Financial Statement Fraud Detection: Synergy Artificial Intelligence with Auditor Characteristics

Kurniawati Kurniawati, Laura Silvany Hivianto

Abstract


The objective of this research is to analysis the effect of auditor characteristics that represented by professional scepticism and auditor experiences toward financial statement fraud detection through artificial intelligence in data analytic.  This study builds upon previous research where respondents were auditors who had used big data analytics and incorporate artificial intelligence, specifically big data analytic as mediator between auditor characteristics and the detection of financial statement fraud. Quantitative research was used in this research by obtaining primary data through 66 respondents from auditor at several public accounting firms in the Jakarta area. Our findings reveal that auditor experience has direct influence effect on financial statement fraud detection while professional scepticism has no effect. But surprisingly, the role of artificial intelligence in data analytics is only able to mediate professional scepticism on financial statement fraud detection, while this does not apply to auditor experience. This study provides empirical evidence that the role of artificial intelligence, namely big data analytic, can act as a catalyst that transforms professional scepticism from mere doubt to evidence-based action, by enhancing the auditor’s ability to identify data patterns and anomalies, reducing cognitive bias and increasing objectivity.

 


Keywords


Artificial intelligence, Auditor Experience, Big data analytics, Financial Statement Fraud; Professional skepticism

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References


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DOI: http://dx.doi.org/10.31000/bvaj.v9i1.14054

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