Description

REQUEST FOR PROPOSAL
RFP #: IP – F1.H1 TITLE: BANKING INSURANCE PRODUCT – PHASE 1

Banking Insurance Product –
Phase 1: IP – F1.H1

Purpose
By responding to this Request for Proposal (RFP), the Proposer agrees that s/he has read and understood all documents within this RFP package.
Submission Details
Responders to this RFP should supply:
• A business report up to 4 pages (not including cover page, table of contents, or any needed appendix), including any supporting plots and tables.
• The commented code used to produce the results.

The report should address all points described in the “Objective” section below.

The report should be returned in the following way:
• Electronic (submit via Moodle)
Background
The Commercial Banking Corporation (hereafter the “Bank”), acting by and through its department of Customer Services and New Products is seeking proposals for banking services. The Bank ultimately wants to predict which customers will buy a variable rate annuity product.

A variable annuity offers a range of investment options. The value of your investment as a variable annuity owner will vary depending on the performance of the investment options you choose. The investment options for a variable annuity are typically mutual funds that invest in stocks, bonds, money market instruments, or some combination of the three. If you are interested in more information, see:
http://www.sec.gov/investor/pubs/varannty.htm

The project will be broken down into 3 phases:
• Phase 1 – Variable Understanding and Assumptions
• Phase 2 – Variable Selection and Modeling Building
• Phase 3 – Model Assessment and Prediction
Objective – Phase 1
The scope of services in this phase includes the following:
• For this phase use only the training data set.
• Explore the predictor variables individually with the target variable of whether the customer bought the insurance product.
o Summarize only the significant variables in a table ranking from most significant to least significant – the Bank currently uses 𝛼 = 0.002, but is open to another if you defend your reason.
§ This table should separate out the four possible classes of variables – binary, ordinal, nominal, continuous.
§ (HINT: Explore the predictor variables individually for now since you have not yet accounted for missing values.)
§ (HINT: The downside to software sometimes is displaying a full p-value for ranking. That doesn’t mean you cannot get them through the right commands. As long as you have the same degrees of freedom you can rank on test statistic as well.)
o In an appendix, include a table with all of the variables ranked by significance.
• Provide a table of odds ratios for only binary predictor variables in relation to the target variable.
o Rank these odds ratios by magnitude.
o Interpret only the highest magnitude odds ratio.
o Report on any interesting findings.
§ (HINT: This is open-ended and has no correct answer. However, you should get use to keeping an eye out for what you might deem important or interesting when exploring data to report in an executive summary.)
• Provide a summary of results around the linearity assumption of continuous variables.
o List both which variables meet and do not meet the needed assumption for continuous variables.
o (HINT: Do not get overly mathematical here. Just report what you find; do not teach.)
• Provide a summary of important data considerations as follows:
o Visual representation of which variables have the highest (defined by you for now) amount of missing values.
o List any combinations of variables that you feel have redundant information so the Bank might consider removing them in the future.
§ (HINT: This is open-ended and has no correct answer. For example, presence of a money market account and money market balance.)
o Report on any interesting findings.
§ (HINT: This is open-ended and has no correct answer. However, you should get use to keeping an eye out for what you might deem important or interesting when exploring data to report in an executive summary. For example, teller visits as well as other variables might represent human contact with the bank as compared to only online contact.)

Data Provided
The following two sets of data are provided for the proposal:
• The training data set insurance_t contains 8,495 observations and 48 variables.
o All of these customers have been offered the product in the data set under the variable INS, which takes a value of 1 if they bought and 0 if they did not buy.
o There are 47 variables describing the customer’s attributes before they were offered the new insurance product.
• The validation data set insurance_v contains 2,124 observations and 48 variables.
• The table below describes the Roles and Description of the variables found in both data sets.
o Except for Branch of Bank, consider anything with more than 10 distinct values as continuous.

Name Model Role Description
ACCTAGE Input Age of oldest account
DDA
DDABAL DEP
DEPAMT
CASHBK
CHECKS
DIRDEP NSF
NSFAMT
PHONE
TELLER SAV
SAVBAL
ATM
ATMAMT
POS
POSAMT
CD
CDBAL IRA
IRABAL LOC
LOCBAL INV
INVBAL
ILS
ILSBAL MM
MMBAL
MMCRED MTG
MTGBAL
CC
CCBAL
CCPURC SDB
INCOME
HMOWN
LORES
HMVAL Input Indicator for checking account
Input Checking account balance
Input Checking deposits
Input Total amount deposited
Input Number of cash back requests
Input Number of checks written
Input Indicator for direct deposit
Input Number of insufficient fund issues
Input Amount of NSF
Input Number of telephone banking interactions
Input Number of teller visit interactions
Input Indicator for savings account
Input Savings account balance
Input Indicator for ATM interaction
Input Total ATM withdrawal amount
Input Number of point of sale interactions
Input Total amount for point of sale interactions
Input Indicator for certificate of deposit account
Input CD balance
Input Indicator for retirement account
Input IRA balance
Input Indicator for line of credit
Input LOC balance
Input Indicator for investment account
Input INV balance
Input Indicator for installment loan
Input ILS balance
Input Indicator for money market account
Input MM balance
Input Number of money market credits
Input Indicator for mortgage
Input MTG balance
Input Indicator for credit card
Input CC balance
Input Number of credit card purchases
Input Indicator for safety deposit box
Input Income
Input Indicator for home ownership
Input Length of residence in years
Input Value of home
AGE Input Age
CRSCORE
MOVED
INAREA INS
BRANCH RES Input Credit score
Input Recent address change
Input Indicator for local address
Target Indicator for purchase of insurance product
Input Branch of bank
Input Area classification