Artificial Intelligence and Machine Learning in the Transformation of Forensic Accounting: A Systematic Literature Review
- 1Faculty of Accounting, Economic, and Administrative Sciences, Universidad del Cauca, Popayán 190001, Colombia
- 2Faculty of Business Sciences, Corporación Universitaria Comfacauca-Unicomfacauca, Popayán 190001, Colombia
- 3Faculty of Administrative and Accounting Science, Universidad Cooperativa de Colombia, Barrancabermeja 687031, Colombia
Abstract
This research analyzed the integration of artificial intelligence (AI) and machine learning in forensic accounting as tools for the detection of accounting fraud. Through a systematic review of the literature in Scopus under the PRISMA protocol, 76 documents published between 2019 and 2026 were selected and analyzed. The results show that supervised machine learning techniques, assembly methods, deep learning and natural language processing have demonstrated superior capacity to rule-based approaches to detect fraudulent patterns, when quality data and professionals capable of interpreting their results with forensic criteria are available. A recurring finding throughout the corpus is that machine learning models tend to perform better than rule-based approaches in contexts where sufficient quality data is available. Although this advantage is not universal, it depends on the type of fraud analyzed, the design of the model, the evaluation metrics used, and the institutional context of implementation. AI expands the practitioner’s ability to identify anomalous patterns in volumes of information that manual analysis cannot cover. However, significant barriers persist related to data quality, model interpretability, professional resistance to technological updating, and the absence of regulatory frameworks that grant evidentiary validity to algorithmic results. Latin America and Africa show a low investigative production in this field, despite concentrating high levels of vulnerability to financial fraud. The study concludes that the transformation of forensic accounting using AI is a social, institutional and educational process, not exclusively technical, which requires interdisciplinary training, data governance and specific ethical frameworks for the forensic field.
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BibTeX
@article{ceron2026artificial,
title = {{Artificial Intelligence and Machine Learning in the Transformation of Forensic Accounting: A Systematic Literature Review}},
author = {Luís Ángel Meneses Cerón and Eliana Fernández and VICTORIA EUGENIA PINO TERÁN and María Alejandra Albán Lara and Rosa Vega Sánchez and Jhon Jairo Fuentes Sánchez},
journal = {Journal of risk and financial management},
year = {2026},
month = jul,
doi = {10.3390/jrfm19070519},
url = {https://doi.org/10.3390/jrfm19070519},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial Intelligence and Machine Learning in the Transformation of Forensic Accounting: A Systematic Literature Review
AU - Luís Ángel Meneses Cerón
AU - Eliana Fernández
AU - VICTORIA EUGENIA PINO TERÁN
AU - María Alejandra Albán Lara
AU - Rosa Vega Sánchez
AU - Jhon Jairo Fuentes Sánchez
PY - 2026
DA - 2026/07/10
JO - Journal of risk and financial management
DO - 10.3390/jrfm19070519
UR - https://doi.org/10.3390/jrfm19070519
AB - This research analyzed the integration of artificial intelligence (AI) and machine learning in forensic accounting as tools for the detection of accounting fraud. Through a systematic review of the literature in Scopus under the PRISMA protocol, 76 documents published between 2019 and 2026 were selected and analyzed. The results show that supervised machine learning techniques, assembly methods, deep learning and natural language processing have demonstrated superior capacity to rule-based approaches to detect fraudulent patterns, when quality data and professionals capable of interpreting their results with forensic criteria are available. A recurring finding throughout the corpus is that machine learning models tend to perform better than rule-based approaches in contexts where sufficient quality data is available. Although this advantage is not universal, it depends on the type of fraud analyzed, the design of the model, the evaluation metrics used, and the institutional context of implementation. AI expands the practitioner’s ability to identify anomalous patterns in volumes of information that manual analysis cannot cover. However, significant barriers persist related to data quality, model interpretability, professional resistance to technological updating, and the absence of regulatory frameworks that grant evidentiary validity to algorithmic results. Latin America and Africa show a low investigative production in this field, despite concentrating high levels of vulnerability to financial fraud. The study concludes that the transformation of forensic accounting using AI is a social, institutional and educational process, not exclusively technical, which requires interdisciplinary training, data governance and specific ethical frameworks for the forensic field.
SN - 1911-8066
SN - 1911-8074
ER -