Unsupervised anomaly detection for internal auditing: Literature review and research agenda
Auditing has to adapt to the growing amounts of data caused by digital transformation. One approach to address this and to test the full audit data population is to apply rules to the data. A disadvantage of this is that rules most likely only find errors, mistakes or deviations which were already a...
| Autores: | ; |
|---|---|
| Tipo de documento: | Artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Huelva (UHU) |
| Repositorio: | Arias Montano. Repositorio Institucional de la Universidad de Huelva |
| Lenguaje: | Inglés |
| OAI Identifier: | oai:ariasmontano.uhu.es:10272/19256 |
| Acceso en línea: | http://hdl.handle.net/10272/19256 |
| Nivel de acceso: | Acceso abierto |
| Palabra clave: | Auditing Anomaly detection Unsupervised Outlier detection |
| _version_ | 1878732379348008960 |
|---|---|
| author | Nonnenmacher, Jakob |
| author2 | Marx Gómez, Jorge |
| author2_role | author |
| author_browse | Marx Gómez, Jorge Nonnenmacher, Jakob |
| author_facet | Nonnenmacher, Jakob Marx Gómez, Jorge |
| author_role | author |
| collection | Arias Montano. Repositorio Institucional de la Universidad de Huelva |
| description | Auditing has to adapt to the growing amounts of data caused by digital transformation. One approach to address this and to test the full audit data population is to apply rules to the data. A disadvantage of this is that rules most likely only find errors, mistakes or deviations which were already anticipated by the auditor. Unsupervised anomaly detection can go beyond those capabilities and detect novel process deviations or new fraud attempts. We conducted a systematic review of existing studies which apply unsupervised anomaly detection in an auditing context. The results reveal that most of the studies develop an approach for only one specific dataset and do not address the integration into the audit process or how the results should be best presented to the auditor. We therefore develop a research agenda addressing both the generalizability of unsupervised anomaly detection in auditing and the preparation of results for auditors |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_41f0d9b0a0e402e2ca197d6d69649ab5 |
| institution | Universidad de Huelva (UHU) |
| instname_str | Universidad de Huelva (UHU) |
| language | eng |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:ariasmontano.uhu.es:10272/19256 |
| publishDate | 2021 |
| publishDateSort | 2021 |
| publisher | Universidad de Huelva |
| publisherStr | Universidad de Huelva |
| reponame_str | Arias Montano. Repositorio Institucional de la Universidad de Huelva |
| spelling | Unsupervised anomaly detection for internal auditing: Literature review and research agenda Detección de anomalías no supervisada para auditoría interna: revisión de la literatura y agenda de investigación Nonnenmacher, Jakob Marx Gómez, Jorge Auditing Anomaly detection Unsupervised Outlier detection Auditing has to adapt to the growing amounts of data caused by digital transformation. One approach to address this and to test the full audit data population is to apply rules to the data. A disadvantage of this is that rules most likely only find errors, mistakes or deviations which were already anticipated by the auditor. Unsupervised anomaly detection can go beyond those capabilities and detect novel process deviations or new fraud attempts. We conducted a systematic review of existing studies which apply unsupervised anomaly detection in an auditing context. The results reveal that most of the studies develop an approach for only one specific dataset and do not address the integration into the audit process or how the results should be best presented to the auditor. We therefore develop a research agenda addressing both the generalizability of unsupervised anomaly detection in auditing and the preparation of results for auditors La auditoría tiene que adaptarse a las crecientes cantidades de datos provocadas por la transformación digital. Un enfoque para abordar esto y probar la población completa de datos de auditoría es aplicar reglas a los datos. Una desventaja de esto es que las reglas probablemente solo encuentren errores, equivocaciones o desviaciones que ya fueron anticipadas por el auditor. La detección de anomalías sin supervisión puede ir más allá de esas capacidades y detectar nuevas desviaciones de procesos o nuevos intentos de fraude. Realizamos una revisión sistemática de los estudios existentes que aplican la detección de anomalías sin supervisión en un contexto de auditoría. Los resultados revelan que la mayoría de los estudios desarrollan un enfoque para un solo conjunto de datos específico y no abordan la integración en el proceso de auditoría o cómo los resultados deben presentarse mejor al auditor Universidad de Huelva http://hdl.handle.net/10272/19256 |
| spellingShingle | Unsupervised anomaly detection for internal auditing: Literature review and research agenda Nonnenmacher, Jakob Auditing Anomaly detection Unsupervised Outlier detection |
| title | Unsupervised anomaly detection for internal auditing: Literature review and research agenda |
| title_full | Unsupervised anomaly detection for internal auditing: Literature review and research agenda |
| title_fullStr | Unsupervised anomaly detection for internal auditing: Literature review and research agenda |
| title_full_unstemmed | Unsupervised anomaly detection for internal auditing: Literature review and research agenda |
| title_short | Unsupervised anomaly detection for internal auditing: Literature review and research agenda |
| title_sort | Unsupervised anomaly detection for internal auditing: Literature review and research agenda |
| topic | Auditing Anomaly detection Unsupervised Outlier detection |
| topic_facet | Auditing Anomaly detection Unsupervised Outlier detection |
| url | http://hdl.handle.net/10272/19256 |