What is Fraud Detection?
Fraud Detection is a process that helps businesses identify and also to prevent fraudulent financial activities. Thus, the process includes detection of unauthorized credit card transactions, insurance scams, cyber hacking, identity thefts and a lot more.
How does it Work?
Most of the detection tools like Machine learning work by identifying patterns and sets. Because most of the frauds and bot crawlers work in patterns. There are three basic steps of a predictive analysis model using Machine learning. These are:
- Collection of Data
The first step is to collect the data from every source possible. The data from external sources is combined with the internal data in order to identify fraud.
- Classification of Data
The fraud detection techniques then classifies the collected data to make automatic decisions. And in case of complex data, the final decision making is left upon the human analyst.
- Final Feedback
Different models provide different sets of analysis. Based on these, the analysts can make their decisions. Further, help of these decisions, analysts provide feedback in order to improve the predictive data set model.
There are many industries which have witnessed various kinds of frauds including the banking, e-commerce and telecommunication sector. Considering the number of transactions that these sectors handle, they are highly prone to the risk of fraud. These include credit card fraud detection, ATM frauds and more.
Depending on the type of data that is available, there are a lot of different models which can be used for detecting fraud. The experts in the field of data science and cybersecurity have invested a lot of time figuring out which model might work for a particular type of fraud and which might not.
Watch the video to learn more about fraud detection, the kind of data used in the detection and how to select the models based on this data and scenarios from the industry professionals.
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