Abstract: In an illustrative embodiment, methods and systems for automatically labeling unstructured data include accessing unstructured data representing data entry and analyzing the unstructured data by applying natural language processing to a text component of the unstructured data to obtain a set of term counts of words and/or phrases identified in the text component. Analyzing may include applying at least one clustering algorithm to the set of term counts to determine a term cluster, identifying a preexisting term cluster most closely matching the term cluster, and applying, to the unstructured data, a predefined label corresponding to the preexisting term cluster. The unstructured data may be analyzed to obtain formatting counts of formatting elements, and a formatting cluster may be determined and applied to match to a preexisting formatting cluster, thus deriving a predefined label corresponding to the preexisting formatting cluster.

Abstract: In an illustrative embodiment, systems and methods for monitoring insurance claims include identifying, based on predetermined monitoring frequency, insurance claims identified for vulnerability detection processing. Vulnerability detection features may be extracted from data files of the claims, which provide an indication of claim handling deficiencies that can cause claim leakage. A trained vulnerability detection data model can be used to detect claim handling vulnerabilities within the extracted vulnerability detection features where each of the vulnerabilities may include a likelihood of the vulnerability resulting in claim leakage. The vulnerability detection data model may be trained with a data set customized to a respective insurance provider for each claim. Vulnerability scores indicating an overall likelihood of claim leakage can be calculated for the claims based on the detected claim handling vulnerabilities.

Abstract: In an illustrative embodiment, systems and methods for monitoring insurance claims include identifying, based on predetermined monitoring frequency, insurance claims identified for vulnerability detection processing. Vulnerability detection features may be extracted from data files of the claims, which provide an indication of claim handling deficiencies that can cause claim leakage. A trained vulnerability detection data model can be used to detect claim handling vulnerabilities within the extracted vulnerability detection features where each of the vulnerabilities may include a likelihood of the vulnerability resulting in claim leakage. The vulnerability detection data model may be trained with a data set customized to a respective insurance provider for each claim. Vulnerability scores indicating an overall likelihood of claim leakage can be calculated for the claims based on the detected claim handling vulnerabilities.

Abstract: In an illustrative embodiment, methods and systems for automatically labeling unstructured data include accessing unstructured data representing data entry and analyzing the unstructured data by applying natural language processing to a text component of the unstructured data to obtain a set of term counts of words and/or phrases identified in the text component. Analyzing may include applying at least one clustering algorithm to the set of term counts to determine a term cluster, identifying a preexisting term cluster most closely matching the term cluster, and applying, to the unstructured data, a predefined label corresponding to the preexisting term cluster. The unstructured data may be analyzed to obtain formatting counts of formatting elements, and a formatting cluster may be determined and applied to match to a preexisting formatting cluster, thus deriving a predefined label corresponding to the preexisting formatting cluster.

Abstract: In an illustrative embodiment, methods and systems for automatically labeling unstructured data include accessing unstructured data representing data entry and analyzing the unstructured data by applying natural language processing to a text component of the unstructured data to obtain a set of term counts of words and/or phrases identified in the text component. Analyzing may include applying at least one clustering algorithm to the set of term counts to determine a term cluster, identifying a preexisting term cluster most closely matching the term cluster, and applying, to the unstructured data, a predefined label corresponding to the preexisting term cluster. The unstructured data may be analyzed to obtain formatting counts of formatting elements, and a formatting cluster may be determined and applied to match to a preexisting formatting cluster, thus deriving a predefined label corresponding to the preexisting formatting cluster.

Abstract: In an illustrative embodiment, methods and systems for automatically labeling unstructured data include accessing unstructured data representing data entry and analyzing the unstructured data by applying natural language processing to a text component of the unstructured data to obtain a set of term counts of words and/or phrases identified in the text component. Analyzing may include applying at least one clustering algorithm to the set of term counts to determine a term cluster, identifying a preexisting term cluster most closely matching the term cluster, and applying, to the unstructured data, a predefined label corresponding to the preexisting term cluster. The unstructured data may be analyzed to obtain formatting counts of formatting elements, and a formatting cluster may be determined and applied to match to a preexisting formatting cluster, thus deriving a predefined label corresponding to the preexisting formatting cluster.

Abstract: In an illustrative embodiment, systems and methods for monitoring insurance claims include identifying, based on predetermined monitoring frequency, insurance claims identified for vulnerability detection processing. Vulnerability detection features may be extracted from data files of the claims, which provide an indication of claim handling deficiencies that can cause claim leakage. A trained vulnerability detection data model can be used to detect claim handling vulnerabilities within the extracted vulnerability detection features where each of the vulnerabilities may include a likelihood of the vulnerability resulting in claim leakage. The vulnerability detection data model may be trained with a data set customized to a respective insurance provider for each claim. Vulnerability scores indicating an overall likelihood of claim leakage can be calculated for the claims based on the detected claim handling vulnerabilities.

Abstract: In an illustrative embodiment, systems and methods for monitoring insurance claims include identifying, based on predetermined monitoring frequency, insurance claims identified for vulnerability detection processing. Vulnerability detection features may be extracted from data files of the claims, which provide an indication of claim handling deficiencies that can cause claim leakage. A trained vulnerability detection data model can be used to detect claim handling vulnerabilities within the extracted vulnerability detection features where each of the vulnerabilities may include a likelihood of the vulnerability resulting in claim leakage. The vulnerability detection data model may be trained with a data set customized to a respective insurance provider for each claim. Vulnerability scores indicating an overall likelihood of claim leakage can be calculated for the claims based on the detected claim handling vulnerabilities.

Abstract: In an illustrative embodiment, systems and methods for monitoring insurance claims include identifying, based on predetermined monitoring frequency, insurance claims identified for vulnerability detection processing. Vulnerability detection features may be extracted from data files of the claims, which provide an indication of claim handling deficiencies that can cause claim leakage. A trained vulnerability detection data model can be used to detect claim handling vulnerabilities within the extracted vulnerability detection features where each of the vulnerabilities may include a likelihood of the vulnerability resulting in claim leakage. The vulnerability detection data model may be trained with a data set customized to a respective insurance provider for each claim. Vulnerability scores indicating an overall likelihood of claim leakage can be calculated for the claims based on the detected claim handling vulnerabilities.