Data defines the model by dint of genetic programming, producing the best decile table.

Credit Risk Modeling – A Machine Learning Approach
Bruce Ratner, Ph.D.

With increasing delinquencies and credit losses on a national scale, creditors are taking a total view of credit risk from a portfolio level. This is in stark contrast to current credit risk management at the loan level by setting loan-level credit criteria. Creditors are using statistical regression modeling to predict future loan losses at the portfolio-level. However, the statistical regression approach is proving ineffective, because the regression paradigm of “fitting the data” to a pre-specified model is not tenable. The credit-risk data captures fluctuating economic conditions and ever-changing loan characteristics that makes fitting the data virtually impossible. The purpose of the article is to present the GenIQ Model© that lets the “data specify the model,” regardless of the data’s fluctuating conditions and ever-changing characteristics. GenIQ provides forecasted portfolio losses, which goes beyond the limited risk score.

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