Abstract: Embodiments described herein are directed towards a synthetic document generation pipeline for training artificial intelligence models. One embodiment includes a method including a device that receives an instruction to generate a document to be used as a training instance for a first machine learning model, the instruction including an element configuration, a document class configuration, a format configuration, an augmentation configuration, and data bias and fairness. The device can receive an element from an interface based at least in part on the element configuration, the element can simulate a real-world image, real-world text, or real-world machine-readable visual code. The device can generate metadata describe a layout for the element on the document based on the document class configuration. The device can generate the document by arranging the element on the document based on the metadata, wherein the document is generated in a format based on the format configuration.

Abstract: Approaches, techniques, and mechanisms are disclosed for generating subscriptions. According to one embodiment, one or more local features of an input request for service subscription are generated based at least in part on one or more messages originated from a client device that represent the input request. One or more global features of a population of input requests originated from a population of client devices are determined based at least in part on a population of input requests. One or more mapped global features of the input request are generated from the one or more global features via one or more mapping functions. One or more machine learning (ML) based prediction models are applied to the one or more local features and the one or more mapped global features of the input request to compute a fraud score for the input request. The fraud score for the input request is used to determine whether the input request for service subscription is to be accepted.