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Abstract: Whether in finance, insurance, or cybersecurity the ability to detect rare items or events that lie outside the norm is critical. When we accumulate millions of data points over a time period we can build models to predict the next set of values likely to occur, the likelihood a predicted value is an anomaly, and ensures the alerts produced are understood by those that need to act.
Detecting and grouping anomalous transactions for review in fund accounting is essential, requiring completion in very tight windows (minutes) for price booking and regulatory compliance. In this webinar, we will demonstrate how to integrate SingleStore with MindsDB to perform various anomaly detection methods.
We will be using the credit card fraud detection dataset from Kaggle. This dataset contains information so credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. We will show how to extract some interesting insights from this rich dataset, build an anomaly detection model, as well as explore the use of various forecasting and machine learning tools.
Abstract: Whether in finance, insurance, or cybersecurity the ability to detect rare items or events that lie outside the norm is critical. When we accumulate millions of data points over a time period we can build models to predict the next set of values likely to occur, the likelihood a predicted value is an anomaly, and ensures the alerts produced are understood by those that need to act.
Detecting and grouping anomalous transactions for review in fund accounting is essential, requiring completion in very tight windows (minutes) for price booking and regulatory compliance. In this webinar, we will demonstrate how to integrate SingleStore with MindsDB to perform various anomaly detection methods.
We will be using the credit card fraud detection dataset from Kaggle. This dataset contains information so credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. We will show how to extract some interesting insights from this rich dataset, build an anomaly detection model, as well as explore the use of various forecasting and machine learning tools.
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