How Machine Learning Facilitates Online Fraud Detection?

March 10, 2021

As per a report, online fraud cost the global economy more than three trillion pounds in 2018. Some enterprises even faced losses of more than ten percent. Thus, it can be concluded that fraud detection is a challenging problem affecting businesses across the globe. Many businesses still primarily rely on rule-based systems to detect and combat online fraud. However, these systems are inadequate and not highly effective. Cybercriminals are adopting sophisticated methods for carrying out fraudulent activities, which traditional techniques can’t handle.

Thus, businesses are exploring new horizons to help detect, prevent, and even eliminate fraud instances. One such technological solution that is proving highly promising in combating online fraud is Machine Learning.

What Is Machine Learning Exactly In The Context of Detecting Online Fraud?

Currently, machine learning is being applied across various domains and is proving beneficial across multiple business operations. Similarly, it is proving extremely effective in facilitating online fraud detection. But how, exactly?

Before we dive deep into the subject, let’s have a look at a simple explanation of machine learning in online fraud detection. In a nutshell, machine learning for fraud detection helps identify and even stop fraudulent transactions from legitimate ones, saving money.

Let’s have a look in detail.

The machine learning solution first gathers in-depth data about all the transactions, such as the mode of transaction, time, user id, device used, and the amount transferred. It then thoroughly analyzes all the gathered data bit by bit and extracts the required features. Next, the model is provided with training data sets that teach it to predict the probability of fraud. Finally, based on the data collected and the training data provided, it creates a fraud detection model. The model is then used to detect future transactions and identify fraudulent activities. The model can also be programmed to either accept, block or flag the activity for manual review, depending upon the severity of the fraud detected. All the computations and operations are completed in milliseconds by the machine learning model.

Example of Using Machine Learning Model for Fraud Detection

Machine learning helps with fraud detection in various industries such as banking and finance, healthcare, online gaming, and e-commerce, to name a few. Let’s look at a simple example of using machine learning for online fraud detection.

Suppose a customer uses their credit card for online transactions on a particular e-commerce platform. The customer generally makes purchases ranging from INR 1000- INR 5000 on clothes and apparel. Additionally, the trades are usually made after dusk. But, there are other instances that don't follow the pattern. But, such instances, too, have some common features.

The machine learning model will gather and analyze all such information to create a customer profile. Similarly, the model is trained with a training data set which contains similar transaction information. Based on the historical and training data, it creates a fraud detection model for such types of customers.

Let’s suppose that suddenly, a transaction is initiated for a costly item, say a mobile phone, costing well over a lakh rupee, from an unknown device and location, with the customer’s credential. The machine learning model will compare this transaction information with the fraud detection model and flag the activity as suspicious if it detects a high probability of it being a fraud.

Based on the set rules, the machine learning solution will either block the transaction altogether, notify the bank, or alert the customer through other mediums. Only after the customer has verified the activity, the transaction will be allowed to be completed by the machine learning model.

Now that we have seen how machine learning facilitates online fraud detection, let’s see why machine learning proves to be a powerful tool and is preferred by businesses over other fraud detection solutions.

Why Choose Machine Learning For Online Fraud Detection?

Machine learning solutions help identify fraudulent transactions from legitimate ones with higher accuracy. The accuracy only keeps on increasing with time as more data becomes available to learn for the model. Here’s why machine learning proves to be the perfect solution for online fraud detection:

  • Faster and more effective than human analysts

Machine learning algorithms can process a high volume of information more quickly and effectively than your best data analysts.

  • Can monitor fraud round-the-clock

Machine learning solutions can work 24/7 and analyze enormous amounts of data at the snap of a finger.

  • Better than traditional fraud detection systems

Machine learning solutions can prove better than traditional rule-based fraud detection systems regarding speed, quality, and operation and management costs.

As online frauds get more sophisticated, machine learning solutions seem to be the most viable and effective option to fight online fraud. Businesses, big and small, across domains have already implemented machine learning solutions to fight online fraud. If your business has faced online fraud issues, it is time that you consider implementing a machine learning-based solution that can help solve the problem.

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