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Predicting fraud behaviour in online betting
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Resumo(s)
Fraud isn’t a new issue, there are discussions about this matter since the beginning of commerce. With the advance of the Internet this technique gained strain and became a billion-dollar business. There are many different types of online financial fraud: account takeover; identity theft; chargeback; credit card transactions; etc. Online betting is one of the markets where fraud is increasing every day.
In Portugal, the regulation of gambling and online betting was approved in April 2015. In one hand, this legislation made possible the exploration of this business in a controlled and regulated environment, but on the other hand it encouraged customers to develop new ways of fraud. Traditional data analysis used to detect fraud involved different domains such as economics, finance and law. The complexity of these investigations soon became obsolete. Being fraud an adaptive crime, different areas such as Data Mining and Machine Learning were developed to identify and prevent fraud.
The main goal of this Project is to develop a predicting model, using a data mining approach and machine learning methods, able to identify and prevent online financial fraud on the Portuguese Betting Market, a new but already strong business.
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
Palavras-chave
Online Fraud Betting Market Data Mining Machine Learning Portugal