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Why AI alone can't meet all fraud detection needs in E-commerce

In the fast-paced e-commerce industry, staying ahead of fraudulent activities is an ongoing task. While Artificial Intelligence (AI) has become the go-to tool for many businesses, its limitations are often left in the shadows. This article aims to shed light on these overlooked aspects, revealing the crucial gaps in AI-powered fraud detection that industry experts need to be aware of.

Michaela Munitzk Jan 10, 2024 5 min
A woman gazing at a computer screen with visible data reflections.

The tendency to be overly cautious

One of the most significant issues with AI in fraud detection is its tendency to be overly cautious. This can result in legitimate transactions being identified as fraudulent. Such situations might not be uplifting for the customers, potentially affecting their experience and their loyalty to the company. Imagine a scenario where a regular customer is unexpectedly hindered from completing a purchase – this could lead them to consider shopping with competitors.

The challenge of peak seasons

During peak seasons such as Black Friday, efficiently handling an increase in orders while preventing fraud is the key issue. Without precise adaptation for high-demand periods, AI systems risk either allowing fraudulent activities or blocking legitimate transactions. Therefore, finding this balance is essential for profit retention and customer trust.

Types of fraud AI can’t catch

A 2022 study showed that 'friendly fraud' is common in e-commerce. This occurs when customers buy things and then ask for refunds. While it remains an ongoing issue, other types of fraud, such as creating fake identities or exploiting return policies, are similarly challenging for AI. These methods require a deeper understanding of human actions and motives, which AI often misses.


AI and the complexities of human behaviour

A major challenge for AI is gaining a comprehensive understanding of complex human behavior, especially when considering the evolving tactics of fraudsters. These tactics often mimic legitimate customer behavior, posing a challenge for AI systems that rely on historical data and predefined patterns. This becomes particularly evident in cases of social engineering fraud, which involves using psychological tactics and interpersonal interactions, rather than exploiting technical loopholes. In these instances, it is not solely about detecting unusual transactions, a task AI can often perform, but rather about understanding how people are manipulated in their interactions with others.

The need for a layered approach  

Recognizing these issues is the crucial step in improving e-commerce fraud detection. To achieve a more comprehensive approach, it's essential to incorporate AI alongside other methods. This insight is critical to handle complex online fraud and developing strategies that mirror the dynamic and adaptable nature of the fraudsters themselves.

As e-commerce grows, so does clever fraud.

Knowing what AI can and cannot do in fraud detection is crucial for companies that want to protect themselves and their customers. By recognizing these gaps, industry leaders can create stronger, more flexible solutions to stay ahead of fraudsters.