Usage of Distributed Systems Data for Automated Financial Health Advice

Abstract: Financial health is a subject that deeply affects the choices and decisions one takes everyday, but for most people, financial concepts are difficult to understand. Financial illiteracy worldwide amounts to approximately 67%, and in Sweden, being one of the world’s least financial illiterate countries, it is around 29% [1]. The goal of this research is to be able to provide more insights into an individual’s financial health situation. During the development, several steps have been taken so as to make a program which will predict the customer’s risk capacity, which means the risk an individual has to take in order to achieve his or her goals. First, a program is developed to map occupations into their closest job titles using NLP, which has to be done so as to be able to use occupations for Data Mining. Then, a case-based recommender system is developed in order to get insights into the customer’s data about the their personal financial situation. Finally, based on the above, using the customer’s profile information and following the CRISP-DM methodology, be able to predict the risk capacity of the customer using Machine Learning and Deep Learning techniques.