Bank of America Corporation

Om Purushotham Akarapu

Rama Krishnam Raju Rudraraju

Durgadatta Belhekar

Daniel Darnadi Widjanarko

Bibhudatta Jena

Srihari Rao Gatpa

Rudraraju Ramakrishnamraju

Jitendra Upadhyay

Ragala Manoj Sivaram

Rajashekar Gangaram Basarveni

Kaushik Kumar Akula

Thomas Britton Lowry

Nunna Venkata Sri Satya Mounika

A system for optimizing anomaly detection determines, based on a confidence score, user clustering information that indicates a cluster to which a user belongs, such that if the confidence score is more than a threshold score, the user clustering information indicates that the user belongs to a first cluster. Otherwise, the user clustering information indicates that the user belongs to a second cluster. The system determines, based on user activities in a virtual environment, user outlier information that indicates whether the user is associated with an unexpected activity. The system determines virtual resource routing information that comprises routings of virtual resources between the avatar and the other avatars within the virtual environment. The system updates the confidence score based at least in part upon at least one of the user clustering information, the user outlier information, or the virtual resource routing information.

Optimizing anomaly detection based on user clustering, outlier detection, and historical data transfer paths

Aspects of the disclosure relate to computing hardware and software for dynamic control limit configuration. A computing platform may receive and format historical data. The computing platform may input the formatted data into a control limit prediction model, which may output predicted control limits by weighting, using an ensemble model, outputs from a plurality of other models to produce predicted control limits. The computing platform may adjust existing control limits based on predicted control limits to create actual control limits. The computing platform may receive real time data, and may identify, using the actual control limits and the real time data, a deviation score for the real time data. The computing platform may compare the deviation score to the actual control limits. Based on detecting that the deviation score breaches the actual control limits, the computing platform may send an indication of the breach.

Dynamically providing real-time configuration of data research control limits

A system for implementing anomaly detection accesses user activities associated with an avatar in a virtual environment. The system extracts features from the user activities, where the features provide information about interactions of the avatar with other avatars and entities in the virtual environment. The system determines a deviation range for each feature, where the deviation range indicates a deviation between the features among the avatars over a certain period. The system determines whether the deviation range for a feature is more than a threshold deviation. If it is determined that a deviation range of a feature is more than the threshold deviation, a confidence score associated with the user is updated based on the deviation range of the feature. If the confidence score is more than a threshold score, the user is not associated with an anomaly. Otherwise, the user is determined to be associated with an anomaly.

System and method for anomaly detection for information security

Aspects of the disclosure relate to apparatuses, method steps, and systems for optimized Internet of Things (IoT) data processing for real-time decision support systems. The systems are used for real-time processing prioritization using a prioritization code and/or processing code. Edge devices may generate processing codes that are used in optimizing the data processing. For example, the system receives sensor data and preprocesses the sensor data with a simplified state estimation module to calculate a variance that is used to determine a processing code and/or a prioritization code.

Optimized IoT data processing for real-time decision support systems

Methods, computer readable media, and apparatuses for analyzing data quality are presented. Transaction information may be received from a database, and the transaction information may describe various aspects of a plurality of transactions handled by an organization. Subsequently, a forecast may be calculated based on the transaction information, and the forecast may predict the future value of a metric. An upper control limit and a lower control limit for the metric may be determined based on the transaction information. Thereafter, the latest actual value of the metric may be computed. A normalized quality score for the metric then may be calculated based on the latest actual value of the metric, the forecast, the upper control limit, and the lower control limit. Optionally, a chart may be generated to evaluate data quality for a plurality of metrics, and metrics that exceed a control limit may be added to an issue log.

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Hyderabad Telangana, India

Om Purushotham Akarapu