Abstract: In some implementations, a device may obtain an electronic document. The device may extract a portion of the electronic document. The device may obtain first text data associated with the portion. The device may determine whether the first text data corresponds to any of a plurality of text indicators of document types. The device may obtain, based on a determination that the first text data does not correspond to any of the plurality of text indicators, second text data associated with a greater portion of the electronic document that includes more than the first text data. The device may determine, using a machine learning model, a type of the electronic document based on the second text data. The device may determine whether the type of the electronic document differs from an expected document. The device may transmit a notification indicating that the electronic document is not the expected document.

Abstract: In some implementations, a device may obtain an electronic document. The device may extract a portion of the electronic document. The device may obtain first text data associated with the portion. The device may determine whether the first text data corresponds to any of a plurality of text indicators of document types. The device may obtain, based on a determination that the first text data does not correspond to any of the plurality of text indicators, second text data associated with a greater portion of the electronic document that includes more than the first text data. The device may determine, using a machine learning model, a type of the electronic document based on the second text data. The device may determine whether the type of the electronic document differs from an expected document. The device may transmit a notification indicating that the electronic document is not the expected document.

Abstract: Systems, methods, and apparatuses are described for providing a holistic data management platform. A computing device may receive different sets of data from different third-party data providers. The computing device may then process that data and store it in a distributed database. That data may be encrypted and/or tokenized. A relationship graph may be generated by processing entities indicated in the stored data. For instance, a relationship type may be identified based on properties of entities indicated by different sets of data stored in the distributed database. That relationship graph may be stored and updated based on new data. A failover of a primary cluster of the distributed database my cause performance of an automatic detach-and-promote process, whereby the relationship graph may be provided by a promoted secondary cluster.

Abstract: In some implementations, a model organizer may receive, from a data source, the original dataset. The model organizer may receive, from an administrator device, an indication of a first factor to remain fixed and an indication of at least one second factor to refrain from anonymizing. The model organizer may provide the original dataset to a synthetic generation model in order to receive the synthetic dataset. The synthetic generation model may refrain from varying the first factor and may anonymize at least one third factor. The model organizer may receive, from the administrator device, an indication of an underwriting model. The model organizer may provide the original dataset and the synthetic dataset to the underwriting model for training, testing, or refinement. The model organizer may transmit, to the administrator device, a notification that the underwriting model has been trained, tested, or refined.

Abstract: In some implementations, a device may obtain an electronic document. The device may extract a portion of the electronic document. The device may obtain first text data associated with the portion. The device may determine whether the first text data corresponds to any of a plurality of text indicators of document types. The device may obtain, based on a determination that the first text data does not correspond to any of the plurality of text indicators, second text data associated with a greater portion of the electronic document that includes more than the first text data. The device may determine, using a machine learning model, a type of the electronic document based on the second text data. The device may determine whether the type of the electronic document differs from an expected document. The device may transmit a notification indicating that the electronic document is not the expected document.