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Financial Audit Using Data Analytics

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Resumo:This thesis investigates the integration of data analytics into financial auditing, highlighting its potential to revolutionize traditional audit methodologies. Through an extensive literature review, the study examines the role of data analytics in enhancing the accuracy, efficiency, and comprehensiveness of financial audit. The methodology involves applying both supervised and unsupervised machine learning algorithms to a general ledger from a steel industry company, focusing on anomaly detection and fraud identification. The results underscore the significant advantages of data analytics over conventional auditing techniques, including the ability to analyze entire datasets, identify intricate patterns, and detect anomalies with higher precision. This study also addresses the implications for the auditing profession, emphasizing the need for auditors to acquire data science skills and adapt to technological advancements. Additionally, the research discusses the challenges and limitations associated with data analytics in auditing, such as data integrity and model interpretability. The thesis concludes by advocating for continuous professional development and the integration of advanced analytical tools to enhance the integrity and accuracy of financial reporting in an increasingly complex and digitalized financial environment.
Autores principais:Nouri, Zied
Assunto:Financial Audit Data Analytics Fraud Detection Machine Learning Regulatory Compliance Data-Driven Audit SDG 9 - Industry, innovation and infrastructure SDG 16 - Peace, justice and strong institutions
Ano:2024
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

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