  FinTech platforms facilitated by digital payments are watching growth
rapidly, which enable the distribution of mutual funds personalized to
individual investors via mobile Apps. As the important intermediation of
financial products investment, these platforms distribute thousands of mutual
funds obtaining impressions under guaranteed delivery (GD) strategy required by
fund companies. Driven by the profit from fund purchases of users, the platform
aims to maximize each transaction amount of customers by promoting mutual funds
to these investors who will be interested in. Different from the conversions in
traditional advertising or e-commerce recommendations, the investment amount in
each purchase varies greatly even for the same financial product, which
provides a significant challenge for the promotion recommendation of mutual
funds. In addition to predicting the click-through rate (CTR) or the conversion
rate (CVR) as in traditional recommendations, it is essential for FinTech
platforms to estimate the customers' purchase amount for each delivered fund
and achieve an effective allocation of impressions based on the predicted
results to optimize the total expected transaction value (ETV). In this paper,
we propose an ETV optimized customer allocation framework (EOCA) that aims to
maximize the total ETV of recommended funds, under the constraints of GD dealt
with fund companies. To the best of our knowledge, it's the first attempt to
solve the GD problem for financial product promotions based on customer
purchase amount prediction. We conduct extensive experiments on large scale
real-world datasets and online tests based on LiCaiTong, Tencent wealth
management platform, to demonstrate the effectiveness of our proposed EOCA
framework.
