<![CDATA[Forensic auditing increasingly employs artificial intelligence (AI), yet practice faces data, transparency, and institutional-readiness gaps, especially in developing countries. This review conducts a Systematic Literature Review (2015–2025) across Scopus, Web of Science, and SINTA: 150 records screened, 30 included and thematically synthesized via manual coding. Findings answer the RQs through three pathways: anomaly detection in ledgers/transactions, text analysis of reports–claims–communications, and network analysis of supplier–contract relations, strengthened by RPA, immutable logging, and visual analytics; together these reduce false positives, speed investigations, and reinforce evidence auditability. Practically, we map use-cases to implementable transparency controls and propose a staged adoption roadmap for SAIs, anti-corruption agencies, and audit firms. Theoretically, we outline an ethics-regulatory adoption frame. Novelty: this review reframes AI as an epistemic instrument and introduces the Integrated Forensic-AI Transparency Stack (IFATS) to operationalize auditability beyond a finance-centric lens, with emphasis on developing-country contexts.]]>
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