Whether organization is aware or not, fraud is a major problem for smooth advancing of the company

Our blog has been suggesting the newest ideas and the innovations in the information technology since the beginning. Hence, our inclination towards almost any subjects is automation, self-learning, artificial intelligence and prediction. As the customer size gets bigger, the problem related with them gets even bigger. However, solution mechanism offers, in generals, specific solutions to specific problems. Fraud can cause a lot of problem whether company recognizes it or not. Thus, an early detection of the fraud and proper solution to it helps a lot as there is no way to completely exclude neither chance of fraud nor the information asymmetry in its root.

Many elements contribute to fraud problem. New criminal tactics, electronic devices, data breeches and so are the main collaborators of the ever-expanding fraud behavior in the market. In fact, the rapid online transformation is inviting more exploiter as user size gets huge. as fraudulent behavior sees no rule, while also can disguise among the “normal” users, the solution of the problem can only be found with great efforts. Nevertheless, efforts can be left with no luck sometimes as application area is immense and the target individuals are expanding.

The best way to start a fraud detection strategy is to comprehend all fraud mechanism as well as all generic types of it. There are two types of fraudulent behavior in the market: friendly and criminal. Although types are straightforward to see, the direct targets are not that easy to spot on. Due to non-static nature of the market, fraudsters are also change their methods, system and rules. The dynamic nature of the data makes things worse for the organization to prevent fraudulent behavior. However, the most advanced solutions are, again, coming from the merits of 21 st century. Artificial intelligence and machine learning come to help the problems arose from the complexity of problem-makers.

The AI and ML used in the fraud detection (or fraud prevention) systems are the backbones of a healthy organizational strategy. As the data and target get complex, the ML backed algorithms use past data to learn from them; thus, identify patterns of fraudulent behavior. In fact, identifying the fraud is a huge initial step where a clustering can be made to predict the prospect movement according to former moves. The most advanced fraud detection strategies use a complex ML system where insights are getting from both supervised and unsupervised channels.

Unsupervised channels use complex data channels to collect insight for the target; thus, determining the validity of the move. By millions of data used, unsupervised ML updates its database and decide the risk of the behavior. This is done by analysis of patterns that represent fraudulent behavior. Supervised channels analyses the updates while also being updated by the organization’s objectives too. The risk or safety of the movement is first identified (or told) by an organization responded; thus, machine learns the difference between a risky action and a safe action.

Therefore, the benefits of early fraud detection can only be achieved if simultaneously backed by artificial intelligence and machine learning algorithms. In fact, organization that are insisting on the manual reviews of the action to detect fraud can only spot few of them when compared to an advanced machine learning algorithm. Working-time saved by the automated process as well as the prevented financial loss are the best straight-out-of-box benefits of the early fraud detection. Using machine learning for the sake of powering the decision-making process is not only stopping unwanted actions such as fraud but it also enhances the organization’s ability to catch up with the expanding competition.

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