As the Basel Accords continue to drum up attention in the global financial markets, many institutions are looking at how they can strike a balance between capital requirements and competitive advantage. One area of focus is consumer credit risk modelling and scoring - as the more accurate and robust the models are, the lower the risk institutions face. While credit modelling has traditionally been based on linear models, it is becoming more apparent that non-linear techniques (e.g. Gradient Boosting, Neural Networks) can make significant improvements to the accuracy of default models and ultimately support an institution’s bottom line.
Breaking with tradition
Is it time for financial institutions to break free from using traditional linear models simply because “that is the way we have always done it” and accept the capabilities and advantages that more advanced predictive modelling techniques can bring?
Over the last few years (both during my PhD research and working in the financial sector), I have assessed and developed predictive modelling techniques that are applicable to estimating:
- The probability of a customer going into default (PD).
- The resultant loss experienced by the company given a customer defaults (LGD).
- The exposure faced by an organisation at the point in time a customer defaults (EAD).
These three aspects make up Pillar 1 of the Basel Accord which prescribes financial institutions calculations for their minimum capital requirements (the minimum amount of capital they are regulated to hold) and are fundamental in determining how much institutions must hold and how much they can lend out to customers in the form of personal loans, mortgages and other forms of credit.
Under the advanced internal ratings-based approach (AIRB), banks have the ability to provide their own internal estimations to the regulators for each of these three aspects: PD, LGD and EAD. As a rule of thumb, linear regression models are used in the estimation of LGD and aspects of EAD, whereas logistic regression is used in the derivation of PD. A typical distribution for an LGD portfolio, for example, is bi-modal with two large point densities around 0 and 1, with a shallow distribution between the peaks (see figure). In practice, it is common to apply a beta transformation to the target variable, and then estimate this transformed value with a linear regression model.
In with the new
From the research conducted, however, a vast improvement in the estimation of LGD could be made with a two-stage approach where a neural network model is trained on the residuals of a linear regression model, therefore combining the comprehensibility of a linear regression model with the added predictive power of a non-linear technique. (For a more detailed discussion of the issues related to implementing a two-stage approach for estimating LGD, please see Loterman, et al. 2011).
One of the major fallouts of the 2008 global financial crisis was that regulators clamped down on financial institutions to make sure that both regulators and institutions fully understand their internal risks and can fully prove without doubt they understand their underlying models. The problem with this requirement is that financial institutions have subsequently become more averse to adopting new ideas and ever more entrenched in the ways of the past. They have also spent a huge investment of time and resources catching up with and providing documentation to the regulators. I am all for the stringent controls of regulatory bodies, but I believe there is still room for these financial institutions to think outside of the ‘white’ box and explore other approaches to model development.
There is merit to using linear regression techniques due to their clarity and ease of use, and more importantly advanced analytical techniques need to be fully understood before data is thrown into them. But with the right amount of knowledge and openness to try new ideas, financial institutions could potentially reap the benefits of applying novel analytical techniques (i.e. improved prediction rates, more accurate capital estimations).
The key would be for financial institutions to embrace the potential of using approaches novel to the financial sector that have been proven in a number of other sectors, such as healthcare, fraud detection and marketing (neural networks for credit card fraud detection, for example, have been used successfully in the detection of abrupt changes in established patterns and recognising typical usage patterns of fraud).
This use of innovation for modelling their credit risk portfolios would also encourage institutions to not fall behind in other sectors in the use of novel analytical techniques, as well as challenge the regulators to show that advanced analytical techniques can in fact lead to better models and better estimations of risk.
For links to papers I’ve written on the area of applying SAS based analytical modelling techniques in the financial sector please see:
- Brown I (2011), Regression Model Development for Credit Card Exposure At Default (EAD) using SAS/STAT® and SAS® Enterprise Miner™ 5.3, SAS Global Forum 2011, Conference proceedings
- Loterman G, Brown I, Martens D, Mues C & Baesens B (2011),Benchmarking Regression Algorithms for Loss Given Default Modeling, International Journal of Forecasting, In press
- Brown I & Mues, C (2012) An Experimental Comparison of Classification Algorithms for Imbalanced Credit Scoring Data Sets, Expert Systems with Applications, Volume 39, Issue 3, 15 February 2012, Pages 3446-3453
*NOTE: Originally published on SAS Voices.