Customer credit scoring is a dynamic interactive process. Simply designing the static reward
function for deep reinforcement learning may be difficult to guide an agent to adapt to the …

Only a few customers can be labeled in realistic credit-scoring problems, while many other
customers cannot. Further, satisfactory performance is difficult, as traditional supervised …

Decision tree (DT) is one of the most popular classification algorithms in data mining and
machine learning. However, the performance of DT based credit scoring model is often …

Recently, the machine learning method and artificial intelligence algorithm have become
increasingly important in classification problems, such as credit scoring. Building an …

Credit scoring models based on accepted applications may be biased and their
consequences can have a statistical and economic impact. Reject inference is the process …

Consumer credit scoring is an essential part of credit risk management in the fast-growing
consumer finance industry and various data mining techniques have been proposed and …

Credit scoring is an important tool for banks and lending companies to realize credit risk
exposure management and gain profits. GBDTs, a group of boosting-type ensemble …

In order to improve the marketing effect of e-commerce products, based on machine learning
algorithms, this paper constructs an e-commerce product marketing model based on …

Credit scoring systems have seen revolutionary development in recent decades, with many
classification algorithms being proposed. However, with the increase in the data volume, the …

With the rapid development of the credit industry, credit scoring models has become a very
important issue in the credit industry. Many credit scoring models based on machine …