Retail Modelling in
the Financial Sector
The applications of statistics into
key financial decisions
About me –
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
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Retail Modelling
Overview
What is Retail Modelling?
Retail Modelling
Retail Modelling within the Financial sector comprises of the following:
Decision Modelling
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Application, including affordability
Back book / behavioural
Collections
Pricing
Propensity
Stress Testing
IRB / Capital
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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:
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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:
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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
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
– Letters sent
– Limit setting
– Type of actions taken
• Behavioural
– Limit increase/decrease
– Authorisation
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• Collections
Decision Modelling
Current Accounts
What’s involved in a model build?
Current Account – Application Model Build
• Selecting a development sample /
• Exclusions
– Observation
– Performance
• Defining what we mean by ‘bad’
– Outcome
– Severity
• Data sources
– Application
– Bureau
– Cross-Holdings
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• Scorecard segmentations
representative population
Decision Modelling
Current Accounts
What do you model?
• The outcome period of a bad definition • The severity of a bad definition is
is decided by the emergence analysis.
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decided by the Roll Rates.
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
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Decision Modelling
Current Accounts
What is a scorecard?
What would you score on this scorecard?
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Decision Modelling
Current Accounts
How is the model applied in the business?
Cut-off setting
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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
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• A more sophisticated approach is
to look at profitability
Decision Modelling
Current Accounts
What happens after the model is built?
The purpose of monitoring is to analyse the following;
Model Stability
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See how Data and Characteristics are evolving compared to the development
sample.
Early Performance
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Early performance analysis can indicate what may happen in the future.
Expected vs. Actual
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Comparing what has actually happened against what was expected to
happen; this may indicate how well the models work.
Decision Modelling
Current Accounts
Monitoring in action…
Over-predicting risk
Under-predicting risk
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:
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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.
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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:
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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
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Pricing for risk allows for additional lending to customers that would be unprofitable with
a single price point
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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.
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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
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Decision Modelling
Stress Testing
Significant regulatory focus
Changing Nature of the Role
What are the key challenges in Stress Testing?
• Increasing Regulatory Scrutiny
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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
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How will Buy-to-Let portfolios perform in an environment where base rates increase?
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:
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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] * (1 - 1.5 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.
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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
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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?
Questions?
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