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

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

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

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

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

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

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

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

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

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

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.

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: 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.

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.

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

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?

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] * (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.

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?

Questions?