Ethics in the World of AI: An Accountant's Guide to Managing the Risks

CPE: 2.0

Learning Objectives:

• Recall strategies for responsible AI development and governance in accounting and finance
• Identify the challenges of maintaining ethical standards while using generative AI tools
• Recognize the impact of data bias on the outputs generated by generative AI models in accounting and finance.

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Data Processing and Analysis Certificate

CPE: 14.0

Learning Objectives:

• Recall the definition and purpose of requirements gathering, data profiling and collection, data cleansing, and data mining and modeling.
• Recognize how requirements gathering, data profiling and collection, data cleansing, and data mining and modeling fit in a data analytics project.
• Recall the steps to follow when gathering requirements and profiling, collecting, cleansing, mining, and modeling data.
• Apply techniques learned to common data processing and analysis scenarios.
• Identify risks and considerations associated with requirements gathering and the profiling, collection, cleansing, mining, and modeling of data.

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Finance Transformation: Manage Disruption Certificate

CPE: 7.5

Learning Objectives:

• Identify disruptive factors within the business environment and its impact on the finance function.
• Recognize how the role of finance is evolving and the resulting new skills and competencies required.
• Identify opportunities for your Finance team to contribute to value creation for your organization.

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Fundamentals of ESG Certificate

CPE: 6.0

Learning Objectives:

• Recognize the importance of sustainability and ESG in today's business environment.
• Recognize the three pillars of ESG.
• Identify key factors influencing the current sustainability reporting landscape.

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Data Visualization and Communication Certificate

CPE: 11.0

Learning Objectives:

• Recall data visualization best practices.
• Identify the strengths and weaknesses of commonly used data visualization tools.
• Recognize how to use critical thinking to add value to a data analytics project.
• Recall best practices when communicating in different formats and to different audiences.
• Recognize the importance of storytelling in communication.

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Critical Thinking in Data Analytics

CPE: 2.5

Learning Objectives:

• Recall the definition of critical thinking and its uses in data analytics.
• Recognize the key components of critical thinking in a data analytics process.
• Apply critical thinking skills throughout a data analytics project to meet organizational needs.
• Recognize how critical thinking can be used to address organizational objectives.
• Identify how to use critical thinking to improve data profiling and collection.
• Identify how to use critical thinking to analyze data and deliver insights.

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Data Mining and Modeling

CPE: 3.5

Learning Objectives:

• Distinguish between data mining and related terms.
• Recognize the data analytics framework’s role in a data mining project.
• Identify the goals and benefits of data mining.
• Recognize the tasks associated with building a data mining model.
• Identify the concept of clustering as a descriptive data mining technique.
• Identify the appropriate data mining techniques and algorithms for predictive analytics.
• Recognize how clustering techniques can be applied in practice.
• Recognize how regression techniques can be applied in practice.
• Recognize tools and programs that can be used for data mining and modeling.

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U.S. International Tax Certificate

CPE: 51.5

Learning Objectives:

• Distinguish between various types of global tax systems and certain characteristics of each.
• Identify the key actions under the Organisation for Economic Cooperation and Development (OECD) base erosion and profit-shifting (BEPS) initiative.
• Recall the basics of the anti-deferral provisions applicable to controlled foreign corporations.
• Identify a controlled foreign corporation (CFC) and a U.S. shareholder.
• Recall the general foreign tax credit (FTC) rules.
• Identify a qualified business unit (QBU) under Section 989.
• Identify the accepted transfer pricing methods and recognize when each is appropriate to use.
• Identify the tax consequences of an exit event, such as liquidation, related-party sale, and third-party sale (with availability of certain elections).

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