Detecting Anomalies in Financial Data Using Machine Learning Algorithms
Bookkeeping data free of fraud and errors are a cornerstone of legitimate business operations. The highly complex and laborious work of financial auditors calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learni...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
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| Series: | Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-8954/10/5/130 |
| _version_ | 1797469772384829440 |
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| author | Alexander Bakumenko Ahmed Elragal |
| author_facet | Alexander Bakumenko Ahmed Elragal |
| author_sort | Alexander Bakumenko |
| collection | DOAJ |
| description | Bookkeeping data free of fraud and errors are a cornerstone of legitimate business operations. The highly complex and laborious work of financial auditors calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learning (ML) techniques nowadays are being successfully applied to detect fraud and anomalies in data. In accounting, it is a long-established problem to detect financial misstatements deemed anomalous in general ledger (GL) data. Currently, widely used techniques such as random sampling and manual assessment of bookkeeping rules become challenging and unreliable due to increasing data volumes and unknown fraudulent patterns. To address the sampling risk and financial audit inefficiency, we applied seven supervised ML techniques inclusive of deep learning and two unsupervised ML techniques such as isolation forest and autoencoders. We trained and evaluated our models on a real-life GL dataset and used data vectorization to resolve journal entry size variability. The evaluation results showed that the best trained supervised and unsupervised models have high potential in detecting predefined anomaly types as well as in efficiently sampling data to discern higher-risk journal entries. Based on our findings, we discussed possible practical implications of the resulting solutions in the accounting and auditing contexts. |
| first_indexed | 2024-03-09T19:25:53Z |
| format | Article |
| id | doaj.art-7d8648675deb46b39cd974ee2e9dc350 |
| institution | Directory Open Access Journal |
| issn | 2079-8954 |
| language | English |
| last_indexed | 2024-03-09T19:25:53Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj.art-7d8648675deb46b39cd974ee2e9dc3502023-11-24T02:54:39ZengMDPI AGSystems2079-89542022-08-0110513010.3390/systems10050130Detecting Anomalies in Financial Data Using Machine Learning AlgorithmsAlexander Bakumenko0Ahmed Elragal1Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, SE 971 87 Luleå, SwedenDepartment of Computer Science, Electrical and Space Engineering, Luleå University of Technology, SE 971 87 Luleå, SwedenBookkeeping data free of fraud and errors are a cornerstone of legitimate business operations. The highly complex and laborious work of financial auditors calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learning (ML) techniques nowadays are being successfully applied to detect fraud and anomalies in data. In accounting, it is a long-established problem to detect financial misstatements deemed anomalous in general ledger (GL) data. Currently, widely used techniques such as random sampling and manual assessment of bookkeeping rules become challenging and unreliable due to increasing data volumes and unknown fraudulent patterns. To address the sampling risk and financial audit inefficiency, we applied seven supervised ML techniques inclusive of deep learning and two unsupervised ML techniques such as isolation forest and autoencoders. We trained and evaluated our models on a real-life GL dataset and used data vectorization to resolve journal entry size variability. The evaluation results showed that the best trained supervised and unsupervised models have high potential in detecting predefined anomaly types as well as in efficiently sampling data to discern higher-risk journal entries. Based on our findings, we discussed possible practical implications of the resulting solutions in the accounting and auditing contexts.https://www.mdpi.com/2079-8954/10/5/130general ledgeraccountingauditinganomaly detectionmachine learning |
| spellingShingle | Alexander Bakumenko Ahmed Elragal Detecting Anomalies in Financial Data Using Machine Learning Algorithms Systems general ledger accounting auditing anomaly detection machine learning |
| title | Detecting Anomalies in Financial Data Using Machine Learning Algorithms |
| title_full | Detecting Anomalies in Financial Data Using Machine Learning Algorithms |
| title_fullStr | Detecting Anomalies in Financial Data Using Machine Learning Algorithms |
| title_full_unstemmed | Detecting Anomalies in Financial Data Using Machine Learning Algorithms |
| title_short | Detecting Anomalies in Financial Data Using Machine Learning Algorithms |
| title_sort | detecting anomalies in financial data using machine learning algorithms |
| topic | general ledger accounting auditing anomaly detection machine learning |
| url | https://www.mdpi.com/2079-8954/10/5/130 |
| work_keys_str_mv | AT alexanderbakumenko detectinganomaliesinfinancialdatausingmachinelearningalgorithms AT ahmedelragal detectinganomaliesinfinancialdatausingmachinelearningalgorithms |