Audit data analytics raises audit quality and fraud detection in West Africa when firms combine IT readiness, trained auditors and supportive institutions; patchy infrastructure, limited analytics skills and fragmented regulation, however, keep adoption low and uneven.

Main Finding

Audit Data Analytics (ADA) adoption in West Africa is driven by the joint alignment of technological readiness, auditor competencies, and supportive institutional environments. Where these elements align, ADA improves audit quality, fraud detection, risk assessment and operational efficiency. However, adoption is uneven and constrained by infrastructure, skills gaps, regulatory fragmentation and organisational culture.

Key Points

• Four recurrent themes shape ADA adoption:
  • Technological infrastructure and organisational capacity (IT systems, data availability, investment).
  • Auditor competencies, human capital and organisational culture (skills, training, resistance to change).
  • Regulatory and institutional influences (standards, professional bodies, legal environment).
  • Effects on audit outcomes (audit quality, fraud detection, efficiency, value creation).
• ADA adoption produces measurable gains in:
  • Audit quality (more thorough evidence, improved risk assessments).
  • Fraud detection and early warning capabilities.
  • Operational efficiency (automation of routine tasks, time savings).
• Major barriers include unreliable digital infrastructure, limited in‑house analytics skills, lack of clear regulatory guidance, and cultural resistance within firms.
• Successful implementation requires coordinated investments in IT, continuous professional development, regulatory clarity, and change management.
• The study integrates the Technology–Organization–Environment (TOE) framework with institutional theory to form a contextually grounded conceptual model for ADA diffusion in West Africa.
• Evidence base: systematic review yielded 23 studies (2015–2025), indicating the literature is still limited and fragmented in the region.

Data & Methods

• Methodological approach: systematic literature review following PRISMA 2020 guidelines.
• Search scope: peer‑reviewed and grey literature from 2015–2025 across Scopus, Web of Science, ScienceDirect, Emerald Insight, EBSCOhost and Google Scholar.
• Screening and appraisal: multi‑stage screening, eligibility assessment and quality appraisal reduced identified records to 23 studies included in the synthesis.
• Synthesis method: thematic analysis to identify recurring patterns and to develop an integrated conceptual framework combining TOE and institutional theory.
• Geographic focus: West African auditing context; studies vary in methodological design (qualitative case studies, surveys, conceptual papers), limiting meta‑analytic quantitative aggregation.

Implications for AI Economics

• Productivity and value creation:
  • ADA is a concrete instance of AI/data analytics raising productivity in professional services by automating routine tasks and enabling higher‑value audit judgments; this supports models of AI complementarity with skilled labor rather than pure substitution.
  • Improved fraud detection reduces information asymmetry, potentially lowering cost of capital and increasing market efficiency—relevant for models of financial intermediation and market discipline.
• Labor market effects:
  • Demand shifts toward analytics, data science and soft skills (interpretation, judgement). Research should quantify skill‑biased technical change in accountancy labor markets and wage premia for analytics skills.
  • Short‑to‑medium term, ADA may reallocate tasks within firms (task polarization), increasing demand for high‑skill auditors while reducing lower‑skill routine audit tasks.
• Adoption economics and diffusion:
  • TOE + institutional framing suggests adoption depends on firm capabilities, external environment (regulation, clients’ expectations), and normative pressures—useful for modeling heterogeneous adoption across firms and countries.
  • Barriers (infrastructure, training, regulation) imply high fixed costs and coordination failures; policy interventions (subsidies for training, shared infrastructure, standardized guidance) could improve social returns.
• Market structure and competition:
  • Early adopters may gain competitive advantage via higher audit quality and efficiency; this can affect market shares among large firms and client switching costs—important for industrial organization analyses of the audit market.
• Policy and regulation:
  • Regulatory clarity and professional standards amplify adoption and value capture. Economists should study optimal regulation that balances audit innovation with accountability and data privacy.
• Research directions & empirical strategies:
  • Need for causal impact studies: difference‑in‑differences, firm‑level panel analyses, and field experiments to estimate ADA’s effects on audit quality, cost, client outcomes (e.g., restatements, detection of fraud), and labor outcomes.
  • Cost–benefit and ROI studies for ADA investments in resource‑constrained settings.
  • Macro‑level work on how ADA diffusion affects financial market quality, investor confidence, and capital allocation in emerging economies.
  • Incorporate institutional heterogeneity in cross‑country models to explain varying adoption trajectories.
• Implication for training and human capital policy:
  • Investments in analytics education and continuous professional development are high‑return; economic evaluations should compare private versus public financing of such training given positive externalities (reduced fraud, improved market trust).

Summary recommendation for researchers and policymakers: treat ADA as an economically meaningful AI/analytics intervention whose adoption and impacts are shaped by infrastructure, skills, and institutions. Prioritize causal evaluation, targeted human capital investments, and regulatory frameworks that lower adoption costs while safeguarding audit integrity.