  Passenger Name Records (PNRs) are at the heart of the travel industry.
Created when an itinerary is booked, they contain travel and passenger
information. It is usual for airlines and other actors in the industry to
inter-exchange and access each other's PNR, creating the challenge of using
them without infringing data ownership laws. To address this difficulty, we
propose a method to generate realistic synthetic PNRs using Generative
Adversarial Networks (GANs). Unlike other GAN applications, PNRs consist of
categorical and numerical features with missing/NaN values, which makes the use
of GANs challenging. We propose a solution based on Cram\'{e}r GANs,
categorical feature embedding and a Cross-Net architecture. The method was
tested on a real PNR dataset, and evaluated in terms of distribution matching,
memorization, and performance of predictive models for two real business
problems: client segmentation and passenger nationality prediction. Results
show that the generated data matches well with the real PNRs without memorizing
them, and that it can be used to train models for real business applications.
