Working with or within the travel industry means constantly having to handle requests of customers coming from all around the world, using various cards and having diversified habits and preferences as to how they shop online. For instance, in Latin America, it is commonplace to pay for anything in instalments - even for inexpensive products or services.
Carrying out one transaction with several cards wouldn’t raise an eyebrow either. With such plentiful region-specific nuances, transactions done by travellers often exhibit sets of features that could easily be taken for fraud designates. Just imagine a situation in which the card was issued in the UK, the computer’s physically based in Poland, and the browser’s default language is set to Spanish, but the billing address indicates the Netherlands. Unlikely enough? Most fraud detection tools would flag such a transaction as unequivocally suspicious and block it. In reality, however, the traveller might be a Briton who owns a business based in the Netherlands, is trying to learn Spanish after hours and happen to be in Kraków, sightseeing. An online travel agency should, therefore, employ a system capable of conducting analysis so profound as to let that British entrepreneur carry out their transaction uninterrupted.
Both risk managers and rule-based fraud detection systems will not do their job effectively if the transactions come from all the corners of the world possible. Risk experts tend to specialise in selected regions. It is not really doable to be an expert in all markets of the world, isn’t it? Their knowledge and experience are converted into rules aimed at fraud detection. Nonetheless, a rule that’s 100% true for Poland may result in declining hundreds of perfectly legitimate transactions from some other country. Conclusion? Well, the human cognitive filter or abilities, whatever you call it, are limited. Given the complexity of the world of payments and its fragmentation, it is far more effective to have the data analysed by a machine rather than to put an employee in charge of the mission. Supplied with extensive data, a machine is capable of detecting fraudsters with accuracy not encountered before. Even if the situation is as twisted as the one with the British business person. The team, in turn, can concentrate on the configuration of the system and learning about different markets instead of running ineffective and time-consuming analyses.