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Fraud detection is the process of looking deceptions designed to secure unlawful gain at the expense of someone else, otherwise known as fraudulent activities. Although fraud can be identified as someone who attempts to pass themselves off as someone else, the term fraud is most often associated with deception in order to receive some kind of financial gain. There are many types of fraudulent transactions, such as payment fraud (a broad term that includes illegitimate chargebacks and online phishing), credit card fraud (when credit cards are used by unauthorized individuals to obtain goods or services, or to pull out cash for the criminal), and insurance fraud (such as when insurance claims are initiated when the person knows that they do not have a right to the benefit they are trying to claim). The advent of the internet has allowed for rapid growth of internet fraud, especially in regards to financial fraud. In this day and age it is more important than ever for businesses and organizations to have fraud prevention and detection practices in place.
Modern technology has allowed for advanced techniques for identifying fraud. In fact, there are several methods that utilize artificial intelligence and machine learning to analyze and recognize fraudulent financial transactions. Data analysis techniques such as supervised neural networks have been used to detect fraud through classification, clustering, and forecasting by ingesting large amounts of data and identifying transactions that are outside of the norm. Some types of machine learning models rely on logistic regression to code transactions and interactions as either “fraudulent” or “non-fraudulent.” Such models often use supervised learning, meaning that when the algorithm was initially set up, there was manual identification of fraudulent and non-fraudulent activities. Identified examples are then used to train the machine learning algorithms so they can run autonomously afterwards.
Data science has also had luck in creating detection techniques that allow for real-time monitoring of suspicious activity. These types of analytics techniques often use machine learning models that utilize a decision tree to predict whether or not an interaction or purchase is fraudulent. By answering yes/no questions down a decision tree, machine learning algorithms can help identify fraud as it is occurring. For example, many credit card companies track where a card is being used. As part of the decision tree, the question may ask “Is this location within the general geographic area that the user typically resides,” and if the answer is no, financial institutions can send an alert to the card owner and disable the card. In the case of false positives, card owners can get in touch with the institution and re-enable the card.
Implementing automated fraud detection can help businesses and organizations by: