Hybrid model and system for predicting quality and identifying features and entities of risk controls
AssigneeCapital One Services, LLC
InventorsPeter Tanski, Matthew Peroni
Life of this patent
Estimating the term…
Abstract
Embodiments disclosed are directed to a computing system that performs steps to automatically identify risk control features and entities in a risk control document. The computing system regenerates, by a semantic prediction machine learning (ML) model, phrases in a risk control document. The computing system then classifies, by the semantic prediction ML model, risk control features associated with the regenerated phrases. Subsequently, the computing system corrects, by a discriminative natural language processing (NLP) model, the classified risk control features based on the phrases and the regenerated phrases.
Claims20
A computer-implemented method for automatically identifying risk control features and entities in a risk control document including original phrases, the computer-implemented method comprising:
- generating, by a semantic prediction machine learning (ML) model, a raw risk control document quality score for the risk control document, wherein the risk control document comprises organizational requirements and actions to mitigate an identified risk or risks for an organization;
- regenerating, by a semantic prediction machine learning (ML) model, phrases in the risk control document based on the original phrases, wherein the regenerating phrases comprises suggesting a word, phrase, or entity to complete one or more textual sequences in the risk control document;
- classifying, by the semantic prediction ML model, risk control features associated with the regenerated phrases, wherein the risk control features comprise a first risk control feature indicative of an entity responsible for performing a risk control action identified in the risk control document;
- correcting, by a discriminative natural language processing (NLP) model, the classified risk control features based on the original phrases and the regenerated phrases and domain specific knowledge;
- and
- generating, by the semantic prediction ML model, a revised risk control document quality score for the risk control document based on the corrected and classified risk control features.
A non-transitory computer readable medium including instructions for causing a processor to perform operations for automatically identifying risk control features and entities in a risk control document including original phrases, the operations comprising:
- generating, by a semantic prediction machine learning (ML) model, a raw risk control document quality score for the risk control document, wherein the risk control document comprises organizational requirements and actions to mitigate an identified risk or risks for an organization;
- regenerating, by a semantic prediction machine learning (ML) model, phrases in the risk control document based on the original phrases, wherein the regenerating phrases comprises suggesting a word, phrase, or entity to complete one or more textual sequences in the risk control document;
- classifying, by the semantic prediction ML model, risk control features associated with the regenerated phrases, wherein the risk control features comprise a first risk control feature indicative of an entity responsible for performing a risk control action identified in the risk control document;
- correcting, by a discriminative natural language processing (NLP) model, the classified risk control features based on the original phrases and the regenerated phrases and domain specific knowledge;
- and
- generating, by the semantic prediction ML model, a revised risk control document quality score for the risk control document based on the corrected and classified risk control features.
A computing system for automatically identifying risk control features and entities in a risk control document, the computing system comprising:
- a storage unit configured to store instructions;
- a control unit, coupled to the storage unit, configured to process the stored instructions to perform operations comprising:
- generating, by a semantic prediction machine learning (ML) model, a raw risk control document quality score for the risk control document, wherein the risk control document comprises organizational requirements and actions to mitigate an identified risk or risks for an organization;
- regenerating, by a semantic prediction machine learning (ML) model, phrases in the risk control document based on original phrases, wherein the regenerating phrases comprises suggesting a word, phrase, or entity to complete one or more textual sequences in the risk control document;
- classifying, by the semantic prediction ML model, risk control features associated with the regenerated phrases, wherein the risk control features comprise a first risk control feature indicative of an entity responsible for performing a risk control action identified in the risk control document;
- correcting, by a discriminative natural language processing (NLP) model, the classified risk control features based on the original phrases and the regenerated phrases and domain specific knowledge;
- and
- generating, by the semantic prediction ML model, a revised risk control document quality score for the risk control document based on the corrected and classified risk control features.
Dependencies are inferred from the claim text.