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.

翻译：数字支付推动下的金融科技平台正快速增长，这些平台通过移动应用实现个性化匹配个人投资者的共同基金分销。作为金融产品投资的重要中介，这些平台依据基金公司要求的保证交付（GD）策略，分发数千只共同基金以获得曝光。受用户基金购买利润驱动，平台旨在通过向潜在感兴趣的投资者推广共同基金，最大化每位客户的交易金额。与传统广告或电商推荐中的转化不同，即使是同一金融产品，每次购买的投资金额也差异显著，这为共同基金的推广推荐带来了重大挑战。除预测点击率（CTR）或转化率（CVR）外，金融科技平台还需估算客户对每只分发基金的购买金额，并基于预测结果实现曝光资源的有效分配，以优化总期望交易价值（ETV）。本文提出一种面向ETV优化的客户分配框架（EOCA），旨在满足与基金公司约定的GD约束下，最大化推荐基金的总ETV。据我们所知，这是首次基于客户购买金额预测解决金融产品推广GD问题的尝试。我们基于腾讯理财通平台的真实大规模数据集进行广泛实验与在线测试，验证了所提EOCA框架的有效性。