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

Expected Transaction Value Optimization for Precise Marketing in FinTech Platforms