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©2013 Experian Ltd. All rights reserved. No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the.

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Presentation on theme: "©2013 Experian Ltd. All rights reserved. No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the."— Presentation transcript:

1 ©2013 Experian Ltd. All rights reserved. No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the prior written permission of Experian. Experian Public. Macroeconomic Factors and Retail Credit Risk Risk Managers Association Event: Istanbul 29 th May 2013 Eric McVittie Director of Economic Research Experian Decision Analytics

2 2 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Macroeconomic factors & retail credit risk Agenda  Economic conditions are fundamentally important to retail credit risk.  Increased economic uncertainty makes it increasingly vital that lenders understand their exposures: ● Loss Forecasting Provisioning ● Concentration Risk ● Risk Appetite ● Scenario Analysis & Stress Testing ● Acquisitions / Account Management / Collections  This is very challenging for data, models and processes.  Effective approaches: ► Maximise information extracted from available data ► Take proper account of economic and non-economic influences on loan performance ► Are robust, transparent and flexible ► Become integrated into lenders’ normal business practice Important Difficult Possible (with care)

3 3 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public Risk Scores & Portfolio Insight Economic forecasting Loss Forecasting Provisioning Concentration Risk Risk Appetite Scenario Analysis & Stress Testing Build economics into decisions Risk Committee / Regulators Operations Provisioning / Capital Requirement Combining information from conventional credit risk data and models with economic data and models improves loss forecasting and helps optimise capital planning and allocation. Economic models provide a robust basis for stress testing and for enhancing decision processes within a rapidly evolving economic environment. Macroeconomic factors & retail credit risk It’s Important: Value added to lenders

4 4 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Macroeconomic factors & retail credit risk It’s Important: Economic challenges persist

5 5 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Macroeconomic factors & retail credit risk It’s Important: Economic challenges persist GDP growth forecasts for the Eurozone, Spring forecast for following year

6 6 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Macroeconomic factors & retail credit risk It’s Important: Economic challenges persist GDP Growth Forecasts for the Turkey, Spring forecast for following year

7 7 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Macroeconomic factors & retail credit risk It’s Difficult: Requirements & challenges Challenges  Limited data  Many potentially relevant non-economic & economic drivers  Uncertainty over model & forecast / scenario assumptions Data on trends / patterns in loan performance 1 Separate economic & non-economic factors 2 Identify correct economic drivers 3 Build robust & stable models for forecasting and stress testing 4 Establish forecast & stress scenario assumptions for economic factors 5 Loss forecasts / stressed losses / adjusted scores / etc.

8 8 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public Macroeconomic factors & retail credit risk It’s Difficult: The Problem ‘True’ Pr(good): ‘Standard’ Credit Scorecard: where P ijt = probability of good (1-PD) for individual i in population j at time t Y it-1 = observable credit history for i up to t-1 S it = (generally unobservable) individual ‘situational’ factors influencing i at t X ijt = economic conditions influencing i in population j at t Note: In general S and X vary by borrower, may include lags, and F may include interaction terms e.g. Logistic regression Standard credit risk models are backward looking, and may be subject to biases and drifts in calibration due to changes in missing ‘situational’ factors which influence loan performance.

9 9 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public Macroeconomic factors & retail credit risk It’s Difficult: Possible Solutions A range of approaches have been attempted to incorporate economic factors in retail credit risk models. Aggregated models using macroeconomic factors are simple, but aggregation may lose much valuable information. Disaggregated approaches maximize information content of available data, and allows flexibility in selecting appropriate economic metrics, controlling for changes to lender and borrower behaviours, and allowing for interaction between economic & non-economic determinants of default.

10 10 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public  Rich supply of historical economic data from official national and international sources: ► Primary data source (for Europe: Eurostat, national statistical offices) and statistics from secondary agencies (e.g. OECD, IMF, Bloomberg, Dow Jones)  The choice of economic factors should consider: ► Do accurate historical data exist, and are there credible forecasts of the economic data available to generate forward-looking scores? ► Is there a robust theoretical and empirical justification for including these factors as drivers of retail credit risk ► Economic factors should directly relate to the ability to pay. These will be factors related to income, employment / unemployment and cost of repayment (i.e. interest rates – even this one is perhaps controversial) ► Care is required in model specification to capture as broad as possible a range of economic risk factors consistent with the data and while avoiding overfitting Macroeconomic factors & retail credit risk It’s Difficult: Selection of economic risk factors Model selection criteria: Estimation diagnostics In-sample fit Predictive performance in hold- out and ‘out of time’ samples Simulation/forecast properties

11 11 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public Macroeconomic factors & retail credit risk It’s Difficult: Selection of economic risk factors  Search space is enormous : ► Many potentially-relevant variables ► Functional forms? ► Lag structures?  Finding ‘correct’ models hampered by: ► Data availability – particularly historical time series ► Estimation/Identification issues – collinearity, endogeneity  High risk of building models based on spurious correlations. Grid Search / Data Mining Theory, priors & ‘expert’ judgement Statistical Data Reduction Methods Macroeconomic factors proxy more direct influences on borrowers – proxy quality varies. Restrict search space to variables with ‘clear’ link to borrower finances: income / net worth / affordability Need to make efficient use of available data, avoiding excessive aggregation and exploiting variation across sub-populations. Great care is needed to establish robust / stable models that can generate reliable forecasts

12 12 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Macroeconomic factors & retail credit risk Example: UK Credit Card Delinquency & Unemployment Normalized series - levels Covariance Analysis

13 13 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Moving correlations between ILO rate & card delinquency rate Full Sample Macroeconomic factors & retail credit risk Example: UK Credit Card Delinquency & Unemployment

14 14 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Normalized series – delinquency rate levels, quarterly changes in unemployment rates (smoothed) Covariance Analysis Macroeconomic factors & retail credit risk Example: UK Credit Card Delinquency & Unemployment

15 15 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Full Sample Moving correlations between quarterly changes in ILO rate & card delinquency rate Macroeconomic factors & retail credit risk Example: UK Credit Card Delinquency & Unemployment

16 16 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Moving regression coefficients between changes in ILO rate & card delinquency rate Full Sample Macroeconomic factors & retail credit risk Example: UK Credit Card Delinquency & Unemployment

17 17 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Predictive accuracy for model estimated on alternative 10 year sub-samples Macroeconomic factors & retail credit risk Example: UK Credit Card Delinquency & Unemployment

18 18 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Macroeconomic factors & retail credit risk It’s Difficult  Some conclusions on aggregate analysis: ► Estimated relationships between credit outcomes and macroeconomic factors vary greatly depending on the estimation sample ► In practice, estimating models linking short time series of historical credit data to macroeconomic factors is highly likely to establish spurious / over-estimated relationships to particular economic variables.  Similar results in other applications using macroeconomic data: ► Loss models fitted on aggregated or account-level lender data ► Argues against explanations based on model specification ► Problem is (partially) mitigated in models using disaggregated economic data – e.g. unemployment by local area or by age To identify robust economic models for forecasting and stress testing need to maximise information extracted from available data – avoiding unnecessary and wasteful aggregation of credit or economic data

19 19 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ‘True’ Pr(good): ‘Augmented ’ Credit Scorecard: where P ijt = probability of good (1-PD) for individual i in population j at time t Y it-1 = observable credit history for i up to t-1 S it = (generally unobservable) individual ‘situational’ factors influencing i at t X ijt = economic conditions influencing i in population j at t Note: In general S and X vary by borrower, may include lags, and F may include interaction terms This approach provides maximum flexibility and transparency while efficiently utilizing all available data where G j (y it-1 ) = standard credit score H j (X ijt ) = economic risk score (calibration adjustment) Use appropriate estimation and inference methods to fit and test models, allowing for (a) potential endogeneity between economic factors and credit score; and (b) error structure given use of aggregated time varying economic data. Macroeconomic factors & retail credit risk It’s Possible: Disaggregated Models

20 20 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public  Most countries publish rich economic data in far more detail than high level macroeconomic series – e.g. for regional and local geographies.  This disaggregated data is potentially more relevant to the conditions of individual borrowers/accounts than are macroeconomic aggregates.  Careful segmentation of accounts can also allow models to capture the variation in sensitivity of different borrowers/accounts to economic conditions. Macroeconomic factors & retail credit risk It’s Possible: Maximizing use of sub-national data

21 21 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public ©2013 Experian Ltd. All rights reserved. Experian Public Macroeconomic factors & retail credit risk Conclusions  There is a need reliably to link credit outcomes to economic conditions for loss forecasting, stress testing (and many other operational & strategic uses)  This raises big challenges for model specification and selection of economic variables – given limited information available for estimating models and the statistical characteristics of candidate economic variables.  Current approaches tend to emphasize a small set of macroeconomic factors (or functions of those factors) that correlated with historical trends in credit outcomes – but some of those relationships appear to be unstable and/or to have broken down in more recent history.  Similar to an early-stage epidemiological study? – we can find correlations but we need to validate their robustness and to understand the mechanisms involved before we hang too much on the results  Is it time to rethink this problem - focusing on identifying & understanding proximate economic drivers of default/delinquency and firmly linking those to reliably measurable proxies? ► Dig deeper into the economic data. ► Exploit variation in disaggregated data for model estimation and loss forecasting.

22 22 ©2012 Experian Information Solutions, Inc. All rights reserved. Experian Public. ©2013 Experian Ltd. All rights reserved. Experian Public


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