Vals Positiewe Tarief (FPR) van 20%. Die ontleding toon dat inkomstemanipulasie die mees algemene tipe bedrog is, gevolg deur verdienstemanipulasie en rekeningkundige kwessies. Terwyl die AFS RAS–raamwerk sterk potensiaal in bedrogopsporing toon, is verdere verfyning nodig om vals positiewe te verminder en akkuraatheid te verbeter. Hierdie studie beklemtoon die belangrikheid van omvattende finansiële ontleding in die verbetering van bedrogvoorkomingstrategieë en die beveiliging van organisatoriese integriteit. Toekomstige navorsing moet fokus op die integrasie van bykomende masjienleertegnieke en die uitbreiding van die datastel om breër databronne en internasionale kontekste in te sluit om die raamwerk se doeltreffendheid verder te verbeter. Masters 2025-02-06T07:08:13Z 2025-02-06T07:08:13Z 2024-12 Thesis https://scholar.sun.ac.za/handle/10019.1/131692 en Stellenbosch University xvi, 172 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Forensic accounting Financial analysis of business enterprises Fraud Financial statements UCTD Kapenda, Deborah A framework combining quantitative analytical methods to detect anomalies in financial statements of organisations |
| title | A framework combining quantitative analytical methods to detect anomalies in financial statements of organisations |
| title_full | A framework combining quantitative analytical methods to detect anomalies in financial statements of organisations |
| title_fullStr | A framework combining quantitative analytical methods to detect anomalies in financial statements of organisations |
| title_full_unstemmed | A framework combining quantitative analytical methods to detect anomalies in financial statements of organisations |
| title_short | A framework combining quantitative analytical methods to detect anomalies in financial statements of organisations |
| title_sort | framework combining quantitative analytical methods to detect anomalies in financial statements of organisations |
| topic | Forensic accounting Financial analysis of business enterprises Fraud Financial statements UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/131692 |
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