Adjusting the credit lines of card users is an important issue. It is essential to establish an optimized approach for credit card companies to identify the proper amount of credit to offer for their customers. Most of the related research concentrated on the prediction of credit card users? default. Our contribution is a consideration of a holistic and heuristic approach that looks at the credit line that maximizes the net profits of the credit card companies. We first apply regression models to find the probability of default of customer and customer?s current balance as a function of credit line. Next we use a regression tree to identify groups of customers assigned with the same credit line. The results are then used to formulate the net profit and genetic algorithm is used to find optimally adjusted credit line for each group of customers. It is expected that our study can contribute to present strategic guidelines for the management of credit lines for card companies.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIP) ( 2013R1A2A1A09004699 ).
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Modelling and Simulation
- Management Science and Operations Research