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Grana - a smart personal financial manager

Grant number: 17/01043-6
Support type:Research Grants - Innovative Research in Small Business - PIPE
Duration: November 01, 2017 - July 31, 2018
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computer Systems
Principal researcher:Renan Ferrari Bangoim
Grantee:Renan Ferrari Bangoim
Company:Grana Tecnologia Eireli - ME
City: São Paulo
Assoc. researchers: Caezilia Loibl ; Fernando Abe Ohara ; Gustavo Toshio Yasunaka
Associated scholarship(s):17/22552-6 - Grana: a smart personal financial manager, BP.PIPE

Abstract

From the last years of the twentieth century until the present moment, the so-called "Behavioral Economics" has gained a certain relevance in the academic field, a branch of research that incorporates in the field of Economics certain theoretical developments and empirical discoveries of psychology, neuroscience, among other human sciences, such as marketing and design. In short, behavioral economics sees a reality made up of people who make economic decisions quickly, based on habits, personal experiences, and simplified practical rules. Also, it is understood that they have difficulty reconciling short- and long-term interests, as well as having their decisions strongly influenced by social and emotional factors. At the same time, more and more researchers in the area of information technology seek to explore the recent advances in behavioral economics to apply them together with mass data analysis and machine learning techniques. The aim is to design digital products that are close to human decision making, helping people to make better decisions. This project aims to offer this type of technology so that people, regardless of their financial literacy, are able to make more conscious decisions for their personal finances. To do so, it is proposed to adapt these techniques of information technology to analyze a large set of financial information of users, collected through the application for smartphone Grana, an application developed by this team and aimed at personal financial management. By following some of the fundamentals of Behavioral Economics, a possible way to help people lead a healthier financial life would be to simplify making good decisions. In personal finances, at the most elementary level, good decisions imply spending less than the income. That is, one way to help this profile is by simplifying the decision-making that will lead you to spend within a budget that matches your income. In this sense, what Grana proposes to research with this project is the viability of the application to make recommendations of realistic daily budgets in a personalized way. By using massive data collected from bank statements and interpreting them, by data analysis and machine learning, one expects to be able to predict how much the user could spend each day automatically, saving him the effort of having to do this on its own. With the results of this research, it would be possible to implement in-app the daily budget functionality and then improve searches linked to the tool's effectiveness in helping people save more and get less debt. (AU)

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