Tuesday, May 21
Artificial Intelligence in eCommerce

Artificial Intelligence in eCommerce

The growth of online shopping in recent years has made eCommerce one of the most successful industries. Due to a rise in internet usage, people have more access to various products and services on sale online- ranging from clothes and shoes to books and food items. However, the unfortunate thing is that this growth has also given rise to fraudulent activities and scams that lead to buyers losing their money or investing in low-quality products. Due to these problems, an advanced form of artificial intelligence (AI) known as deep learning is being applied in the eCommerce industry with some companies using it to detect and prevent fraud.

eCommerce growth has given rise to fraudulent activities and scams

However, the unfortunate thing is that this growth has also given rise to fraudulent activities and scams that lead to buyers losing their money or investing in low-quality products.

This problem is further compounded by the fact that there are many companies that do not conduct extensive background checks on their sellers before selling them access to their platforms. This means that any person can become an eCommerce seller without much vetting taking place, which poses a serious threat to consumers.

Deep Learning applied in the eCommerce industry helps companies detect and prevent fraud

Deep learning is a subfield of machine learning in which artificial neural networks (ANNs) are used to train algorithms to solve specific problems. It has been widely adopted by companies in the eCommerce, retail and transportation industries because it can be applied to analyse large amounts of data and make predictions based on the analysis.

AI works on the principle that machines can be taught or programmed to learn from experience without being explicitly programmed for each specific situation. This differs from traditional programming where instructions are encoded into source code and compiled into an executable program for execution by a computer processor (or central processing unit).

Taking advantage of AI and machine learning as a way of managing their online stores

This has prompted some industries to begin taking advantage of AI and machine learning as a way of managing their online stores. As a tool, AI is used to solve problems. In this case, the problem is managing your online store and keep it safe from fraud. AI can be used to detect fraud as well as prevent it from happening in the first place. Detecting and preventing fraud are different types of operations that require different types of systems.

In addition to detecting and preventing fraud, you may also want to use machine learning (ML) technologies like natural language processing (NLP) or image recognition software for other purposes such as:

    • Preventing scams by identifying fake reviews
    • Detecting scams based on keywords within text or images

AI will be taking over the eCommerce industry

AI has already made shopping and selling more efficient and convenient by providing personalization, recommendations and suggestions that allow customers to quickly find what they need. For example: if you’ve never shopped at a particular store before but are interested in purchasing something that’s currently on sale there now—and have taken steps to indicate this by putting items in your cart or wish list—then an eCommerce site could use AI to show those products first when you visit their site on a mobile device while also looking through your social media activity (e.g., Instagram posts) to see if there are any other categories of interest for which these same items might be relevant.

In conclusion, AI is the future of eCommerce. It can help companies identify fraudulent transactions, prevent buyers from making multiple purchases and even stop people who are trying to steal money away from legitimate sellers. This technology has already been implemented by some companies, who have seen great results with it as they’ve been able to cut losses while also increasing productivity levels among their employees.