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DEEP LEARNING APPROACH FOR ASSESSING CREDIT RISK

机译:评估信用风险的深度学习方法

摘要

Systems and methods to facilitate credit risk assessment are described here-in. 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.
机译:此处介绍了有助于信用风险评估的系统和方法。本文描述的系统和方法涉及实现和训练包括文档模型和公司模型的信用风险模型。可以将文档模型配置为阅读文档的文本,了解单词,短语与一个或多个财务事件的发生之间的长期关系,并基于该得分创建指示财务事件是否可能发生的文档评分文件。可以生成文档模型状态向量,该文档模型状态向量表示基于文档得分针对给定实体在每个文档内以及在一组文档中标识的重要特征和关系。公司模型可以基于与该实体相关联的文档的文档模型状态向量,生成表示该实体发生财务事件的整体可能性的默认概率分数序列。

著录项

  • 公开/公告号WO2019198026A1

    专利类型

  • 公开/公告日2019-10-17

    原文格式PDF

  • 申请/专利权人 FINANCIAL & RISK ORGANISATION LIMITED;

    申请/专利号WO2019IB52995

  • 发明设计人 ROSER RYAN;BRONSTEIN ADAM;

    申请日2019-04-11

  • 分类号G06Q40;

  • 国家 WO

  • 入库时间 2022-08-21 11:52:47

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