International Business Machines Corporation

Lucy Lu

Eliza Salkeld

Weichen Wang

Shanna Hayes

Yi-Hui Ma

An entity resolution system performs a method of resolving one or more candidate entities based on a data set. The entity resolution system has a machine learning module and a narrative module. The machine learning module generates a synthesized data set, the synthesized data set comprising similarity ratings for each entity feature. The narrative module applies a clustering analysis to determine one or more distances between the group of similarity ratings for each entity feature and one or more clusters associated with known relationships between entities, generates a narrative output based on one or more distances. The narrative output states at least one identified relationship between at least two entities of the plurality of candidate entities and a confidence score. The narrative engine also provides the narrative output to a user interface.

System for identifying duplicate parties using entity resolution

An entity resolution system performs a method of resolving one or more candidate entities based on a data set. The entity resolution system has a rules-based module, a machine learning module, a narrative module, and an evaluation module. The rules-based module compares the first entity features to the second entity features and determines whether a rule identifies a relationship between the first entity and the second entity. The machine learning module rates a similarity of the first entity features and the second entity features. The narrative module generates a narrative output based on one or more of the rules-based module and the machine learning module, the narrative output stating an identified relationship between the first entity and the second entity. The evaluation module determines one or more metrics to apply feedback to the system.

A detection modeling system performs a distribution analysis to alert to model degradation. The detection modeling system may have a distribution analysis module configured to perform an alerting process in conjunction with a processing device. The distribution analysis module may receive a risk tolerance rating for alerting to degradation of an analytical model, and determine a threshold value for a model metric based on the risk tolerance rating. The model metric may be a measure of a parameter associated with the analytical model. The distribution analysis module may also monitor the analytical model for degradation using the threshold value for the model metric and model metric values and alert to model degradation of the analytical model based on the monitoring of the analytical model.

Alerting to model degradation based on distribution analysis using risk tolerance ratings

A detection modeling system for alerting to analytical model degradation has a processing device and a memory coupled to the processing device. The detection modeling system is configured to perform a distribution analysis on the selected detection model to determine a first health rating for the selected detection model, perform a survival analysis on the selected detection model to determine a second health rating for the selected detection model, generate an indicative score for the detection model based on the first health rating and the second health rating, and compare the indicative score to a threshold value and alert to model degradation based on the comparison.

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Durham, NC, United States of America

Lucy Lu