辅导MATH 60603A编程、辅导R程序设计

MATH 60603A Statistical Learning Assignment #1

Fall 2021 1

 

Credit risk game

 Individual assignment.

 Upload your decision(s) before 8:30AM (EDT) on October 15th, 2021.

 You are required to provide your R code (upload it on ZoneCours).

 This round of business simulation is worth 10% of your final grade.

 

Context:

In order to minimize loss and maximize profits from lending, banks require careful assessment of their borrowers. This has

led to credit risk computation becoming a commonly used application of statistics and data science. You are working with a

large bank to help them optimize their profits from financing individuals who apply for a loan. The bank has provided you

with the records and results of lending money to some customers. You will be using this information and R to decide how

many and which individuals to lend money to. Your objective is to maximize the banks’ profits obtained from these loans.

Data:

A zip file containing the data is available on ZoneCours. The file CreditGame_TRAIN.csv contains records of previous loans

provided by the bank. They include features about the customers and information about their default status as well as the

magnitude of profit or loss incurred for each loan. The file CreditGame_Applications.csv contains information about current

loan applications. You must decide who on this list get their loan approved.

How to play the game:

You need to prepare a one column CSV file with the list of IDs of the customers whose loan application you accept from

that the loans have generated for the bank after 24 months. The platform allows for multiple uploads per person, up to 99,

which means that you may try many different solutions.

On the upload platform, you will see not only your results but also those of the whole group. While the game is being played,

you will see the “interim leaderboard.” Before the deadline of the assignment, you must select your final decision as one of

your uploads. When the game ends, the “real-life leaderboard” will be unveiled and will prevail for the final ranking. The

“real-life leaderboard” plays the role of a test set: it is a holdout sample that is kept until the end to measure the performance

of the final answers of everybody.

Method:

Your objective is based on a business outcome: profit. The process of prediction will involve cleaning, analyzing, modeling,

and getting results. We do not give further instructions on the methods used; you are on your own for that. There is no single

good answer and multiple strategies that can support the business problem reasonably well. We expect each student to come

up with their own approach.

Disclaimer:

There are additional nuances for credit risk assessment in a real-life setting. Banks need to abide by the Basel accords and

must comply with some rules to assess their credit risk. Credit risk typically implies a need to interpret the results of a model,

and some standards in methodology apply. Although this business simulation is very realistic, both in terms of context and

data, it does not depict those field-specific particularities.

 

MATH 60603A Statistical Learning Assignment #1

Fall 2021 2

Evaluation:

Look for the baseline on the upload platform. It corresponds to the profit made when all customer applications are approved.

To get a passing grade, you must do better than that!

The evaluation will be based on the results at the end of the game. Each student must select one of their uploads as their

final answer, and that answer will prevail. You get:

 0% - if you are below the baseline on the interim leaderboard; at least 50% if your profit is above.

 100% - Top 10% of students on the “real-life leaderboard.”

 The rest of the marks will be linearly interpolated using the following equation with values from the “real-life

leaderboard”: 50{1 + ( ? )/( ? )}

where is your profit, the baseline, and the profit of the last student with 100% from the previous criterion.

 

You must upload your R code on ZoneCours. It will not be reviewed systematically, only if some precisions are needed. Your

grades could be reduced if irregularities are found in the R code, or if there is evidence you have not used R.

Variables:

Variable name Description

ID_TRAIN Unique borrower ID

TYP_FIN Type of funding requested (Car, Mortgage, or Credit)

NB_EMPT Number of borrowers (borrower variables are only for the principal borrower)

R_ATD Total Debt Amortization (TDA) Ratio, i.e., monthly financial commitments over monthly

income

PRT_VAL The requested loan amount over the value of the goods

DUREE Requested loan duration

AGE_D Age of the borrower

REV_BT Gross Income

REV_NET Net Income

TYP_RES Residence Type – P: Owner, L: Tenant, A: Others

ST_EMPL Employment Status – R: Regular, P: Part-Time, T: Self Employed

MNT_EPAR Savings Value

NB_ER_6MS Number of transactions refused due to insufficient funds in the last 6 months

NB_ER_12MS Number of transactions refused due to insufficient funds in the last 12 months

NB_DEC_12MS Number of overdrafts in the last 12 months

NB_OPER Total number of transactions in record

NB_SATI Total number of satisfactory transactions in record (No payment delay)

NB_COUR Number of current transactions

NB_INTR_1M Number of inquiries in the last month

NB_INTR_12M Number of inquiries in the last 12 months

NB_DEL_30 Number of 30–59 day delinquency in the last 12 months

NB_DEL_60 Number of 60–89 day delinquency in the last 12 months

NB_DEL_90 Number of 90+ day delinquency in the last 12 months

MNT_PASS Value of financial Liabilities

MNT_ACT Value of financial Assets

MNT_AUT_REN Total authorized amount of revolving credit

MNT_UTIL_REN Total used amount of revolving credit

MNT_DEMANDE Loan amount requested

DEFAULT Default is considered when payment is 90 days or more late within 24 months

1: Default, 0: Did not default

PROFIT_LOSS Profit made or loss incurred with this loan after two years

 

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