Data Analytics has emerged as an indispensable ... the pivotal role of data modelling and anomaly detection in managing the complexity of modern audit tasks [2]. In parallel, research evaluating ...

The research outlines the transformative impact of AI in public administration, calling it a fourth industrial revolution ...

"Fraudulent transactions may be hard to prevent or detect without analytic algorithms assessing high volumes of data with targeted anomaly and risk detection." Many of the chief audit executives who ...

Editor’s note: This is the third article in a four-part series that is part of a larger initiative the AICPA Auditing Standards ... ledger that it ran through a data analytics application that scored ...

FRAMINGHAM, Mass.--(BUSINESS WIRE)--Prelert, the leading provider of behavioral analytics for IT security and operations teams, today announced V4 of its Anomaly Detective application. Building on ...

The solution adopts a multi-faceted approach, combining rules-based, statistical, and machine learning algorithms to meet rigorous auditing standards and boost anomaly detection ... depend on such ...

but also pinpoints new hidden anomalies across multiple data dimensions, reducing manual analysis that previously took weeks to minutes. It brings business-level clarity to anomaly detection, not ...

and Principal Component Analysis (PCAs). Supervised anomaly detection uses data set with a set of "normal" and "abnormal" labels and a trained classification algorithm. ML builds a predictive ...

Anomaly detection is an analytic process for identifying points of data or events that deviate significantly from established patterns of behavior. In cybersecurity, anomaly detection is one of ...