Invention Grant
- Patent Title: Deep learning approach for assessing credit risk
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Application No.: US16381137Application Date: 2019-04-11
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Publication No.: US11783414B2Publication Date: 2023-10-10
- Inventor: Ryan Roser , Adam Bronstein
- Applicant: Refinitiv US Organization LLC
- Applicant Address: US NY New York
- Assignee: Refinitiv US Organization LLC
- Current Assignee: Refinitiv US Organization LLC
- Current Assignee Address: US NY New York
- Agency: Sheppard Mullin Richter & Hampton LLP
- Main IPC: G06Q40/03
- IPC: G06Q40/03 ; G06N20/00 ; G06N3/08 ; G06N7/01

Abstract:
Systems and methods to facilitate credit risk assessment are described herein. The systems and methods described herein relate to implementing and training a credit risk model comprising a document model and a company model. The document model may be configured to read text of a document, understand long range relationships between words, phrases, and the occurrence of one or more financial events, and create a document score that indicates whether the financial events are likely to occur based on that document. A document-model-state vector may be generated that represents important features and relationships identified within each document and across a set of documents for a given entity based on the document scores. The company model may produce a sequence of default probability scores representing overall likelihoods of the occurrence of the financial events for an entity based on the document-model-state vector for documents associated with that entity.
Public/Granted literature
- US20190318422A1 DEEP LEARNING APPROACH FOR ASSESSING CREDIT RISK Public/Granted day:2019-10-17
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