1. Retail Modelling in the Financial Sector The applications of statistics into key financial decisions

2. About me – Matthew Jones
work experience across public & private sectors including academia, pharmaceuticals and finance
Matthew Jones
10/03: PhD in Statistics
10/03 – 01/07: Statistical Consultant (Pharmaceuticals)
02/07 – 03/12: Senior Analyst, Manager, Senior Manager.
03/03 – 07/13: University Lecturer – Computational Statistics
03/12 –: Senior Manager (Model Validation, Credit Cards Risk, Retail Modelling, IRB Modelling)
Head of Retail Modelling

3. Retail Modelling Overview
What is Retail Modelling?
Retail Modelling
Retail Modelling within the Financial sector comprises of the following:
Decision Modelling
Application, including affordability
Back book / behavioural
Collections
Pricing
Propensity
Stress Testing
IRB / Capital

4. Decision Modelling Evolving team
Increasing business focus to meet business needs
The Changing Team
There has been rapid expansion within the retail modelling teams in the last 2 years:
Increasing headcount from 4 FTE to 18 FTE for Decision Modelling
majority of the team are Mathematics graduates/post-graduates, or have a numerically based degree
The expansion allows for:
The enhanced monitoring of existing models
A swift replacement of any under-performing models
Additional modelling options – e.g. modelling only on accounts that the model will have the greatest impact on, rather than entire populations

5. Decision Modelling Current Accounts
One of Nationwide's headline products
Product Types
FlexAccount – Standard current account
FlexDirect – High credit interest
FlexPlus – Packaged account with a fee
FlexOne – Aimed at younger customers and students (on the horizon)
Score based decisions
Application
– Accept/Decline
– Limit setting
Behavioural
– Limit increase/decrease
– Authorisation
Collections
– Letters sent
– Type of actions taken 5

6. Decision Modelling Current Accounts
What’s involved in a model build?
Current Account – Application Model Build
Selecting a development sample / representative population
Exclusions
– Observation
– Performance
Defining what we mean by ‘bad’
– Outcome
– Severity
Data sources
– Application
– Bureau
– Cross-Holdings
Scorecard segmentations 6

7. Decision Modelling Current Accounts
What do you model?
The outcome period of a bad definition is decided by the emergence analysis.
The severity of a bad definition is decided by the Roll Rates. 7

8. Decision Modelling Current Accounts
Making a model a scorecard
Modelling Methodology
Logistic regression is the standard methodology used in Decision Modelling
To each account, the logit (log odds) of the account becoming a ‘bad’ is assigned
To make this more palatable for the business, this log odds is converted to a score
Centre Score
The score where the ratio of Goods and Bads is 1:1
The centre score is where the bad rate is expected to be 50%
PDO (Points to Double Odds)
The number of points to increase or decrease the ratio of Goods to Bads
For example, we might have a centre score of 200 and a PDO of 20 8

9. Decision Modelling Current Accounts
What is a scorecard?
What would you score on this scorecard? 9

10. Decision Modelling Current Accounts
How is the model applied in the business?
Cut-off setting
This will be a balance of accept rates and bad rates
We could set the new cut-off to match the current accept rate or bad rate
A more sophisticated approach is to look at profitability 10

11. Decision Modelling Current Accounts
What happens after the model is built?
The purpose of monitoring is to analyse the following;
Model Stability
See how Data and Characteristics are evolving compared to the development sample.
Early Performance
Early performance analysis can indicate what may happen in the future.
Expected vs. Actual
Comparing what has actually happened against what was expected to happen; this may indicate how well the models work.

12. Decision Modelling Current Accounts
Monitoring in action…
Over-predicting risk
Under-predicting risk

13. Decision Modelling Propensity
Models used to identify reward
Propensity
When modelling propensity, we are analysing the likelihood for a customer / applicant to meet specific criteria, such as:
Transact on their account
Revolve a balance in the next X months, e.g. on a Credit Card
Take up a product
Typically, these are heavily used in marketing, particularly to improve the effectiveness of marketing campaigns.

14. Decision Modelling Pricing
Ensure that the price point is right
Why Tier Pricing?
A key reason for having different price points is to allow for Risk Based Pricing:
Rather than a single price point, multiple can exist
This allows for higher quality customers to receive a better APR
If this is not in place, there are elements of ‘negative selection’ – the best customers can receive lower prices elsewhere and do not take up the product
Pricing for risk allows for additional lending to customers that would be unprofitable with a single price point
At Nationwide the member relationship also comes into consideration to ensure that we are rewarding deep customer relationships
51% if customers must receive the headline rate – after this Risk Based pricing may be applied.

15. Decision Modelling Stress Testing
Increasingly important area of modelling
The Role
Responsible for providing loss forecasts for Secured and Unsecured portfolios
Why are these forecasts required?
Base Scenario
This is the best estimate for what the business believe will happen within the economy – allows for understanding of how the portfolios will perform under this scenario
Stressed Scenario
Key economic factors are stressed to determine the impact on retail portfolios
This can highlight which areas of a portfolio may perform poorly under the given stressed scenario
There has been a greater emphasis on stressed scenarios since the financial crisis
These scenarios can help the regulator to understand the health of retail portfolios

16. Decision Modelling Stress Testing
Significant regulatory focus
Changing Nature of the Role
What are the key challenges in Stress Testing?
Increasing Regulatory Scrutiny
Annual stress tests to become the norm from various regulators
Multiple scenarios, from both UK and EU regulators – leading to greater challenges for both deadlines and resources
Senior management must know the results to a greater level of detail than in the past
Knowledge of Emerging Portfolios and Risks
How will Buy-to-Let portfolios perform in an environment where base rates increase?

17. Decision Modelling IRB / Capital
Assessing Nationwide’s Capital requirements
The risk of retail portfolios is calculated to determine the regulatory capital that must be held, modelling:
Probability of Default (PD)
Exposure at Default (EAD)
Loss Given Default (LGD)
Expected Loss = PD * EAD * LGD
The capital requirement can then be calculated, using the following formula:
Capital requirement (K) = [LGD * N [(1 - R)^-0.5 * G (PD) + (R / (1 - R))^0.5 * G (0.999)]- PD * LGD] * ( x b(PD))^ -1 × (1 + (M - 2.5) * b (PD)
N() is the standard normal distribution; G() is the inverse normal distribution
R is the asset correlation (dependent on portfolio); b(PD) is the maturity adjustment
Expected Losses are the average level of credit losses a financial institution can reasonably expected. Unexpected Losses are to be protected against by the Capital requirement.

18. Decision Modelling IRB / Capital
Key challenges in Capital modelling
Changing Nature of the Role
The key challenges within IRB are:
Regulatory
Much greater scrutiny of assumptions within models from the regulators
This has led to enhanced internal scrutiny and governance procedures for model developments
IFRS9 Regulations
IFRS9 regulations require 1 year expected losses for accounts that have not suffered a significant credit deterioration
Lifetime expected losses are required for those that have suffered a significant credit deterioration – can current EL models be adapted for use within this framework?

19. Questions?