Abstract: Systems and methods are provided for enhanced machine learning refinement and alert generation. An example method includes accessing datasets storing customer information reflecting transactions of customers. Individual risk scores are generated for the customers based on the customer information. Generating the risk score includes providing identified occurrences of scenario definitions and customer information as input to one or more machine learning models, the scenario definitions identifying occurrences of specific information reflected in the datasets, with the machine learning models assign respective risk scores to the customers. An interactive user interface is presented.

Abstract: Systems and techniques for sharing security data are described herein. Security rules and/or attack data may be automatically shared, investigated, enabled, and/or used by entities. A security rule may be enabled on different entities comprising different computing systems to combat similar security threats and/or attacks. Security rules and/or attack data may be modified to redact sensitive information and/or configured through access controls for sharing.

Abstract: Methods, devices, systems and computer program products enable monitoring and responding to cyber security attacks. One such system relates to a consortium of monitoring companies and an infrastructure including one or more central monitoring stations or local handling stations for a monitoring company are provided. A central monitoring station of a monitoring company detects a cyberattack that has been launched against a client computer system, and requests a local station to respond to the cyberattack via onsite visits or requests additional resources from other monitoring companies through the consortium system. The central monitoring station also sends to the consortium system updates on a cyberattack that is detected or mitigated by a central monitoring station or local handling station of the monitoring company. The monitoring consortium enables stronger capabilities than any individual monitoring company can offer by the combination and coordination of the efforts and resources of the members.

Abstract: Systems and methods are provided for enhanced machine learning refinement and alert generation. An example method includes accessing datasets storing customer information reflecting transactions of customers. Individual risk scores are generated for the customers based on the customer information. Generating the risk score includes providing identified occurrences of scenario definitions and customer information as input to one or more machine learning models, the scenario definitions identifying occurrences of specific information reflected in the datasets, with the machine learning models assign respective risk scores to the customers. An interactive user interface is presented.

Abstract: Systems and techniques for sharing security data are described herein. Security rules and/or attack data may be automatically shared, investigated, enabled, and/or used by entities. A security rule may be enabled on different entities comprising different computing systems to combat similar security threats and/or attacks. Security rules and/or attack data may be modified to redact sensitive information and/or configured through access controls for sharing.

Abstract: Systems and techniques for sharing security data are described herein. Security rules and/or attack data may be automatically shared, investigated, enabled, and/or used by entities. A security rule may be enabled on different entities comprising different computing systems to combat similar security threats and/or attacks. Security rules and/or attack data may be modified to redact sensitive information and/or configured through access controls for sharing.

Abstract: Systems and techniques for sharing security data are described herein. Security rules and/or attack data may be automatically shared, investigated, enabled, and/or used by entities. A security rule may be enabled on different entities comprising different computing systems to combat similar security threats and/or attacks. Security rules and/or attack data may be modified to redact sensitive information and/or configured through access controls for sharing.

Abstract: Systems and techniques for sharing security data are described herein. Security rules and/or attack data may be automatically shared, investigated, enabled, and/or used by entities. A security rule may be enabled on different entities comprising different computing systems to combat similar security threats and/or attacks. Security rules and/or attack data may be modified to redact sensitive information and/or configured through access controls for sharing.

Abstract: Systems and techniques for sharing healthcare fraud data are described herein. Healthcare fraud detection schemes and/or fraud data may be automatically shared, investigated, enabled, and/or used by entities. A healthcare fraud detection scheme may be enabled on different entities comprising different computing systems to combat similar healthcare fraud threats, instances, and/or attacks. Healthcare fraud detection schemes and/or fraud data may be modified to redact sensitive information and/or configured through access controls for sharing.

Abstract: Systems and techniques for sharing security data are described herein. Security rules and/or attack data may be automatically shared, investigated, enabled, and/or used by entities. A security rule may be enabled on different entities comprising different computing systems to combat similar security threats and/or attacks. Security rules and/or attack data may be modified to redact sensitive information and/or configured through access controls for sharing.

Abstract: Systems and techniques for sharing healthcare fraud data are described herein. Healthcare fraud detection schemes and/or fraud data may be automatically shared, investigated, enabled, and/or used by entities. A healthcare fraud detection scheme may be enabled on different entities comprising different computing systems to combat similar healthcare fraud threats, instances, and/or attacks. Healthcare fraud detection schemes and/or fraud data may be modified to redact sensitive information and/or configured through access controls for sharing.

Abstract: Systems and techniques for sharing security data are described herein. Security rules and/or attack data may be automatically shared, investigated, enabled, and/or used by entities. A security rule may be enabled on different entities comprising different computing systems to combat similar security threats and/or attacks. Security rules and/or attack data may be modified to redact sensitive information and/or configured through access controls for sharing.