  Secure online transaction is an essential task for e-commerce platforms.
Alipay, one of the world's leading cashless payment platform, provides the
payment service to both merchants and individual customers. The fraud detection
models are built to protect the customers, but stronger demands are raised by
the new scenes, which are lacking in training data and labels. The proposed
model makes a difference by utilizing the data under similar old scenes and the
data under a new scene is treated as the target domain to be promoted. Inspired
by this real case in Alipay, we view the problem as a transfer learning problem
and design a set of revise strategies to transfer the source domain models to
the target domain under the framework of gradient boosting tree models. This
work provides an option for the cold-starting and data-sharing problems.
