BIg data: https://drive.google.com/drive/folders/1OyH5H8lej6lbjQf0i4OyQp3taWodD6WL
Please see the following requirements:
Summarize University Rating Systems and Carnegie Rating systems to describe why and how individuals/organizations might manipulate this type of data. What types of organizations would be most likely to fraudulent report data (based off of incentives). Also, describe where this type of fraud fits into the ACFE’s Fraud Tree.
Document 3-5 reporting frauds that you think would be relevant to this data set. Use this section to serve as the base as your hypothesis development, below.
Use your knowledge and apply the fraud theory approach to search for fraud in a set of thousands of real estate transactions using real data. According to the ACFE Fraud Examiner’s manual, this includes:
Analyzing available data
Creating a hypothesis
Testing the hypothesis
Refining and amending the hypothesis.
Overall, within your data analysis and hypothesis testing, you should:
Describe data (~1/2 page).
Describe how you cleaned data (e.g. inconsistent dates, missing records, improper formatting) using either Tableau, Excel, Idea, Alteryx, or Azure Machine Learning (~1/2 page). Please feel free to use any technology you are comfortable using, Python, Excel, Idea, Tableau, Alteryx, or others.
Analysis should incorporate descriptive data analysis (~1/2 page) (Means, medians, std, box and whiskers, correlations, etc.).
You may wish to develop expectations of what other variables in the data set you think would move with (correlate with) the variables which are most likely to be manipulated.
Describe how you initially tested your hypothesis. (~1 page)
At least one Data Visualizalition. Also, please include visuals that would help demonstrate the fraud to a lay person.
At least one Mathematical Analysis (e.g. Regressions, Correlation, Means and Standard Deviations, Benford’s Law, etc.)
At least one Machine Learning Analysis (from what you are learning in Dr. Kipp’s class).
Refining and amending the hypothesis and subsequent tests (~1 page)
6)Afterward, you document your results highlighting the top universities you suspect as being suspicious.
Big Data:
https://drive.google.com/drive/folders/1OyH5H8lej6lbjQf0i4OyQp3taWodD6WL
