Types Of E-Commerce Fraud That Artificial Intelligence Can Detect

Types Of E-Commerce Fraud That Artificial Intelligence Can Detect


Economic transactions carried out through the Internet have registered growth in the last year due to the pandemic. However, these were already experiencing an increase of approximately 23% per year before the health crisis, which shows how e-commerce has established itself as one of the essential sales channels for businesses.

Without forgetting that the online channel is key to improving the benefits of companies, it also carries risks. Fraud through the Internet has increased exponentially in recent years,” forcing companies to improve their detection systems and users to be more alert to social engineering attacks such as Phishing or Pharming”, they explain from , a Spanish platform for time series data analysis in the field of Big Data.

Given this, the company points out that Artificial Intelligence (AI) has become the best ally of e-commerce to detect fraud since it makes it easier for businesses to identify possible scams thanks to alterations in patterns such as those collected below  :

Usual scams

Artificial Intelligence learns from past fraudulent transactions that appear in each company’s database. Thus, thanks to this historical database, electronic businesses can build a system that learns and identifies already known patterns to detect whether a transaction is fraudulent or not. These metrics will also predict the probability that a new transaction is fraudulent.

New scams

When the deception is unique and there are no previous patterns that warn of fraud, Artificial Intelligence focuses, in this case, on detecting anomalies—for example, identifying unusual transactions in real-time that will alert us that we may be facing suspicious activity. Transactions identified as abnormal by the AI ​​may be validated by a human operator or directly blocked, depending on the degree of confidence of the algorithms and/or the amount of the transactions. In this way, it seeks to offer greater security by minimizing the blocking of legitimate commerce.

Combination of habitual and new frauds

In this case, Artificial Intelligence considers both the historical database of scams and the further alterations that are detected. Thus, the AI ​​will learn as behavioral patterns are modified, improving the detection of possible fraud. It will gradually incorporate the practices that allow new scams to be identified in its models.

Detection of fraudulent chargebacks

When the customer disputes a transaction made with a card and requests a refund from his bank because he does not recognize the charge or has returned the product, the chargeback occurs. However, this action may be a fraud since, on occasion, the client makes these chargebacks despite having received the product, generating evident damage to the business. Given this, Artificial Intelligence can use the customer’s previous experience to alert e-commerce that they face a potential risk activity.

Card fraud

In this case, we are talking about credit card cloning or information theft. Usually, purchases made with cloned cards come from countries where the cardholder does not reside, so Artificial Intelligence can detect this pattern to warn of possible fraud and information theft.

Finally,  they collect that thanks to Artificial Intelligence, companies increase not only the processing capacity of large volumes of data but also the performance of machine learning progressively improves, which allows them to make predictions that detect possible fraud more quickly, easily and efficiently thanks to the history of the existing time series of data.

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