Reference no: EM132471094

Company summary

LendingClub is a U.S.-based, peer-to-peer lending company, headquartered in San Francisco, California. LendingClub facilitates both borrowing and lending by providing a platform for unsecured personal loans between $1,000 and $35,000. The loan period is for either 3 or 5 years. You have been brought in to help managers improve their loan application process.

Technique

• Some critical and creative thinking is helpful here. Software needed

• Word processor

In this lab:

Part 1: Identify appropriate questions and develop a hypothesis for each question.

Part 2: Identify fields and values in a database that are relevant to your questions.

Part 1: Identify the Questions LendingClub currently assigns a risk score to all loan applicants. This risk score is used to determine (1) whether a loan is accepted and (2) what interest rate approved loans will receive. The risk score has been used for the past 5 years, but LendingClub thinks there may be better ways to evaluate this given that the number of defaulted loans has increased in the past 2 years. It would like you to propose a model that would help it potentially assign a risk score to loan applicants.

Question 1. Create new word processing document and name the file "Lab 1-2 Data Analytics in Managerial Accounting Lab - [Your name] [Your email address]."

Question 2. Use what you know about loan risk (or search the web if you need a refresher) to identify three different questions that might influence risk. For example, if you suspect risky customers live in a certain location, your question might be "Where do the customers live?" Type three questions in the document.

Question 3. Next to each question, generate a hypothetical answer to each question to help you identify what your expected output would be. You may use some insight or intuition or search the Internet for ideas on how to inform your hypothesis. For example: "Hypothesis: Risky customers likely live in coastal towns."

Question 4. Finally, identify the data that you would need to answer each of your questions. For example, to determine customer location, you might need the city, state, and zip code. Additionally, if you hypothesize a specific region, you'd need to know which cities, state, and/or zip codes belong to that region. Add your required data sources to each question in your document.

Question 5. Save your document.

Part 2: Master the Data

To answer your questions, you'll need to evaluate specific data that LendingClub collects. It has provided a listing of fields that it collects in Table 1-2A:

1. Attribute Description
2. id Loan identification number
3. member_id Membership id
4. loan_amnt Requested loan amount
5. emp_length Employment length
6. issue_d Date of loan issue
7. loan_status Fully paid or charged off
8. pymnt_plan Payment plan: yes or no
9. purpose Loan purpose: e.g., wedding, medical, debt_consolidation, car
10. zip_code Zip code
11. addr_state State
12. dti Debt-to-income ratio
13. delinq_2y Late payments within the past two years
14. earliest_cr_line Oldest credit account
15. inq_last_6mnths Credit inquiries in the past 6 months
16. open_acc Number of open credit accounts
17. revol_bal Total balance of all credit accounts
18. revol_util Percentage of available credit in use
19. total_acc Total number of credit accounts
20. application_type Individual or joint application

Question 6. Evaluate each question from Part 1. Do the data you identified in your questions exist in the table provided? Write the applicable fields next to each question in your document.

Question 7. Are there data values you identified that don't exist in the table? Write where else you might look to collect the missing data or how you might suggest collecting those it.