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FNCE30012: Foundations of Fintech - Assignment 5
This assignment builds on Lectures 10 to 12 and on Tutorials 8 to 11. You might want to consider using parts of
the Python code discussed in those tutorials to answer some of the questions below.
Important: It is important that you do not change the type (markdown vs. code) of any cell, nor
copy/paste/duplicate any cell! If the cell type is markdown, you are supposed to write text, not code, and vice
versa. Provide your answer to each question in the allocated cell. Do not create additional cells. Answers
provided in any other cell will not be marked. Do not rename the assignment files. All files in the assignment
directory should be left as is.
Setting
Equifax Australia has provided us with synthetic loan application data from Australian proprietary companies.
This data was generated to match the characteristics of actual lending proposals approved between February
2017 and March 2018. The Equifax data consists of two parts, which, to make it easier for you, we have
merged together into one data set:
1. Company Business Trading History Data: This first part of the data set contains historical business trading
data from 25,000 Australian proprietary companies who were granted a loan between February 2017 and
March 2018.
2. Director Data: This second part of the data set contains information on up to four directors of each
company. In case a company has more than one director, the corresponding data has been averaged
across directors at the company level.
Since this is proprietary data that belongs to Equifax, we are not allowed to give you direct access to it.
However, thanks to Jupyter Hub, you are able to access it remotely. In particular, using your knowledge from
Tutorial 9, you are able to analyse it at an aggregate level and to use it for the estimation of credit scoring
models.
The file called Equifax_Data_Dictionary.xlsx provides you with the dictionary for both company and
director level data.
Helpful commands
The merged Equifax dataset will be referred to by the name assignment5 . Please see Tutorial 9 for the
details of how to run functions on remote data. We have implemented additional functions to help you with this
assignment. Details of these functions are below:
1. send_grouped_mean_request() : This function takes inputs in the form of a dictionary containing
names of two columns, and returns a Pandas dataframe that contains grouped means of a column with
respect to another column. For example, using this function with input {"data":
"tutorial9.1","var": "age", "y": "SeriousDlqin2yrs"} will return a dataframe where each
entry has a value for age and the respective mean of SeriousDlqin2yrs for applicants' of that age
(see Tutorial 10).
2. send_glm_request() : This function takes inputs in the form of a reference to the remote data ("data")
and the dependent variable ("y"), i.e., {"data": data, "y": y} , and outputs the detailed results of a
full-fledged logistic regression model without feature selection.
3. send_logit_request() : This function is similar to the send_nn_request() method, but it
performs the remote estimation of a customised logistic regression. It takes inputs in the form of a
2019/10/21 Assignment-5 - Jupyter Notebook
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p g g p
dictionary {"data": data, "test": 0.2, "x": features, "y": y, "scale":"True"}
where:
a. "data": Reference to the remote data (see below)
b. "test": Fraction of the data used for testing
c. "x": List of features (independent variables) used by the model
d. "y": Target variable (dependent variable) of the model
e. "scale": Indicator ("True"/"False") for scaling
Predefined variables
For your convenience, we have predefined certain variables which you should reuse for this assignment:
1. data : The remote Equifax dataset that should be used in this assignment
2. target : The target variable (default indicator over 12 months)
3. all_features : The complete list of available features
Note: Please do not change the values of these variables.
2019/10/21 Assignment-5 - Jupyter Notebook
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In [ ]:
# the merged Equifax dataset
data = "assignment5"
# the target (dependent) variable of interst (good/bad flag): 0 means no default wit
target = 'Commercial_GBF_12m'
# the complete list of available features as of the loan approval date (see Equifax_
all_features = ['EFX_Comp_ID',
'loanAmt',
'External_Admin',
'Petitions',
'Writs_and_Summons',
'Writs_and_Summons_Value',
'Writs_and_Summons_LT_12M',
'Writs_and_Summons_LT_12M_Value',
'Writs_and_Summons_GT_12M',
'Writs_and_Summons_GT_12M_Value',
'Judgements',
'Judgements_Value',
'Judgements_LT_12M',
'Judgements_LT_12M_Value',
'Judgements_GT_12M',
'Judgements_GT_12M_Value',
'Directors',
'Defaults',
'Defaults_Value',
'Defaults_12',
'Defaults_12_Value',
'Defaults_GT_12M',
'Defaults_GT_12M_Value',
'Telco_Defaults_LT_12M',
'Telco_Defaults_LT_12M_Value',
'Utility_Defaults_LT_12M',
'Other_Defaults_LT_12M',
'Other_Defaults_LT_12M_Value',
'Defaults_Paid',
'Defaults_Paid_Value',
'Defaults_Unpaid',
'Defaults_Unpaid_Value',
'Credit_Enqry',
'Credit_Enqry_Value',
'Credit_Enqry_LT_12M',
'Credit_Enqry_LT_12M_Value',
'Credit_Enqry_GT_12M',
'Credit_Enqry_GT_12M_Value',
'Broker_Enqry',
'Broker_Enqry_Value',
'Broker_Enqry_LT_12M',
'Broker_Enqry_LT_12M_Value',
'Broker_Enqry_GT_12M',
'Broker_Enqry_GT_12M_Value',
'Mercantile_Enqry_LT_12M',
'Mercantile_Enqry',
'Mercantile_Enqry_GT_12M',
'Mercantile_Enqry_GT_12M_Value',
'ny7513_df_6m',
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4
/
9
'ny7514_df_12m'
,
'ny7516_df_60_84m'
,
'ny7517_df_tcut_6m'
,
'ny7518_df_tcut_12m'
,
'ny7520_df_tcut_60_84m'
,
'ny7568_df_sts_unpd_60_84m'
,
'ny7585_df_origamt_tcut_60_84m'
,
'ny7586_df_origamt_60_84m'
,
'ny7587_df_latamt_12m'
,
'ny7588_df_latamt_60_84m'
,
'ny7589_df_time_1'
,
'ny7591_df_s_1_60_84m'
,
'ny7601_adv_48_84m'
,
'ny7999_enq_7d'
,
'ny8000_enq_1m'
,
'ny8001_enq_3m'
,
'ny8002_enq_6m'
,
'ny8003_enq_12m'
,
'ny8006_enq_60m'
,
'ny8028_enq_rm_1m'
,
'ny8029_enq_rm_3m'
,
'ny8030_enq_rm_6m'
,
'ny8031_enq_rm_12m'
,
'ny8034_enq_rm_60m'
,
'ny8042_enq_tcut_1m'
,
'ny8043_enq_tcut_3m'
,
'ny8044_enq_tcut_6m'
,
'ny8045_enq_tcut_12m'
,
'ny8048_enq_tcut_60m'
,
'ny8049_enq_own_3m'
,
'ny8050_enq_own_12m'
,
'ny8056_enq_amt_1'
,
'ny8057_enq_amt_2'
,
'ny8059_enq_time_1'
,
'ny8060_enq_time_2'
,
'ny8062_enq_amt_3m'
,
'ny8063_enq_amt_60m'
,
'np7504_dj_60m'
,
'np7505_dj_time_1'
,
'np7506_dj_out_amt_60m'
,
'np7508_dj_out_60m'
,
'np7509_wr_48m'
,
'np7510_wr_out_48m'
,
'np7511_wr_60m'
,
'np7512_wr_out_60m'
,
'np8500_dr_cur'
,
'np8501_dr_time_max_cur'
,
'np8502_dr_prev_60m'
,
'np8508_pr_cur'
,
'np8509_dr_prev_120m_ever'
,
'na8905_ntb_flg'
,
'na8902_age_fle_max'
,
'na8904_age_fle'
,
'na8908_age_ind'
,
'na8920_em_time_1'
,
'na8921_ad_time_1'
,
'ny8056_enq_amt_1_is_1'
]
2019/10/21 Assignment-5 - Jupyter Notebook
https://jh4.bmmlab.org/user/linweiz/notebooks/Assignment-5/Assignment-5.ipynb 5/9
Please run the following cell to import the required libraries.
In [ ]:
In your assessment, please address the following questions.
Question 1 (2 marks)
Write a Python code that creates two bar plots of average default rates ( Commercial_GBF_12m ) depending
on (i) whether a company was under external administration ( External_Admin ) or (ii) had filed petitions
(https://www.thegazette.co.uk/all-notices/content/101087) ( Petitions ). Make sure your plots' axes are
appropriately labelled.
Answer 1
In [ ]:
Question 2 (2 marks)
Write a Python code that creates two plots of average default rates ( Commercial_GBF_12m ) as a function of
(i) the number of months since a director's last commercial default ( ny7589_df_time_1 ) and (ii) the
frequency of adverse commercial events over four years 48 months prior to application
( ny7601_adv_48_84m ). Make sure your plots' axes are appropriately labelled.
Answer 2
# Usual imports
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
# Essential libraries for this assignment
from finml import *
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
%matplotlib inline
import nest_asyncio
from tfclient import send_nn_request, send_logit_request, send_glm_request, send_gro
nest_asyncio.apply()
# Suppress warnings for deprecated methods from TensorFlow
import tensorflow as tf
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
"""Write your code in this cell"""
# YOUR CODE HERE
pass
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In [ ]:
Question 3 (1 mark)
How do you interpret the above plots from Questions 1 and 2? What is your conclusion?
Answer 3
YOUR ANSWER HERE
Question 4 (2 marks)
Run a full-fledged logistic regression model without any ex-ante feature selection. Based on the estimation
output, select and report all features that are significant at the 5%-level (or below).
Note: To increase the stability of the estimation, Python will automatically omit certain variables.
Answer 4 - Code
In [ ]:
Answer 4 - Text
YOUR ANSWER HERE
Question 5 (3 marks)
Run a logit model using the function send_logit_request() and applying the following specifications:
1. Relative size of test data: 20%
2. Only use the features from Question 4 with a significance level below 5%
3. Scaling: "True"
Evaluate the testing performance of your logit model.
Answer 5 - Code
"""Write your code in this cell"""
# YOUR CODE HERE
pass
"""Write your code in this cell"""
# YOUR CODE HERE
pass
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In [ ]:
Answer 5 - Text
YOUR ANSWER HERE
Question 6 (4 marks)
Write a Python code that estimates a series of full-fledged neural networks with the following specifications:
1. Number of layers: 1
2. Number of units: 2, 4, 16, 64, 256
3. Relative size of test data: 20%
4. Scaling: "True"
Generate one plot that shows each model's ROC ("roc"), both for testing and training. What is your
conclusion?
Answer 6 - Code
In [ ]:
Answer 6 - Text
YOUR ANSWER HERE
Question 7 (2 marks)
Based on the testing performance of the above five neural network model, which one would you pick and why?
Rerun the estimation of your chosen model.
Answer 7 - Text
YOUR ANSWER HERE
Answer 7 - Code
"""Write your code in this cell"""
# YOUR CODE HERE
pass
"""Write your code in this cell"""
# YOUR CODE HERE
pass
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In [ ]:
Question 8 (3 marks)
Conduct an in-depth comparison between the "simple" logit model (Question 5) and your preferred neural
network (Question 7). What are their respective potential advantages and disadvantages? If you were to run a
credit scoring agency, which type of model you think your clients would prefer?
Answer 8
YOUR ANSWER HERE
Question 9 (3 marks)
The average loan amount across the Equifax sample is $75,000. Furthermore, let us make the following
simplifying assumptions:
1. The interest rate charged for each loan under the simple logit model (Question 5) is 5% p.a.
2. Each loan has a duration of one year
3. If a loan defaults, the total amount is lost (zero recovery) and no interest payments occur
4. A loan application only gets granted, if the respective model predicts no default
5. Each granted loan generates administrative costs of 1% p.a.
You are running a business that lends loans of $75,000 to small companies. Based on the above testing data,
what is the interest rate implied by your chosen neural network (Question 7), such that you will generate the
same net income as under the simple logit model?
Note: For your calculations, you can neglect any time value of money effects.
Answer 9
YOUR ANSWER HERE
Question 10 (1 mark)
When you go through the dictionary provided in the file Equifax_Data_Dictionary.xlsx , you will notice
that Equifax uses primarily legal data rather than accounting data to predict defaults. Why do you think that is?
Answer 10
YOUR ANSWER HERE
"""Write your code in this cell"""
# YOUR CODE HERE
pass
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Question 11 (2 marks)
Discuss the pros and cons of using deep learning, i.e., hierarchical machine learning applied to big data, in the
context of credit scoring.
Answer 11
YOUR ANSWER HERE
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