  The prosperity of mobile and financial technologies has bred and expanded
various kinds of financial products to a broader scope of people, which
contributes to advocating financial inclusion. It has non-trivial social
benefits of diminishing financial inequality. However, the technical challenges
in individual financial risk evaluation caused by the distinct characteristic
distribution and limited credit history of new users, as well as the
inexperience of newly-entered companies in handling complex data and obtaining
accurate labels, impede further promoting financial inclusion. To tackle these
challenges, this paper develops a novel transfer learning algorithm (i.e.,
TransBoost) that combines the merits of tree-based models and kernel methods.
The TransBoost is designed with a parallel tree structure and efficient weights
updating mechanism with theoretical guarantee, which enables it to excel in
tackling real-world data with high dimensional features and sparsity in $O(n)$
time complexity. We conduct extensive experiments on two public datasets and a
unique large-scale dataset from Tencent Mobile Payment. The results show that
the TransBoost outperforms other state-of-the-art benchmark transfer learning
algorithms in terms of prediction accuracy with superior efficiency, shows
stronger robustness to data sparsity, and provides meaningful model
interpretation. Besides, given a financial risk level, the TransBoost enables
financial service providers to serve the largest number of users including
those who would otherwise be excluded by other algorithms. That is, the
TransBoost improves financial inclusion.

Machine Learning;Statistical Finance