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Data to information to text summaries of financial data
The field of auditing is becoming increasingly dependent on information technology as auditors are forced to follow the increasingly complex information processing of their clients. There exists a need for a system that can convert vast quantities of data generated by existing systems and data analy...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2019
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| _version_ | 1867613204806369280 |
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| access_status_str | Open Access |
| author | Kyle, Cameron |
| author2 | Keet, Maria |
| author_browse | Keet, Maria Kyle, Cameron |
| author_facet | Keet, Maria Kyle, Cameron |
| author_sort | Kyle, Cameron |
| collection | Thesis |
| description | The field of auditing is becoming increasingly dependent on information technology as auditors are forced to follow the increasingly complex information processing of their clients. There exists a need for a system that can convert vast quantities of data generated by existing systems and data analytics techniques, into usable information and then into a format that is easy for someone not trained in data analytics to understand. This is possible through Natural Language Generation (NLG). The field of auditing has not previously been applied to this pipeline. This research looks at the auditing of Investment Fund Management, of which a specific procedure is the comparison of two time series (one of the fund being tested and another of the benchmark it is supposed to follow) to identify potential misstatements in the investment fund. We solve this problem through a combination of incremental innovations on existing techniques in the text planning stage as well as pre-NLG processing steps, with effective leveraging of accepted sentence planning and realisation techniques. Additionally, fuzzy logic is used to provide a more human decision system. This allows the system to transform data into information and then into text. This has been evaluated by experts and achieved positive results with regard to audit impact, readability and understandability, while falling slight short of the stated accuracy targets. These preliminary results are positive in general and are therefore encouraging for further development. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/29643 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:26.116Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/29643 Data to information to text summaries of financial data Kyle, Cameron Keet, Maria Computer Science The field of auditing is becoming increasingly dependent on information technology as auditors are forced to follow the increasingly complex information processing of their clients. There exists a need for a system that can convert vast quantities of data generated by existing systems and data analytics techniques, into usable information and then into a format that is easy for someone not trained in data analytics to understand. This is possible through Natural Language Generation (NLG). The field of auditing has not previously been applied to this pipeline. This research looks at the auditing of Investment Fund Management, of which a specific procedure is the comparison of two time series (one of the fund being tested and another of the benchmark it is supposed to follow) to identify potential misstatements in the investment fund. We solve this problem through a combination of incremental innovations on existing techniques in the text planning stage as well as pre-NLG processing steps, with effective leveraging of accepted sentence planning and realisation techniques. Additionally, fuzzy logic is used to provide a more human decision system. This allows the system to transform data into information and then into text. This has been evaluated by experts and achieved positive results with regard to audit impact, readability and understandability, while falling slight short of the stated accuracy targets. These preliminary results are positive in general and are therefore encouraging for further development. 2019-02-18T11:37:21Z 2019-02-18T11:37:21Z 2018 2019-02-13T11:51:48Z Master Thesis Masters MSc http://hdl.handle.net/11427/29643 eng application/pdf Department of Computer Science Faculty of Science University of Cape Town |
| spellingShingle | Computer Science Kyle, Cameron Data to information to text summaries of financial data |
| thesis_degree_str | Master's |
| title | Data to information to text summaries of financial data |
| title_full | Data to information to text summaries of financial data |
| title_fullStr | Data to information to text summaries of financial data |
| title_full_unstemmed | Data to information to text summaries of financial data |
| title_short | Data to information to text summaries of financial data |
| title_sort | data to information to text summaries of financial data |
| topic | Computer Science |
| url | http://hdl.handle.net/11427/29643 |
| work_keys_str_mv | AT kylecameron datatoinformationtotextsummariesoffinancialdata |