AI and Fraud Detection: Securing eCommerce Transactions
Have you heard of the hacking incident involving Target, the well-known American retailer? In 2013 Target experienced a large-scale security breach which compromised several of its POS systems with malware, giving cybercriminals access to millions of customers’ personal and financial data. The incident became one of the most notable data breaches of the decade, affecting customers nationwide and unfortunately, it was not the only one. According to Juniper Research, 2023 reached an alarming $48 billion in losses due to fraud. The issue has grown year by year and this shows the escalating risks in online transactions. However, the new AI developments are helping fraud detection tremendously, becoming essential for securing eCommerce transactions.
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eCommerce fraud – from financial harm to reputational damage
From phishing to accounts takeover or payment frauds, they pose significant risks for online shops. One of the types everyone surely heard about is phishing, when fraudsters deceive customers or employees into divulging sensitive information like login credentials or credit card details by posing as legitimate entities.
Account takeovers (ATO) are also pretty common and more dangerous because cybercriminals gain unauthorized access to user accounts by exploiting weak passwords, security breaches, or phishing attacks, allowing them to make fraudulent purchases or withdraw funds.
In recent years, payment fraud has also risen, involving using stolen credit card information to make purchases.
As a statistic, the Federal Trade Commission (FTC) reported that in the United States alone, online shopping fraud nearly doubled during and after the pandemic, compared to the previous years.
Usually major eCommerce platforms have robust security measures in place, with many of them releasing patches frequently. These patches constantly improve security and users are actively urged to update their systems to the latest versions, to benefit from these increased capabilities to protect against fraudsters.
However, there are many who ignore these threats. As an example, although Adobe announced the EOL for Magento 1 in June 2020 stating that they no longer offer security patches for it, a majority of Magento 1 websites continue to use it. As the stats show it, it is not a small number. In fact, there are over 80000 sites using Magento 1.9 or lower, almost 30% of the total number of all Magento sites.
What needs to be understood is that, even though financial losses can be significant, the impact of eCommerce fraud extends far beyond them.
According to a study by LexisNexis Risk Solutions, every dollar lost to fraud costs U.S. retailers approximately $3.60 due to these additional expenses like chargebacks, refunds, and the costs associated with implementing fraud prevention measures.
But at the same time, businesses that fall victim to fraud may lose the trust of their customers, with a long-term negative effect.
That’s why businesses have to invest in robust fraud prevention strategies to protect both themselves and their customer relationships.
The limitations of traditional fraud detection methods
In the early stages of eCommerce, fraud prevention measures relied heavily on the human factor. Many businesses employed human analysts to evaluate flagged transactions and determine their legitimacy. They used rule-based systems, which are sets of predefined criteria or “rules” used to flag potentially fraudulent activities. For example, if a customer made multiple large purchases in a short period, it might trigger an alert. These rules were often based on past fraud cases and could be adjusted as new fraud trends were identified.
When transaction volumes were lower, and fraud patterns were more predictable, these methods were effective, but with the advance of eCommerce, they soon struggled to keep up with the sophisticated tactics employed by modern cybercriminals.
Welcome to the future: AI-powered fraud detection
The power of Artificial Intelligence (AI), particularly machine learning, lies in its ability to learn from vast amounts of data and identify patterns that are indicative of fraudulent behavior. Unlike traditional rule-based systems, machine learning models analyze historical transaction data to detect subtle, complex patterns that may signal fraud. These models can continuously update and improve their detection capabilities by learning from new data, making them more adept at identifying emerging fraud tactics.
Amazon for example has been using AI and machine learning for fraud detection for a few years now. Amazon’s fraud detection systems can flag suspicious activities such as unusual purchasing patterns, high-risk transactions, and account takeovers by comparing them against the vast amounts of data collected from millions of transactions daily.
A key component of Amazon’s strategy is the use of neural networks to assess risks in real-time. These networks help Amazon identify subtle patterns that may be missed by traditional rule-based systems. Additionally, Amazon leverages predictive analytics to anticipate potential fraud before it occurs, enabling the company to take preemptive action.
Key AI technologies used
Several AI technologies are used successfully by fraud detection systems, making them far more effective than traditional methods.
First, AI models are trained to identify deviations or anomalies from normal behavior. This technology is particularly useful in flagging transactions that do not fit a user’s typical spending patterns, even if they do not trigger traditional red flags.
Neural networks are designed to mimic the human brain’s processing abilities. Far more powerful, they analyze complex data sets and identify intricate patterns that may be indicative of fraud.
Last but not least, Predictive analytics is another vital technology, which uses historical data to forecast potential fraudulent activities before they occur. These models can proactively identify and mitigate risks, providing businesses with a powerful tool to stay ahead of fraudsters.
The advantages are tremendous. One of the primary benefits is real-time detection, where AI systems can analyze transactions as they occur, identifying and blocking fraudulent activities almost instantaneously.
At the same time, AI provides higher accuracy because it can distinguish between legitimate and fraudulent transactions with greater precision. Not last, AI’s capability to learn continuously will help it adapt almost instantly to fraudsters’ new tactics.
There are numerous retailers that have implemented AI in their fraud detection systems. Walmart, Ebay, Alibaba, they all use AI to analyze vast amounts of transactional data in real-time, identifying patterns that could indicate fraudulent activities. But, as you can understand, the costs to undertake such developments are huge.
Developing, training, and maintaining AI models necessitates vast computational resources, specialized expertise, and ongoing data management – all of which can incur considerable costs.
The financial burden extends beyond the initial setup, as companies must continuously update and refine AI systems to stay ahead of evolving threats and maintain compliance with privacy laws. That’s why at the moment, this choice is feasible only for big players in the industry.
What could the future bring
For the first time, AI’s capacity to continuously learn and improve brings the hope that fraud will one day be eradicated. This will be achieved not only by refining existing technologies, but also by finding new ways that will revolutionize the field. Behavioral biometrics and the integration of blockchain technologies are such developments.
Unlike traditional biometrics, such as fingerprints or facial recognition, behavioral biometrics analyze the unique ways individuals interact with devices, such as typing speed, mouse movements, or even the angle at which they hold a smartphone. AI can use this data to create highly accurate user profiles, making it much harder for fraudsters to impersonate legitimate users.
Another key trend is the integration of AI with blockchain technology. Blockchain’s decentralized and immutable ledger – a digital record book that cannot be changed or erased – provides an additional layer of security, making it extremely difficult for fraudsters to alter transaction data.
Additionally, the fact that AI systems employ a multi-modal approach, combining data from various sources – such as transaction history, behavioral biometrics, and even social media activity – will help them build a more comprehensive picture of user behavior and detect anomalies.
These predictions in terms of AI advancements mean that its role in fraud detection is expected to expand and become even more integral to the security of online transactions.
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