Carbon Capture, Utilization, and Storage (CCUS):…
Fraud detection bot
The extraction of common behaviors from these everyday life datasets can help companies improve their data quality, detect outliers and in the end increase their financial performance.
Sectors and actors looking for common and abnormal behaviors
Companies in energy, utilities, banking, insurance, and transport are gathering huge amounts of time series data, such as client consumption or payment transactions. The extraction of common behaviors from these everyday life datasets can help companies improve their data quality, detect outliers and in the end increase their financial performance. To face all these challenges, our Datascience team has developed a bot to simulate fraud / demand-response modeldesign detection from an electrical consumption load curve.
How does it work – remove problematic points with a machine learning approach
Sia Partners’ Datascience team has used all its forecast algorithm skills to train a robust model with the available data, containing both regular behavior and fraud data. With an iterative multi-model approach, atypical points are removed from the training set of the forecast algorithm until the convergence of the model to the common behavior is achieved.
If you wish to learn more about our Fraud Detection bot, use the link below to access a demo :
Our Fraud Detection bot is part of Heka, the ecosystem of AI solutions developed by Sia Partners.
To learn more about Heka and its ecosystem