Enhancing Audit Quality and Reducing Costs: The Impact of AI in Banking and Financial Services

Amar Johri^1*Anu Sayal²Kim Mee Chong^2*Maysoon Khoja¹Chaithra N³Janhvi Jha³Neha Tyagi⁴

• ¹Saudi Electronic University, Riyadh, Saudi Arabia
• ²Taylor's University, Subang Jaya, Malaysia
• ³JAIN (Deemed-to-be University), Bengaluru, India
• ⁴Amity University Noida, Noida, India

By integrating automation and artificial intelligence (AI) into the auditing process, a revolutionary shift has been achieved in audit methodologies, auditor responsibilities, and audit quality. Machine learning algorithms and natural language processing are among the AI-driven tools that enable advanced anomaly detection, fraud identification, and predictive analytics. Auditors can respond proactively by identifying trends and risks earlier with the assistance of these features. Furthermore, AI-driven systems have the ability to search through vast amounts of data, both structured and unstructured, and identify patterns that would have been overlooked by conventional methods. The research demonstrates the potential of artificial intelligence (AI) tools, such as predictive modelling and machine learning, to improve anomaly detection, simplify resource allocation, and provide valuable insights through lead propensity analysis and company volume forecasting. AI-driven analytics achieved 87% recall in lead identification and 5% forecasting error in business volumes, explaining 94% of variance in actual loan disbursements and enabling complete dataset analysis versus traditional sampling approaches. This study heavily employs financial data to ensure accurate analysis while maintaining confidentiality. The findings demonstrate that AI is capable of analysing entire datasets, automating menial tasks, and identifying high-risk or high-value areas with a greater degree of precision than traditional sampling-based audits.