Artificial intelligence (AI) has significantly transformed fraud detection in accounting by offering advanced analytical capabilities to address the increasing complexity and volume of financial data. Traditional methods, such as manual audits and rule-based systems, are limited in their effectiveness against sophisticated fraud schemes. AI techniques, including machine learning (ML), natural language processing (NLP), and data mining, provide powerful tools for identifying patterns, anomalies, and hidden relationships in financial data. Supervised learning models are trained on labeled datasets to distinguish between fraudulent and legitimate transactions, while unsupervised learning detects anomalies without prior labeling. NLP is used to analyze textual data, such as emails and financial documents, to uncover suspicious activities. Data mining techniques help extract useful information from large datasets, identifying trends and patterns that may indicate fraud. Several case studies demonstrate the practical applications of AI in fraud detection. Financial institutions have implemented ML algorithms to detect credit card fraud in real-time, significantly reducing losses. A multinational corporation used NLP and data mining to uncover a complex fraud scheme during internal audits. Government agencies have employed AI to detect procurement fraud, saving millions of dollars and improving transparency. These examples highlight the effectiveness of AI in enhancing fraud detection, improving accuracy, and reducing false positives and negatives. AI-driven fraud detection offers numerous benefits, including increased accuracy, enhanced efficiency, and proactive fraud prevention through predictive analytics. However, challenges such as data quality, ethical concerns, and integration with existing systems must be addressed. The future of AI in fraud detection is promising, with advancements in machine learning, NLP, and blockchain technology expected to further enhance fraud detection capabilities. The integration of AI with emerging technologies will improve security, transparency, and compliance in financial transactions, ultimately strengthening the integrity of financial systems and promoting trust among stakeholders.Artificial intelligence (AI) has significantly transformed fraud detection in accounting by offering advanced analytical capabilities to address the increasing complexity and volume of financial data. Traditional methods, such as manual audits and rule-based systems, are limited in their effectiveness against sophisticated fraud schemes. AI techniques, including machine learning (ML), natural language processing (NLP), and data mining, provide powerful tools for identifying patterns, anomalies, and hidden relationships in financial data. Supervised learning models are trained on labeled datasets to distinguish between fraudulent and legitimate transactions, while unsupervised learning detects anomalies without prior labeling. NLP is used to analyze textual data, such as emails and financial documents, to uncover suspicious activities. Data mining techniques help extract useful information from large datasets, identifying trends and patterns that may indicate fraud. Several case studies demonstrate the practical applications of AI in fraud detection. Financial institutions have implemented ML algorithms to detect credit card fraud in real-time, significantly reducing losses. A multinational corporation used NLP and data mining to uncover a complex fraud scheme during internal audits. Government agencies have employed AI to detect procurement fraud, saving millions of dollars and improving transparency. These examples highlight the effectiveness of AI in enhancing fraud detection, improving accuracy, and reducing false positives and negatives. AI-driven fraud detection offers numerous benefits, including increased accuracy, enhanced efficiency, and proactive fraud prevention through predictive analytics. However, challenges such as data quality, ethical concerns, and integration with existing systems must be addressed. The future of AI in fraud detection is promising, with advancements in machine learning, NLP, and blockchain technology expected to further enhance fraud detection capabilities. The integration of AI with emerging technologies will improve security, transparency, and compliance in financial transactions, ultimately strengthening the integrity of financial systems and promoting trust among stakeholders.