Abstract: Embodiments of the disclosure are directed towards pipe leak prediction systems configured to predict whether a pipe (e.g., a utility pipe carrying some substance such as waster) is likely to leak. The pipe leak prediction system may include one or more predictive models based on one or more machine learning techniques, and a predictive model can be trained using data for the characteristics of various pipes in order to determine the patterns associated with pipes without leaks and the patterns associated with pipes with leaks. A predictive model can be validated, used to construct a confusion matrix, and used to generate insights and inferences associated with the determinant variables used to make the predictions. The predictive model can be applied to data for various pipes in order to predict which of those pipes will leak. Any pipes that are identified as likely to leak can be assigned for further investigation for potential repair or preventative maintenance.

Abstract: Embodiments of the disclosure are directed towards pipe leak prediction systems configured to predict whether a pipe (e.g., a utility pipe carrying some substance such as waster) is likely to leak. The pipe leak prediction system may include one or more predictive models based on one or more machine learning techniques, and a predictive model can be trained using data for the characteristics of various pipes in order to determine the patterns associated with pipes without leaks and the patterns associated with pipes with leaks. A predictive model can be validated, used to construct a confusion matrix, and used to generate insights and inferences associated with the determinant variables used to make the predictions. The predictive model can be applied to data for various pipes in order to predict which of those pipes will leak. Any pipes that are identified as likely to leak can be assigned for further investigation for potential repair or preventative maintenance.

Abstract: Embodiments of the disclosure are directed towards electricity fraud detection systems that involve a behavioral detection ecosystem to improve the detection rate of electricity fraud while reducing the rate of false-positives. More specifically, machine learning algorithms are eschewed in favor of two separate models that are applied sequentially. The first model is directed to improving the detection rate of electricity fraud through the use of detectors to identify customers engaging in suspicious behavior based on the demand profiles of those customers. The second model is directed to reducing the rate of false-positives by identifying potential legitimate explanations for any suspicious behavior. Subtracting away the suspicious behavior with legitimate explanations leaves only the identified, unexplained suspicious behavior that is highly likely to be associated with fraudulent activity.

Abstract: Embodiments of the disclosure are directed towards electricity fraud detection systems that involve a behavioral detection ecosystem to improve the detection rate of electricity fraud while reducing the rate of false-positives. More specifically, machine learning algorithms are eschewed in favor of two separate models that are applied sequentially. The first model is directed to improving the detection rate of electricity fraud through the use of detectors to identify customers engaging in suspicious behavior based on the demand profiles of those customers. The second model is directed to reducing the rate of false-positives by identifying potential legitimate explanations for any suspicious behavior. Subtracting away the suspicious behavior with legitimate explanations leaves only the identified, unexplained suspicious behavior that is highly likely to be associated with fraudulent activity.

Abstract: Embodiments of the disclosure are directed towards electricity fraud detection systems that involve a behavioral detection ecosystem to improve the detection rate of electricity fraud while reducing the rate of false-positives. More specifically, machine learning algorithms are eschewed in favor of two separate models that are applied sequentially. The first model is directed to improving the detection rate of electricity fraud through the use of detectors to identify customers engaging in suspicious behavior based on the demand profiles of those customers. The second model is directed to reducing the rate of false-positives by identifying potential legitimate explanations for any suspicious behavior. Subtracting away the suspicious behavior with legitimate explanations leaves only the identified, unexplained suspicious behavior that is highly likely to be associated with fraudulent activity.

Abstract: Embodiments of the disclosure are directed towards electricity fraud detection systems that involve a behavioral detection ecosystem to improve the detection rate of electricity fraud while reducing the rate of false-positives. More specifically, machine learning algorithms are eschewed in favor of two separate models that are applied sequentially. The first model is directed to improving the detection rate of electricity fraud through the use of detectors to identify customers engaging in suspicious behavior based on the demand profiles of those customers. The second model is directed to reducing the rate of false-positives by identifying potential legitimate explanations for any suspicious behavior. Subtracting away the suspicious behavior with legitimate explanations leaves only the identified, unexplained suspicious behavior that is highly likely to be associated with fraudulent activity.

Abstract: Embodiments of the disclosure are directed towards pipe leak prediction systems configured to predict whether a pipe (e.g., a utility pipe carrying some substance such as waster) is likely to leak. The pipe leak prediction system may include one or more predictive models based on one or more machine learning techniques, and a predictive model can be trained using data for the characteristics of various pipes in order to determine the patterns associated with pipes without leaks and the patterns associated with pipes with leaks. A predictive model can be validated, used to construct a confusion matrix, and used to generate insights and inferences associated with the determinant variables used to make the predictions. The predictive model can be applied to data for various pipes in order to predict which of those pipes will leak. Any pipes that are identified as likely to leak can be assigned for further investigation for potential repair or preventative maintenance.