In the second phase, the behavior rules for normal customers in the first phase are used to eliminate the customers who satisfied the rules, and the percentage of default customers in the data therefore increases. In this phase, a decision tree is formed for drawing predicts from and analyzing the rest of the data.
However,there are many different kinds of default behavior.
The dates signed by customers are different, and some new customers have no historical data. In order to find the customers who met the
objective, different attributes are selected for analysis to produce different decision trees and rules.
After verification, the rules that are more than 80% accurate are selected and stored in an SQL database in order to find the target customers