Abstract
Transaction traces analysis is a key utility for marketing, trend monitoring, and fraud detection purposes. However, they can also be used for designing and verification of contextual risk management systems for card-present transactions. In this paper, we presented a novel approach to collect detailed transaction traces directly from payment terminal. Thanks to that, it is possible to analyze each transaction step precisely, including its frequency and timing. We also demonstrated our approach to analyze such data based on real-life experiment. Finally, we concluded this paper with important findings for designers of such a system.
Highlights
The design and evaluation of new systems, algorithms, and techniques heavily rely on the existence of productive datasets
To extend and improve such a situation, in the paper [42], we proposed a new Cardholder’s reputation system that can be utilized in contextual risk management solutions for payment transactions
5 Conclusion and future work Transaction traces collected in various points of payment ecosystem are a valuable source of information for diverse analyses
Summary
The design and evaluation of new systems, algorithms, and techniques heavily rely on the existence of productive datasets. It was firstly discussed in [44], where we proposed a new Cardholder Verification Method which is One-time PIN verification This method assumes that each transaction is authorized online and the decision if PIN verification should be performed is being made by the Issuer based on various contextual factors (like Cardholder’s reputation, place, and time of the transaction). To extend and improve such a situation, in the paper [42], we proposed a new Cardholder’s reputation system that can be utilized in contextual risk management solutions for payment transactions. This reputation system covers all possible transaction flows. The sum of all presented tokens is 19,042; it means that generally, loyal customers of one retail chain are usually making their transaction in one store
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