AI Driven Systems for Improving Accounting Accuracy Fraud Detection and Financial Transparency

Abstract

The use of artificial intelligence (AI) in accounting and finance is reshaping how organizations ensure accuracy, detect fraud, and maintain transparency in their financial operations. This paper reviews how AI-driven technologies-particularly machine learning (ML), deep learning (DL), and natural language processing (NLP)-are being applied to modern accounting systems. We discuss how these tools enhance financial accuracy by automating data processing, identifying anomalies in real time, and correcting errors intelligently. Advanced fraud detection systems based on supervised and unsupervised learning, neural networks, and ensemble methods are shown to recognize suspicious transactions and accounting irregularities with remarkable precision. The paper also explores how AI supports transparency through automated compliance checks, smart auditing systems, and blockchain-based solutions that build trust and accountability. In addition, we highlight recent developments in predictive analytics for financial forecasting, robotic process automation (RPA) in accounting workflows, and explainable AI (XAI) for regulatory compliance. Key implementation challenges are addressed, including data quality, algorithmic bias, model interpretability, and evolving regulatory frameworks. The review further considers how AI integrates with enterprise resource planning (ERP) systems, safeguards sensitive financial data, and raises new ethical questions around automation and human oversight. Finally, we identify emerging directions such as federated learning for cross-organization fraud detection, graph neural networks for analyzing complex transaction patterns, and hybrid human-AI collaboration models. These advancements point toward a future where continuous auditing, multimodal financial analysis, and AI-driven regulatory technologies transform the landscape of accounting and financial management.