Yet Another Blog in Statistical Computing

I can calculate the motion of heavenly bodies but not the madness of people. -Isaac Newton

Integration of Economic Information in Credit Scorecard

The incorporation of economic information in the credit scorecard has been researched extensively in the academic world and started to receive increasing attention in the banking industry since the most recent global economic recession.

To our knowledge, the conventional usage of economic information in the credit scorecard is mostly for the segmentation purpose, e.g. to segment the business footprint into couple geographic regions based upon the economic heterogeneity. However, scorecard models are still developed solely on the information of credit profile and payment history. In our view, this is a static approach without the consideration of economic dynamics.

During the course of 2008 recession, most lending organizations have observed performance decay in their scorecards as a result of the economic downturn. Three major remedies for scorecard deterioration had taken place with the incorporation of economic information. First of all, a quick patch considered by most banks was to tighten up credit policies by increasing scorecard cutoffs in stressed areas based on their economic conditions, e.g. unemployment rates and housing price changes. Secondly, hybrid risk models were developed in many risk workshops by combining existing scorecards with economic data in order to restore the rank order capability and expected odds ratio. Thirdly, with the most recent credit profiles and payment behaviors of customers during the downturn, many scorecards were re-developed so as to guard against the rapid increases in non-performing loans and credit losses. Whilst approaches described above are able to address scorecard performance issues in a timely manner, they are still considered short-term treatments after the occurrence of recession and often tend to over-correct the underlying problems at the cost of missing revenue opportunities. Consequently, these approaches are inevitably subject to future adjustments or replacements in the economic recovery.

In light of the above discussion, a question raised is how we can effectively take advantage of economic information to improve the predictability and stability of scorecards through the economic cycle. Based upon our experience, a sensible resolution is to overlay economic data on top of the traditional scorecard development procedure by directly using economic indicators as model predictors. While the most predictability of a scorecard would still come from individual-level credit characteristics, economic indicators are able to complement credit attributes and to provide additional predictive lift to justify their values. For instance, given two individuals with identical risk profiles but living two MSAs with different economic outlooks, it is obvious that the one in a stressed local economy is more likely to become delinquent. Despite the predictability, a scorecard developed with a built-in economic trend is more robust and able to fluctuate automatically along with the cycle, reducing the necessity of scorecard adjustments in the dynamic economy. In addition, the inclusion of leading indicators or economic projections in the scorecard enables us to have a forward-looking prediction capability, which provides us an opportunity to proactively employ early interventions and preventions in loss recovery and mitigation initiatives.

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Written by statcompute

March 24, 2012 at 10:55 am

Posted in Loss Forecasting

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