Scenario: Identify a business that is of interest to you. It can be real or fictitious, but it should be a realistic business. Think of one specific risk that your chosen business could face, and consider how and where you will get data that can be analyzed to address that risk.
Complete the following steps:
2. Write an introduction to your chosen topic. Describe the business that you have chosen, identify the risk you selected, and briefly describe how you expect analytics to help you address that risk. Label this section Introduction.
3. Perform a SWOT analysis for your business. Ensure that you have at least one element with a description in each of the four quadrants of a SWOT table. Label this section SWOT Analysis.
4. Identify which quadrant of a risk matrix your selected risk would fall into (High Probability/High Impact; High Probability/Low Impact; Low Probability/High Impact; or Low Probability/Low Impact. Provide a rationale explaining why you have classified your chosen risk in the way you did. Label this section Risk Classification.
5. Obtain or create a data set that you can use to address your risk. You can use built in data sets in R, data sets from the Internet, or data sets from a company with appropriate permission. It is acceptable to create your own data set if you wish. Keep in mind that you will need to use one of the risk analytics techniques you have learned in this class, so the data you use and the outcome your produce must be aligned with a valid technique “Expected Payoff” Please see attached template. Make a screen capture of a representative sample of your data. Place it in your document and label this section Data.
6. Create a model for your data that will allow you to address your selected risk. This will be in Excel for the Expected Payoff as your technique. In your Word document, provide evidence by way of labeled screen captures and written descriptions of your model building. Label this section Modeling.
7. For Expected Payoff, document your findings for your audience. In your Word document, provide evidence by way of labeled screen captures and written descriptions of your model application/outcomes. Label this section Outcomes.
8. Write a conclusion that summarizes what you did to assess and analyze risk using data. Discuss legal and/or ethical considerations that your chosen business must consider and address in using your risk analytics approach. Label this section Conclusion.
9. Include a References page that cites all sources used to complete your Unit 6 Assignment. The list of references must include no fewer than five authoritative and relevant sources beyond the class textbook and sources of data. Ensure that all of your cited sources are referred to parenthetically throughout the body of your paper where relevant, and then in APA format on the References page.
I’m interested in analyzing the risk of fraudulent activity for a fictitious online grocery delivery business called “Fresh to Your Door.” Fresh to Your Door offers a convenient and affordable way for customers to get their groceries delivered straight to their doorstep. However, the online nature of the business makes it susceptible to fraudulent orders, which can lead to lost revenue and damage the company’s reputation.
I believe that data analytics can be a powerful tool in identifying patterns and red flags associated with fraudulent activity, allowing Fresh to Your Door to proactively prevent such incidents and minimize potential losses. By analyzing data on past orders, customer profiles, and payment information, we can develop a model that predicts the likelihood of fraud and flags suspicious orders for further review.
Strengths:
Weaknesses:
Opportunities:
Threats:
I classify the risk of fraudulent activity for Fresh to Your Door as High Probability/High Impact. While individual fraudulent orders may not represent a significant financial loss, the potential for frequent or large-scale fraud could severely impact the business’s profitability and reputation. The online nature of the business makes it particularly vulnerable to this type of risk.
To address the risk of fraudulent activity, I would utilize a dataset containing information on past orders, including:
This data could be obtained from Fresh to Your Door’s internal records or supplemented with external data sources such as IP geolocation databases or fraud prevention services.
Data Sample:
Order ID | Customer ID | Age | Location | Date/Time | Items | Amount | Delivery Address | Payment Method | Flags |
---|---|---|---|---|---|---|---|---|---|
12345 | ABC123 | 35 | City A | 10/26/2023, 12:00 PM | Bread, milk, eggs | $20 | 123 Main St. | Credit Card | None |
67890 | DEF456 | 22 | City B | 10/27/2023, 8:00 PM | High-end electronics | $1,500 | 456 Fake St. | Debit Card | Multiple billing addresses |
23456 | GHI789 | 50 | City A | 10/28/2023, 5:00 AM | Large order of groceries | $300 | 123 Main St. (different apartment) | Prepaid card | Rush delivery reques |