Question

When using AI to find patterns in financial data, what is the specific mathematical process that allows you to identify outliers that differ from the norm?

Accepted Answer

To find outliers, which are pieces of data that look strange because they don&#x27;t fit the usual pattern, AI often uses a process called distance calculation. The most common way to do this is by measuring the distance between a data point and the average value, which is known as the mean. The math used here relies on something called standard deviation, which is just a measure of how spread out the data is. If a number is too many standard deviations away from the average, the AI flags it as an outlier. Think of it like a teacher looking at test scores; if almost everyone gets a B, but one person gets a score that is miles away from the group, that score is clearly an outlier. Another popular method is called isolation forests, which work by chopping up the data into smaller groups. Since outliers are different from everything else, they usually end up alone in a small group much faster than normal data points. By counting how many steps it takes to isolate a point, the AI can decide if that point is a normal part of the set or an outlier.

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When using AI to find patterns in financial data, what is the specific mathematical process that allows you to identify outliers that differ from the norm?

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