Credit Card Logistic Modeling Principles, Methods and Wind Control Strategy Building

Abstract

Now there are many methods of credit card modeling, of which Logistic regression is the most commonly used. Logistic regression has been modified since its introduction. The core of logical regression is theoretically supported by linear regression, with the introduction of nonlinear factors to deal with the secondary classification problem through the sigmoid function. This paper aims to explore the principles and processes of logic regression specifically, and to use logical returns for rating card modeling. It is therefore recommended that banks use credit cards for effective risk management. Logical regressions are more influenced by the data, so in the data processing link, we use sample partitions and distribute conversions to WOE values. At the same time, use examples to validate conclusions and to present the advantages and disadvantages of Logical Regression. This article also proposes the need for back-end monitoring of scorecard models and requires artificial judgment of whether the data type of the client reveals valid characteristics.