With the rapid growth of the internet and the IoT and the resultant digital transformation of the world we live in, there is an explosion of data that is being generated, collected, and stored.  There is data available for “normal” transactions, as well as for the fraudulent of interest to a company. If one could successfully analyze this data and gain meaningful insights and draw conclusions from it, it would be possible to use that insight for reducing the threats and risks against organizations.

However, due to the sheer volume of data that would need to be processed, it is practically impossible to accomplish that objective using traditional data analysis and Business Intelligence (BI) tools. That is where modern Artificial Intelligence (AI) and Machine Learning (ML) tools may be able to help mitigate risk.

Role of Anomaly Detection

One of the proactive approaches to mitigate and eliminate risk is to detect the patterns of anomaly that are often seen in conjunction with risky transactions. These anomalies or outliers are rare items that are most of the time related to an issue or underlying problem. In some cases, these can indicate fraud, process risks, and malfunctioning machinery parts. The data sets give an understanding of what is not normal, and these can be specifically interesting for the business to gain perspective.

Why Machine Learning for Anomaly Detection?

ML is the ideal technology for Anomaly Detection as it leverages the full processing power of modern computers, coupled with the appropriate AI-driven algorithms to rapidly churn through massive amounts of data (aka Big Data!) to identify anomalous patterns that can be used to isolate specific transactions to be analyzed further.  These techniques are initially used to process data where the ultimate status (fraudulent or not) is known. The ML algorithm’s predictions are compared with the known (“control”) data set to provide critical feedback to the ML engine to help improve its future predictions.  This feedback loop is a critical component of refining ML algorithms.  Sometimes that is also known as the “feedback” or “learning” loop.

It is worth noting here that the detection of an “anomaly” doesn’t necessarily mean that the transaction in question is fraudulent, it just indicates that the standard deviation of that particular data is beyond what is normal. The investigators can decide if the instance is due to some acceptable circumstances or are fraudulent.  This decision provides the basis for the ML feedback loop described in the previous paragraph.

The Benefits of Machine Learning in Anomaly Detection

Automated – The data can be fed directly to the ML environment and the algorithm can analyze the data sets to identify the abnormal patterns.  This elimination of manual and labor-intensive steps helps to improve the process flow and increase reliability.

Fast – The system processes vast volumes of data far faster than any human can. The resulting turnaround is quicker and gives businesses the benefit of fast actionable insights.

Rigorous – Machine driven processes are rigorous by nature, and completely eliminate any lack of rigor due to human foibles. A computer does not fall sick, nor does it have any mood swings!

Accurate – ML reduces false positives, which is a huge relief as it reduces unpleasant encounters while case management of such instances.

Self-Learning – The system gets better on an ongoing basis, thanks to the feedback loop that reinforces correct behavior, and can quickly stop unwanted behavior (wrong predictions). It learns from the patterns and grows smarter in predicting anomalies.