ASSESSING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON FINANCIAL AUDITING AND RISK ASSESSMENT

Abstract

Background

The rapid advancement of Artificial Intelligence (AI) has profoundly impacted various industries, including financial auditing and risk assessment. Traditional auditing practices, often labor-intensive and time-consuming, have struggled to keep pace with the increasing complexity and volume of financial transactions in a globalized economy. The adoption of AI technologies, such as machine learning and predictive analytics, offers new opportunities to enhance efficiency, accuracy, and strategic decision-making in auditing. However, this transformative shift also introduces challenges, including ethical concerns, algorithmic biases, and regulatory gaps, which must be addressed to ensure responsible AI integration.

Aims

This study aims to:

1. Explore the transformative impact of AI on financial auditing, particularly in terms of efficiency and accuracy.
2. Investigate the role of AI in enhancing fraud detection and risk management.
3. Identify the regulatory and ethical challenges associated with AI adoption in auditing.
4. Provide actionable recommendations to maximize the benefits of AI while mitigating associated risks.

Research Method

The study employs a mixed-methods approach, combining quantitative and qualitative data collection techniques. Surveys were conducted with auditors and financial professionals to assess their experiences and perceptions of AI tools in auditing. Semi-structured interviews provided deeper insights into the practical applications, benefits, and challenges of AI integration. Secondary data from academic journals, case studies, and industry reports complemented the primary data, offering a comprehensive understanding of AI’s impact on financial auditing.

Results and Conclusion

The findings indicate that AI significantly enhances the efficiency and accuracy of financial auditing by automating routine tasks, enabling real-time data analysis, and improving fraud detection. Predictive analytics also allows organizations to proactively identify and mitigate risks. However, challenges such as regulatory gaps, algorithmic biases, and transparency issues remain critical barriers to AI adoption. The study concludes that while AI offers transformative potential, its successful integration requires robust governance frameworks, continuous training for auditors, and collaboration among industry stakeholders to address ethical and regulatory concerns.

Contribution

This study contributes to the academic discourse on AI in financial auditing by providing empirical evidence of its benefits and challenges. It offers practical recommendations for auditors, regulators, and organizations to responsibly integrate AI, balancing innovation with accountability. By bridging the gap between theoretical knowledge and real-world applications, this research provides a roadmap for leveraging AI to improve financial auditing practices.