Online Self-Study CPE for CPAs
This course provides an overview of leveraging AI for data analysis and reporting in accounting. It covers the automation of data extraction, cleansing, and normalization processes as well as how AI facilitates automated financial analysis and forecasting.
This course provides an overview of future trends in AI and software for CPAs. This includes a discussion of technologies such as RPA, blockchain, as well as other technologies like quantum computing, edge computing, and decentralized AI. This course also addresses the evolution of AI in accounting practices as well as how AI may impact jobs in the future.
This course explores the ethical and regulatory aspects of integrating AI in accounting. It covers ethical considerations like bias, transparency, and accountability, alongside regulatory frameworks such as data protection laws and professional standards.
This course provides an overview of some of the implementation strategies and considerations when integrating AI technologies within organizations. It addresses how you can assess readiness for AI adoption as well as integrating AI into current workflows.
This course provides an overview of the AI considerations with respect to tax preparation and tax research. Includes a discussion of how AI technology can be used in tax preparation, including automated data collection and tax optimization strategies.
An overview of the basics of AI, including the key concepts related to AI such as machine learning, deep learning, and Natural Language Processing.
An overview of basic fringe benefits that examines their mechanics, compensation methods, valuation, withholding, and accounting. Achievement awards, term life insurance, medical reimbursement, meals & lodging, educational assistance, dependent care, employer-provided automobiles, interest-free loans, etc. are discussed. Course No. M360 Format: Online pdf (90 pages). Printed book available. Instructional Delivery Method: QAS Self-Study Prerequisites: General […]
AI can play a significant role in audit planning and risk assessment by leveraging advanced data analytics to analyze vast amounts of financial and non-financial data. This allows auditors to identify potential risks more effectively, including fraud, errors, and compliance issues. By employing machine learning algorithms, AI can identify patterns and anomalies indicative of risk factors, enabling auditors to set materiality thresholds based on historical data and industry benchmarks.
This course provides an overview of the accounting requirements with respect to accounting changes and error corrections and the reporting implications within an entity’s financial statements.
This course provides an overview of Artificial Intelligence (AI) in accounting with a focus on its significance and evolution.