By Fidel Beraldi, Fraud and Risk Department, First Data Brazil and Alair Pereira do Lago, Department of Computer Science, Universidade de São Paulo
Editor’s note: Fraud prevention technology continues to become more sophisticated as e-commerce merchants and those trying to steal from them move and countermove. But, the research behind advances in fraud prevention is not always visible, even within the industry. Fidel Beraldi, a fraud and risk manager with First Data in Brazil has agreed to share recent research conducted by Dr. Alair Pereira do Lago, a faculty member in the Department of Computer Science at the Universidade de São Paulo on a new statistical model that can be applied to fraud prevention that will enable fraud scoring systems to adapt to shifting fraud techniques quicker.
While the tone of this article is academic, it has been adapted from a much longer research report. It does retain some statistical language that is more sophisticated than usually appears on CardNotPresent.com. Readers interested in clarification or discussing the results in more depth with the authors can contact Fidel Beraldi at firstname.lastname@example.org .
Fraud indicators have shown that card-not-present transactions are riskier than card-present transactions, due to the fact that neither the cardholder nor the card itself is physically present at the point of sale. In these scenarios, more opportunities are created for fraudsters to produce new methods, resulting in large losses for the financial system.
As fraudsters quickly adapt to existing fraud prevention measures, statistical models for fraud detection also need to be adaptable and flexible to change over time in a dynamic way. Fraud scoring models can be updated sporadically or continuously over time, which raises the question of dynamic update of the model parameters to detect fraud.