Abstract: Techniques described herein relate to initiating interventions based on individual records, using assessment data and intervention subtype profiles. Assessment data may be received from individual client devices and/or from a number of internal or external assessment data stores, and used to determine aggregate rating values for an individual record for a plurality of attributes. The individual record may be determined to be associated or not associated with each of the attributes based on the assessment data, and these associations may be compared to intervention subtype profiles to identify one or more matching intervention subtypes. Based on the matching intervention subtypes identified for individual records, customized interventions may be implemented for the individual record, including dynamic assessments, device notifications, and custom report generation.

Abstract: Techniques described herein relate to initiating interventions based on individual records, using assessment data and intervention subtype profiles. Assessment data may be received from individual client devices and/or from a number of internal or external assessment data stores, and used to determine aggregate rating values for an individual record for a plurality of attributes. The individual record may be determined to be associated or not associated with each of the attributes based on the assessment data, and these associations may be compared to intervention subtype profiles to identify one or more matching intervention subtypes. Based on the matching intervention subtypes identified for individual records, customized interventions may be implemented for the individual record, including dynamic assessments, device notifications, and custom report generation.

Abstract: Characteristic normalization (or leveling) is a process that yields consistent and equitable characteristic definitions across multiple sources of credit information. This leveling ensures that when the same data is present for multiple credit sources, for example two or more credit reporting agencies, it is interpreted in the same manner, acknowledging that differences in the data itself may still be present.

Abstract: Characteristic normalization (or leveling) is a process that yields consistent and equitable characteristic definitions across multiple sources of credit information. This leveling ensures that when the same data is present for multiple credit sources, for example two or more credit reporting agencies, it is interpreted in the same manner, acknowledging that differences in the data itself may still be present.

Abstract: Characteristic normalization (or leveling) is a process that yields consistent and equitable characteristic definitions across multiple sources of credit information. This leveling ensures that when the same data is present for multiple credit sources, for example two or more credit reporting agencies, it is interpreted in the same manner, acknowledging that differences in the data itself may still be present.

Abstract: Characteristic normalization (or leveling) is a process that yields consistent and equitable characteristic definitions across multiple sources of credit information. This leveling ensures that when the same data is present for multiple credit sources, for example two or more credit reporting agencies, it is interpreted in the same manner, acknowledging that differences in the data itself may still be present.