Deep learning approach for assessing credit risk

A system configured to use a trained neural network to computationally assess the credit quality of an entity based at least in part on analyzing unstructured text from documents related to the entity to determine relationships between words and phrases that are indicative of one or more future financial events and utilizing a document model and a company model, the system comprising:

• one or more servers comprising electronic storage, one or more physical processors, and computer program components comprising a document model component including the document model configured to analyze unstructured text from documents, and a company model component configured to utilize the company model to produce a sequence of default probability scores representing overall likelihoods of the occurrence of one or more financial events for an entity, the one or more physical computer processors configured by computer readable instructions to:
• electronically obtain a set of documents related to a first entity from one or more document sources over a network, the set of documents comprising at least a first document containing unstructured text and a second document containing unstructured text;
• generate, utilizing the document model, document scores and document-representation vectors for at least the first and second documents of the set of documents based on analysis of the unstructured text, the document scores including a first document score for the first document of the set of documents and a second document score for the second document of the set of documents, the document-representation vectors including a first document-representation vector comprising a word embedding representation of the first document of the set of documents and a second document-representation vector comprising a word embedding representation of the second document of the set of documents, wherein the first and second document scores each indicate a likelihood of an occurrence of one or more future financial events for the first entity based on the first and second document, respectively;
• aggregate the first and second document scores and the first and second document-representation vectors;
• create, using the aggregated first and second document scores and first and second document-representation vectors, a document-model-state vector representing relationships identified within individual documents of the set of documents and across the set of documents;
• produce, utilizing the company model, a sequence of default probability scores representing overall likelihoods of the occurrence of one or more future financial events based on the document-model-state vector for the set of documents related to the first entity.