# A Generative Adversarial Network for Financial Advisor Recruitment in Smart Crowdsourcing Platforms
## 1 Introduction
Most of the previous work dealing with the problem of financial advisor retention are based on empirical studies and surveys. In this study, we propose to tackle the problem by modeling it as an automated crowdsourcing system. In fact, crowdsourcing can be employed to help in addressing the portfolio optimization problems. In the context of our paper, we propose to help investors recruit financial advisors either humans or robots, in order to assist them and provide optimized portfolios that match their profile and revamp their financial outcomes. Crowdsourcing can be employed to overcome some of the challenges faced by investors when recruiting financial advisors. Indeed, the area of expertise, the efficiency, and the strategies of advisors to solve the POP as well as the psychological characteristics of the investor may constitute important factors that highly impact the portfolio management decisions [41]. For example, low risk-takers prefer to invest their money on stocks since they have lower volatility than real estate and commodities, therefore, they need financial advisors with good expertise in such assets. Thus, it is a must for every investor to wisely recruit the financial advisor or the group of financial advisors that matches his/her strategy and preferences in order to build a portfolio that suits the targeted financial planning goals. While other studies focus on proposing new algorithms and optimization techniques of the portfolio itself [42; 43], we believe that efficiently matching investors with financial advisors has a significant impact on the portfolio's return. The authors of [44] investigated the selection of financial advisors by considering the gain maximization as the main criterion for recruitment. A machine learning approach has been designed to select the financial advisor providing the maximum predicted expected return. In this paper, we propose to conceive a smart crowdsourcing platform to enable investors recruiting potential financial advisors that best match their profiles and preferences as well as their financial goals. The proposed platform is designed to automatically suggest for a given investor, also known as requester, the potential financial advisors, also known as workers, that are more likely to match the investors' profile and hence, provide optimized financial portfolios that respond to the latter's financial goals. The framework operates with regards to both investors' and advisors' profiles defined by various features representing the budget, risk preferences, domain of expertise, etc. All the features defining investors and financial advisors are stored in the crowdsourcing platform database. An unsupervised deep learning model, namely Generative Adversarial Networks (GAN) is trained based on the previous cooperation of different pairs of investors and financial advisors. The training data set contains pairs of investors and corresponding optimal financial advisors. Afterwards, the GAN is used to predict for a given investor'sprofile the corresponding optimal financial advisor's profile. Borrowed from the image processing field, the profiles of financial advisors are encapsulated in gray-scale images. Hence, the GAN aims to generate gray-scale images given the features of the clients. These images are defined as the financial advisors' signatures. Then, we preform an optimized matching technique, which is modeled as an Integer Linear Programming (ILP) and solved heuristically using bipartite graph-based algorithms to assign available financial advisors to the investors. To the best of our knowledge, we are the first to propose a framework with the following contributions:  
* We develop a smart crowdsourcing approach to leverage the process of financial advisors recruitment. * We combine the concept of crowdsourcing and artificial intelligence to optimize the recruitment of financial advisors and hence, enhance the portfolio's performances.