AI can spot anomalies during auditing much faster than humans can

Fennaio | blog post



AI can spot anomalies during auditing much faster than humans can

Anomaly detection using AI is a powerful and efficient way of spotting anything unusual in any process, data or system - it's easy to do using an Artificial Neural Network such as, it's fast and it's accurate.

The most common way of using AI for anomaly detection is via a process known as Supervised Learning. Here, we show AI such as examples of normal and abnormal for the AI to learn from e.g. you may have a list of 10,000 financial transactions, most of which are legitimate, some are fraudulent. AI will then learn all the intricate links and relationships within and between the provided data so it knows when presented with real-time data whether an anomaly is present or not. The benefit of doing it this way is you can train AI to think how a human does - it will learn what a human thinks is normal or abnormal.

Another way of doing this, depending on your data or system is UnSupervised Learning. In this case all we have is a mass of data and we'd like to learn from it. uses a process called clustering which accurately groups data into distinct types - and in this case we would be looking for normal and abnormal data.

In both cases, the data can be as dynamic and complex as you like - there is no need for programming, code or specific algorithms to deal with specific data or problems.

Crucially, when AI has been trained to spot and understand what is normal or abnormal, it can then detect anomalies extremely quickly - much faster than a human can do - and it can do this on masses of data in real time.


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