Data Analytics in Managerial Accounting Let’s see how we might use customer data to understand some simple data analytics. The purpose of this lab is to help you identify relevant questions that may be answered using data analytics.
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, you will:
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 bebetter 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.
Create a new word processing document and name the file "Lab 1-2 Data Analytics in Managerial Accounting Lab – [Your name] [Your email address]."
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 your three questions in your document.
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."
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.
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:
Attribute
Description
id
Loan identification number
member_id
Membership id
loan_amnt
Requested loan amount
emp_length
Employment length
issue_d
Date of loan issue
loan_status
Fully paid or charged off
pymnt_plan
Payment plan: yes or no
purpose
Loan purpose: e. g., wedding, medical, debt_consolidation, car
zip_code
Zip code
addr_state
State
dti
Debt-to-income ratio
delinq_2y
Late payments within the past two years
earliest_cr_line
Oldest credit account
inq_last_6mnths
Credit inquiries in the past 6 months
open_acc
Number of open credit accounts
revol_bal
Total balance of all credit accounts
revol_util
Percentage of available credit in use
total_acc
Total number of credit accounts
application_type
Individual or joint application
LAB TABLE 1-2A Names and Descriptions of Selected Data Attributes Collected by LendingClub
Page 33Evaluate 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.
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.
Save your document and submit to your instructor.