INTEGRATING AI ALGORITHMS INTO FINANCIAL AUDITS: A DISRUPTIVE APPROACH TO FRAUD DETECTION

Abstract

A systematic analysis of the scientific literature related to the incorporation of Artificial Intelligence in financial audits and its use in fraud identification was carried out, following the PRISMA methodology (Recommended Reporting Objects for Systematic Evaluations and Meta-Analysis). The main purpose of this study was to describe the publications registered in the Scopus and WoS databases, assessing their scope and relevance in the investigation of the suggested variables.

The initial search process detected 41 linked publications, which were examined and filtered using specific keywords: ARTIFICIAL INTELLIGENCE, FINANCIAL AUDITS and FRAUD. After eliminating duplicates and applying inclusion and exclusion criteria, a final corpus of 15 documents was established for exhaustive analysis. This set of articles facilitated the exploration of the most prominent trends in research, the most employed methods, and the growing fields in the use of artificial intelligence algorithms for the identification of financial anomalies.

The findings of this review provide a comprehensive perspective of scientific progress in this area, emphasizing both the possibilities and challenges associated with the implementation of these revolutionary technologies in the professional practice of auditing. Likewise, the analysis helps to understand how artificial intelligence can strengthen financial control and transparency systems, laying the foundations for future research and practical suggestions in this field.