|Year of selection||2016|
|Institution||Finance Faculty of Economics Università della Svizzera Italiana|
|Risk||Data & Tech Risks|
Type of support
130 000 €
Payment methods are undergoing a revolutionary change. The use of cash and checks has been steadily decreasing since the beginning of the 1990s, progressively being replaced by card transactions, and more recently by online money transfer systems like PayPal. In 2015, the ratio of paper-based to electronic transactions in Europe was approximately one to eight. While electronic payment methods facilitate purchases and boost business, they also represent a gold mine for fraudsters. In 2014, the overall worldwide losses from card fraud totaled over 16 billion US dollars, and the amount is expected to double by 2020. To minimize the loss suffered by banks, insurers and consumers, Dr. Bruno Buonaguidi is developing rapid detection techniques that could improve current methods, in particular by avoiding false alarms.
Indeed, the ways in which cyber fraud is currently confronted by card issuers and online payment services providers can result in ill-judged and unnecessary blocks, creating inconvenience for the client and damaging customer relationships. Dr. Bruno Buonaguidi’s objective is to minimize this risk, while at the same time detecting anomalies as fast as possible to counteract illegal activities. In other words, the goal is to obtain a better trade-off between false alarms and quick detection. To achieve his aim, Dr. Buonaguidi is developing a theoretical model that applies a well-known mathematical theory, called optimal stopping theory, to the context of fraud detection. This branch of mathematics is about choosing the right time to take a given action in order to maximize an expected payoff or to minimize an expected cost.
Optimal stopping theory: applying probability and statistics to fraud detection
"In current methods, the normal card user pattern of expenditures is constructed and then subsequent expenditures are analyzed to determine if there are deviations," explains Dr. Bruno Buonaguidi. "In our method, based on optimal stopping theory, the expenditures pattern of a card user is monitored constantly over time. When the probability of having a fraud exceeds a certain threshold, then we can declare its occurrence." To illustrate how optimal theory works, Dr. Bruno Buonaguidi uses the following comparison: "Imagine you are observing the evolution of a stock price. You want to know when to sell or buy the stock in order to make a profit. What you need to be looking at is ‘the change point’, the time at which a change occurs in the features of the stock. That way, you are able to react promptly and save or make money. By using optimal stopping theory, you can infer that ‘change point’ by monitoring the stock market data as it is collected. Coming back to our context, the same goes with the expenditures of card users."
Working on a theoretical model is the first step in Dr. Buonaguidi’s research plan. The second will be to develop a technology that will be used together with current methods for the detection of cyber fraud. The ultimate goal is to contribute to the challenging problem of rapidly and correctly disclosing frauds in electronic transactions. In this sense, Dr. Bruno Buonaguidi’s research could have an important impact on society. Indeed, the negative consequences of frauds in electronic payments are not only suffered by banks, but also by merchants, card users and insurance companies. Despite the progress made in the field of electronic fraud detection, especially artificial intelligence methods, the current cost of electronic fraud remains extremely high and is expected to increase in the future. Current methods would benefit from an additional instrument capable of disclosing fraud more rapidly, but also more efficiently to avoid unjustified blocks