BUS 4575 - Accounting Analytics and Artificial Intelligence

• Understand how financial data and non-financial data interact to forecast events, optimize operations, and determine strategy
• Make better business decisions about the emerging roles of accounting analytics and artificial intelligence
• Apply methods learned to make create strategy using financial data
• Use tools common for artificial intelligence focusing on techniques used in accounting and finance
• Understand the application of machine learning and deep neural networks to discovery of new knowledge and information

• None

• How data analytics is important to accountants
• Sources of accounting data and data storage
• Financial reporting and analysis: slicing and dicing, queries and reports
• Data visualizations and data warehouse, data mining
• Auditing analytics
• Fraud detection
• Modes for accounting decision making
• Computational intelligence, interpretation and evaluation of results
• AI and accounting: history and use of AI in accounting.  
• Supervised learning training set: all the algorithms are tested
• Train the system with the correct answers. Do we have adequate data sets of training models to make the decision? Do we have enough rows of data to get the right answer for the output? Test sets to determine if the AI should be deployed
• Machine learning: tabular data
• Unsupervised learning: cluster data
• Deep learning, sensor data, computer vision (self-driving cars with camera data) natural language processing, conversational interfaces (microphone data) 
• Use deep learning to use tabular data with a predictive model.
• Full life cycle project: get data, clean, generate decision models