Bruno Carniero Camara is studying Electrical Engineering at the University of Sao Paulo in Brazil.
Bruno became fascinated with computer science and programming in his first year of study. Since then, he has been extending his knowledge in many related areas including the Internet of Things, Robotics, Web Applications, Data Science and programming in general. In early 2021, he took his first Big Data and Analytics classes, working on a project that culminated in producing final project for his course and the research and results are reflected in his poster.
Tons of money is lost because of fraud committed by companies. There are already laws to punish company partners for these abusive acts for their own benefit, however, how can the authorities locate and take the necessary actions? This is where my work comes in. Identifying registration inconsistencies, suspicious behaviors or unusual situations may prevent or locate frauds. Using three different public databases as the starting point, I was able to link companies and partners to suspicious behaviors, such as receipt of undue government benefit by company partners and reports of work analogous to slavery in companies.
The three public databases used were:
- Brazilian Companies - Divided into 3 categories of Companies, Partners and Establishments. All of them have specific information, such as company status, partner position, age group, localization, date and others.
- Government Subsidy - The people who received government aid. I have chosen 2 famous Brazilian subsidies: Bolsa Família and Auxílio Emergencial. The two subsidies are aimed at people with low income.
- Work Analogous to Slavery - Information about companies or employers that practice slavery-like work.
All these datasets are publicly available on the Brazilian government websites.
With the three databases properly treated and cleaned I was able to reach my goal of identify suspicious behaviors, unusual situations and registration inconsistencies, obtaining three main datasets: partners that received benefits and their respective companies, establishments that had some type of complaint about work analogous to slavery and partners with reports of labor practices analogous to slavery. Those 3 resulting datasets were descriptively analyzed in order to highlight group singularities and trends. The most relevant keys used to analyze groups were: partner position, partners’s age group and area of activity of the company. One of the trends highlighted was: 49% of the partners that received benefits were managing partners which is a high position to receive assistance,
In short, this project shows that by combining Big Data and Analytics with HPCC Systems could be a promising alternative to prevent and locate frauds, using registration inconsistencies as a tool to raise potential fraudulent companies and company partners.
In this Video Recording, Bruno provides a tour and explanation of his poster content.
Preventing Fraud by Registration Inconsistencies
Click on the poster for a larger image.