How E-Commerce Can Benefit using Machine Learning for Fraud Detection?

Machine Learning

Machine Learning

Fraudulent activities in e-commerce are very high as the sector relies on online transactions.  

The ability of Machine Learning is already proved to be cogent across almost every industry. The technology is able to process large datasets much better than humans, identifying and detecting thousands of patterns on a user’s buying behavior. In the e-commerce industry, as businesses deal with voluminous datasets, using machine learning algorithms and models pose a significant role. It can assess vast numbers of transactions in real-time and is continuously evaluating and processing new datasets. Since the industry entirely relies on internet connectivity and banking for online purchasing, it is vulnerable to fraud or deception.

In its 2018 Global Fraud and Identity Report, Experian found that almost 72 percent of businesses reported fraud as a growing concern over the past 12 months and nearly two-thirds, 63 percent, reported the same or higher levels of fraudulent losses. The report further showed that 75 percent of businesses seek more advanced security measures and authentication processes that have little or no impact on the customer.

Apart from this, e-commerce sales are anticipated to reach over US$630 billion by 2020. But the growing sales would not always mean for bigger revenue, but also present an increasing set of challenges and bigger losses due to fraud. Thus, if companies do not implement appropriate fraud prevention measures for their e-commerce business they can face major losses. Reports show that e-commerce fraud losses in the United States can recapitulate over US$12 billion by the next year.

What Machine Learning Means for Fraud Detection and Prevention?

The machine learning model improves with large datasets as it can distinguish and simplify multiple behaviors. When it comes to detecting and preventing e-commerce fraud, the machine learning model assists in this process by collecting and segmenting data, then fed with training sets to envisage the possibility of fraud. The data collected is generally split into three different sections, training, testing, and cross-validation. 

As the machine learning algorithm is trained on a partial set of data and parameters tweaked on a testing set, the performance of the data is measured using a cross-validation set. The high performing models will be then tested for an assortment of random splits of data to make sure reliability in outcomes. 

Machine learning algorithms learn things from experience and iteratively enhance their performance. Significantly, they adapt accordingly to the conditions evolve with a data set, and keep up with situations that humans align them, especially in the case of e-commerce fraud. The application of machine learning is majorly used to deliver fraud detection solution. As companies want to foresee values of some output, mostly in the payment process to envision the payment is fraudulent and false otherwise, and the value given some inputs like the country or countries wherein the card was issued in the past day. 

Moreover, machine learning models are trained by using the data that involves records with both the output values for a wide array of input values, and these records are often collected from historical data.

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