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- All Subjects: Supply Chain
- All Subjects: Analytics
- Creators: Department of Information Systems
The next question: What do these changes in the roles and responsibilities look like for the auditors of the future? Cognitive technology will assuredly present new issues for which humans will have to find solutions.
• How will humans be able to test the accuracy and completeness of the decisions derived by cognitive systems?
• If cognitive computing systems rely on supervised learning, what is the most effective way to train systems?
• How will cognitive computing fair in an industry that experiences ever-changing industry regulations?
• Will cognitive technology enhance the quality of audits?
In order to answer these questions and many more, I plan on examining how cognitive technologies evolved into their use today. Based on this historic trajectory, stakeholder interviews, and industry research, I will forecast what auditing jobs may look like in the near future taking into account rapid advances in cognitive computing.
The conclusions forecast a future in auditing that is much more accurate, timely, and pleasant. Cognitive technologies allow auditors to test entire populations of transactions, to tackle audit issues on a more continuous basis, to alleviate the overload of work that occurs after fiscal year-end, and to focus on client interaction.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
Home advantage affects the game in almost all team sports across the world. Due to<br/>COVID and all of the precautions being taken to keep games played, more extensive research is able to be conducted about what factors truly go into creating a home advantage. Some common factors of home advantage include the crowd, facility familiarity, and travel. In the English Premier League, there are no fans allowed at any of the games; furthermore, in the NBA, a bubble was created at one neutral venue with no fans in attendance. Even with the NBA being at a neutral site, there was still a “home team” at every game. The sports betting industry struggled due to failing to shift betting lines in accordance with this decreased home advantage. With these leagues removing some of the factors that are frequently associated with home advantage, analysts are able to better see what the results would be of removing these variables. The purpose of this research is to determine if these adjustments made due to COVID had an impact on the home advantage in different leagues around the world, and if they did, to what extent. Individual game data from the past 10 seasons were used for analysis of both the NBA and the Premier League. The results show that there is a significant difference in win percentage between prior seasons and seasons behind closed doors. In addition to win percentage, many other game statistics see a significant shift as well. Overall, the significance of being the home team disappears in games following the COVID-19 break.