An Investment Recommender Multi-agent System in Financial Technology

Abstract

In this article is presented a review of the state of the art on Financial Technology (Fintech) for the design of a novel recommender system. A social computing platform is proposed, based on Virtual Organizations (VOs), that allows to improve user experience in actions that is associated with the process of investment recommendation. The work presents agents functionalities and an algorithm that will improve the accuracy of the Recommender_agent which is in charge of the Case-based reasoning (CBR) system. The data that will be collected and will feed the CBR corresponds to user’s characteristics, the asset classes, profitability, interest rate, history stock market information and financial news published in the media.