Chapter 5 consumer lending

Chapter 5
Consumer Lending
The Presentation Slides for Teaching Regulatory Technology
Website : https://sites.google.com/site/quanrisk
E-mail : quanrisk@gmail.com
Copyright © 2021 Dr. LAM Yat-fai

Declaration
 Copyright © 2021 Dr. LAM Yat-fai
 All rights reserved. No part of this presentation file may be
reproduced, in any form or by any means, without written
permission from Dr. LAM Yat-fai.
 Authored by Dr. LAM Yat-fai (林日辉),
Chief Data Scientist, CapitaLogic Limited,
Adjunct Professor of Finance, City University of Hong Kong,
Doctor of Business Administration,
CFA, CAIA, CAMS, CFE, FRM, PRM, MCSE, MCNE.
Copyright © 2021 Dr. LAM Yat-fai 2

Outline
 Sample data set
 Regulatory requirements
 Model development
 Default prediction model
 Overdue prediction model

Two class sampling techniques
 For machine learning theories
 50% to 50%
 Real full data set
 30% to 70%
 20% to 80%
 Under sampling
 Reduce the majority class to match the minority class
 Over sampling
 Increase the minority class to match the majority class

Full data set

Under sampling

Over sampling

SMOTE
 Synthetic Minority Oversampling Technique
 Create new records from existing minority
class
 For each record of minority class
 Substitute the value of a feature with the value of a
record nearby

Feature type
 Numeric
 Continuous feature Annual income
 Discrete feature No. of children
 Non-numeric
 Binary feature Gender
 Categorical feature Education

Binary feature
 Gender
 Male 0
 Female 1
 Martial status
 Single 0
 Married 1

Categorical feature
 Education
 Postgraduate
 Undergraduate
 Secondary school
 Primary school
 No education
 Country
 United States
 United Kingdom
 Canada
 Australia
 China

Assign ranks to categorical variable
Country Label average Rank
Australia 0.21 5
Canada 0.63 1
China 0.49 3
United Kingdom 0.38 4
United States 0.52 2

Chapter 5a2 – Sample data set (1)
 Datasets
 Chapter 5a1 – Full data set.csv
 Chapter 5a3 – Country rank
 SMOTE
 Label column y
 SMOTE percentage 58
 Edit Metadata
 Column Gender, Country

Chapter 5a2 – Sample data set (2)
 Convert to Indicator Values
 Categorical columns Gender
 Join Data
 Join key columns for Left Country
 Join key columns for Right Country
 Select Columns in Dataset
 All columns exclude Gender, Gender-Male,
Country, Country(2)
 Convert to CSV

Outline
 Sample data set

Banking activities
Deposits
< 1%
Shareholders’
equity
Dividend +
equity price
appreciation
3% - 20%
Term loans,
credit cards,
mortgages
Bank

Major retail lending products
 Term loan
 Fixed loan principal
 No collateral
 Credit card
 Subject to a credit limit
 No collateral
 Residential mortgage
 Principal amortization
 Property as collateral

HKMA SPM CR-G-1
General Principles of Credit Risk Management
 4.1.4 Credit decisions should be supported by
adequate evaluation of the borrower's
repayment ability based on reliable
information. Sufficient and up-to-date
information should continue to be available to
enable effective monitoring of the account.

Commercial Banks Law, PRC
 Article 35. Before granting a loan,
commercial banks shall strictly examine the
borrower's purpose for the loan, ability to
repay the loan, method of repayment, etc.
.

20
Default
 Generic definition
 A borrower fails to pay to the lender the interest
and/or principal in full on schedule
 Regulatory definition
 A borrower fails to pay to the lender the interest
and/or principal within 90 days of the due date

21
Survival → overdue → default
Survival
Overdue
Default
Payment
Payment
Yes
Yes No
No
90
days

Risk management objectives
 To predict whether a borrower will default in
the following one year
 Good, bad
 Good, moderate, bad
 Good, good to moderate, moderate to bad, bad
 Probability of default

Regulatory and legal constraints
 Interpretability
 The relationship between the label and features can
be well established by experience and theory
 No violation with common sense unless the
common sense is inapplicable
 Equal opportunity
 Religion, race, disability … cannot be used
directly as a feature, as mandated by anti-
discrimination law

Outline
 Sample data set

Standard procedure (1)
 Analyze regulatory requirements
 Collect historical records
 Prepare sample data set
 Over sampling
 Convert binary features into [0,1]
 Convert categorical features into ranks

Standard procedure (2)
 Assess monotonicity and principal components
 Develop machine learning model
 Evaluate model performance
 Create prediction model
 Assess sample, positive class and negative
class accuracies
 Conduct prediction

Modelling assumptions
 Many historical borrower records
 Many borrowers survive in one year
 Some borrowers default in one year
 There is a monotonic relationship between
 Label: whether a borrower defaults in one year
 Features: borrower information collected one year
ago

Label
 0
 The borrower survives in one year
 1
 The borrower defaults in one year

Features
 From application form
 Personal data
 Employment data
 Income data
 Asset data
 Liability data

Expected monotonicity
 Strong
 Annual income, outstanding loan, equity investments
 To be verified by data
 Weak or unknown
 Education, gender, martial status
 To be determined by data
 No
 ID card no., telephone no., application date
 To be ignored

Data dictionary
 Label
 Survive (0) or Default (1)
 Description
 The default status of a borrower in one year after
his features were collected
 Label type
 Binary

Data dictionary
 Feature
 Income
 Description
 The total income during the last 12 month, including
fixed salary, commission and bonus, after income tax
and pension contribution have been deducted
 Feature type
 Continuous value
 Expected impact to default
 Strong
 Negative

Data dictionary
 Feature
 Loan amount
 Description
 The outstanding loan amount
 Feature type
 Continuous value
 Expected impact to default
 Strong
 Positive

Outline
 Sample data set

Two class PD model
 
       
1 2 3 N
Default Default Default Default
1 2 3 U
Survive Survive Survive Survive
1 2 3 V
PD = F x , x ,x , , x
Maximize
L = PD × PD × PD × × PD ×
1 - PD × 1 - PD × 1 - PD × × 1 - PD
35
Copyright © 2020 CapitaLogic Limited

Full data set
 Feature
 Data on existing mortgage borrowers one year ago
 Label
 Either survives or defaults in one year
 Several thousand records
 Imbalanced
 Some features missed
 Some records duplicated

Label and all features
 Label
 0: Survives in the last year
 1: Defaults in the last year
 Loan amount
 Loan purpose
 Debt consolidation
 Home improvement
 Job title
 No. of years at current job
 No. of major derogatory reports
 No. of delinquent credit lines
 Age of oldest credit line in
months
 No. of credit enquiries in the
most recent 30 days
 No. of credit lines
 Debt-to-income ratio

Chapter 5b2 – Sample data set (1)
 Datasets
 Chapter 5b1 – Full data set.csv
 Chapter 5b3 – Job title rank
 SMOTE
 Label column Default
 Edit Metadata
 Column Loan purpose, Job title

Chapter 5B2 – Sample data set (2)
 Categorical columns Loan purpose
 Join Data
 Join key columns for Left Job title
 Join key columns for Right Job title
 Save as Dataset
 All columns exclude Loan purpose, Loan purpose-
Loan Consolidation, Job title, Job title(2)
 Convert to CSV

Feature selection
 Weak monotonicity
 Mortgage amount
 No. of credit lines
 Loan purpose-Home
 No. of principal components
 8

Chapter 5b4 – Prediction model
 Datasets
 Chapter 5b2 – Sample data set
 Edit Metadata
 Column Job title
 Join Data
 All columns exclude Loan purpose, Job title, Job
title(2)

Other standard procedures
 Run training experiment
 Create predictive experiment
 Modify Web service input and Web service output
 Run predictive experiment
 Deploy web service
 Download Excel prediction model
 Upload Excel prediction model to OneDrive
 Conduct assessment and prediction

Excel prediction model
 5 samples test
 All samples accuracy
 Overall accuracy
 Survive class accuracy, full data set
 Upper cutoff score
 Defaulted class accuracy, full data set
 Lower cutoff score
 Prediction

Outline
 Sample data set

Label and all features, three class
 Label
 Survive
 Overdue
 Default
 Loan amount
 Loan purpose
 Job title
 Age of oldest credit line in
months
 No. of credit enquiries in the
most recent 30 days

Label and all features, two class
 Label
 0: Survive
 1: Overdue or default
 Loan amount
 Loan purpose
 Job title
 Age of oldest credit line in months
 No. of credit enquiries
 Enquiries in the most recent 30 days

Chapter 5c2 – Sample data set (1)
 Datasets
 Chapter 5c1 – Full data set.csv
 Chapter 5c3 – Job title rank
 SMOTE
 Label column Default
 Edit Metadata

Chapter 5c2 – Sample data set (2)
 Join Data
 Convert to CSV

Feature selection
 Weak monotonicity
 Mortgage amount
 Binary feature
 Loan purpose-Home improvement
 PCA cannot be applied
 No. of principal components
 9

Chapter 5c4 – Prediction model (1)
 Datasets
 Chapter 5b2 – Sample data set
 Edit Metadata

Chapter 5c4 – Prediction model (2)
 Join Data

Is overdue a weak form of default?
 Default
 Loan purpose
 Overdue
 Loan amount

Creation of theory
 Hypothesis
 Overdue is a week form of default
 Testing
 H0: Overdue and default are caused by the same
set of features
 H0: Overdue and default are NOT caused by the
same set of features
 To be researched

Chapter 5 consumer lending

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