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Here’s A Better Way to Predict Credit Risk

Reprinted by permission of the Federation of Credit and Financial Professionals

Accurate prediction of the future is the ultimate goal of credit risk analysis. How much risk should you take on an account? Will they pay on a timely basis? How can you determine when to pull the plug and not sell them anymore? These are some of the questions a credit manager is expected to have answers to. And now there is a better way to help answer these questions using a revolutionary technique that relies on more than one model to solve these problems, a so called ensemble model. This technique has far better accuracy than traditional statistical-based analysis and judgment-based models are left in the dust.

What is an Ensemble Model?

An ensemble model combines two or more models to enable a more accurate prediction of credit risk. If you have a current statistical model that you like it can become part of the ensemble, but its accuracy will be significantly enhanced by the other models in the ensemble. The goal of the ensemble is to predict which accounts will go BAD and which will not. Where BAD is your definition of BAD (for example, an account might be considered BAD if more than 20% of its total monthly outstanding balance goes 91 or more days past due).

Building an Ensemble Model

Like most scientific-based models an ensemble model, for your company, will be created from your historical data. Anything you know about your accounts is applicable. The table below shows some of the possible data that the model might use:

 

The first step in the model building process is to determine which variables are the most predictive and should be considered for use in the final model. The off-the-shelf software used in this application has the capability to help you do this. In a given application you might start off with 100 possible variables and wind up with a fraction of that which are used in the final model.

Once the predictive variables are determined, multiple models will be developed with different variables receiving a variety of weights. Again, the software has the ability to vary the models and help you decide which ones to use in the ensemble. You might create 25 models, but use only five of them in the ensemble.

To read the entire article that includes:

  • Using the Ensemble to Determine a Credit Line
  • Model Output Good or Bad
  • Using the Ensemble as an Early Warning system

visit HERE

Albert Fensterstock is managing director of Albert Fensterstock Associates. He has more than 40 years’ experience in financial and operations management and analysis. He specializes in the development and implementation of decision support systems based on various types of predictive analytics including statistical analysis, neural networks and simulation technology. His solutions are frequently used for improving risk analysis capability and collection department efficiency. He can be reached at 516-313-1020 or via e-mail at albie@afassociates.org

 

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