Data Science Techniques for Fraud Detection

Vaibhav Srivastava
17 June 2021

Fraud detection is a challenging problem. The fact is that fraudulent transactions are rare; they represent a very small fraction of activity within an organization. The challenge is that a small percentage of activity can quickly turn into big dollar losses without the right tools and systems in place. Criminals are crafty. As traditional fraud schemes fail to pay off, fraudsters have learned to change their tactics. The good news is that with advances in fraud analytics, systems can learn, adapt and uncover emerging patterns for preventing fraud with the help of data science techniques for fraud detection.

Also Read: How Data Science Is Transforming the Logistics Industry?

Just take a glance at any business and you will see the deep root problem that they all face –  Financial FRAUDS. Be it banking, insurance or professional offices, all these industries are the hardest hit due to fraudulent transactions. Though the percentage of such activities is on the lower side but the biggest problem is, even this small percentage can turn into a significant dollar loss. However, the fraudsters these days have become smarter and they have all the keys to unlock the traditional safety techniques. But, with the advancement in fraud analytics, systems are becoming capable to learn and adapt with the new methods to prevent frauds. So, read this post about the different data science techniques for fraud detection.

Repetitive Analysis 

Also termed as competitive analysis, repetitive analysis is one of the widely used data science techniques for fraud detection. The process involves writing scripts which are examined thoroughly through a large volume of data to spot the fraudulent activities done over a period of time. Though the script is operated continuously, but you can be set it to give periodic notifications about the occurrence of any fraud, making the entire process more efficient and consistent.


In this data science techniques for fraud detection, analysts use the hypothesis to check transactions done and try to figure out if there is a scope of any fraudulent activities. On the basis of the results it gives, the incidents are investigated further.


Sampling is one of the popular data science techniques for fraud detection. It is very useful while building and validating machine learning models especially for those applications where the chances of occurrence of target event is rare like in fraud detection. It is a technique of great help when it comes to fraud detection, but at the same time, it has a disadvantage. As it analyses a small part of the data, the chances are it may miss to detect some of them. 

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Analytical Technique

There are many data science techniques for fraud detection that are used by data scientist and one of them is the analytical technique. It mainly helps in finding out the frauds that do not look normal. The prime focus in this method is to look out and identify the oddity in the pattern. It includes detecting those values that are more than the standard deviation averages. Other than this, data scientists look at the low and high values vigilantly and try to figure out even the smallest anomalies there. Anomalies in the data are often a sign of fraud.

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