In the fast-paced world of ecommerce, fraud is evolving at an alarming rate, meaning many companies struggle to stay ahead of online fraud and abuse.
This problem is never more acute than during retail peak season, as Black Friday approaches, followed by Christmas and the January sales. But the biggest threat for most merchants isn’t organized cybercrime—it’s their own customers.
Over the past decade, we’ve seen customer-led fraud growing rapidly. It’s now common for everyday shoppers to take advantage of generous return and refund policies, buy clothes to wear once and return, and make fake claims about not receiving items (or receiving them in an “unacceptable state”).
Worse still, some consumers are turning to fraud-as-a-service schemes, and particularly “refunds-as-a-service”, where criminal third parties use social engineering and falsified information to get a fraudulent refund for a customer. The customer gets to keep the item and still receive a refund, while the professional fraudster gets a cut of the profit.
For a growing number of consumers, committing these types of fraud has become normalized. They see it as a victimless crime.
Recent Ravelin research into customer fraud found that more than 40% of internet shoppers admit to committing fraud within the last 12 months. Moreover, one in three (36%) are considering committing fraud in the future. First-party fraud (fraud committed by the actual cardholder rather than a professional using stolen card details) is now the top risk factor for ecommerce finance leaders.
And the risks to merchants are considerable. GlobalData estimates that in 2022, UK shoppers returned over £4.1 billion in online clothing orders alone, with predictions that this will grow by 17% by 2027.
How can businesses fight back?
For enterprise companies facing this double threat of organized crime and customer-driven dishonesty, a dedicated fraud team is essential. But they are often stretched too thin. It’s clear that technology must integrate more deeply, giving fraud professionals the speed and precision to respond to increasingly complex fraud.
The best way to do this is to apply engineering principles and automation—primarily artificial intelligence (AI) and machine learning (ML)—to the rising problem of ecommerce fraud. ML isn’t just an additional layer of protection. It can only be at the core of fraud defense, enabling businesses to block fraud before it happens, by constantly detecting and flagging patterns and anomalies that would otherwise go undetected, and providing recommendations.
Importantly, ML allows for robust fraud and abuse protection at scale. Instead of expecting humans to review each suspicious case manually, its recommendations allow for better decision making when faced with thousands of orders. Humans can thus make better decisions and automate protection from fraud and abuse.
Fraud is becoming increasingly sophisticated, while legitimate customers are evermore demanding and quicker to complain or churn. Merchants need to bring intelligence and automation to fraud if they are to grow.
Growing numbers of merchants are adopting AI-based, real-time tools which detect and block fraud automatically. This enables online businesses to contain fraud risks while maintaining a frictionless experience for genuine customers.
From gathering historical transaction data to fine-tuning models and ongoing human evaluation, the process helps merchants recognize and understand behavioral patterns rather than simply flagging each fraudulent transaction in isolation.
This approach provides fraud leaders and their teams with granular analytics that allow them to usher good, loyal customers through to checkout – without adding undue friction to their customer experience.
Balancing fraud prevention and great customer experiences
Balancing fraud prevention and customer conversions is a priority for many companies. The most effective risk mitigation strategy isn’t just about blocking fraud but ensuring legitimate customers can make their purchases with ease. Attention spans are short and customer loyalty is never guaranteed, so a seamless purchasing experience is crucial.
Using whitebox ML models, which are fully transparent and explainable, means fraud teams can understand each flagged transaction in-depth, and that ML models are constantly improving. As a result, fraud teams can make better decisions, as well as adjust their fraud rules accordingly.
It also means fraud defenses can better spot and respond to new fraud attacks without direct human intervention. Thus, merchants can keep one step ahead of fraudsters without unnecessary friction in legitimate customers’ shopping journeys.
The future of fraud prevention
Ecommerce fraud will only continue to grow and diversify. Professional fraudsters will refine tactics to blend in as genuine customers, while opportunist customers will continue to take advantage of companies and share their methods with other consumers via social media, effectively “democratizing” fraud and abuse against companies.
This means that anti-fraud systems need to evolve rapidly, leveraging vast amounts of data and the benefits of AI to spot fraud and predict it before it happens. Constantly pushing technology to adapt to these shifts is paramount.
The coming years will bring greater integration of AI tools into customer service, payments, and delivery processes. Fraud detection needs to be increasingly embedded into every stage of the customer journey, helping brands catch fraud early and provide a seamless experience for honest customers.
This doesn’t mean asking every single customer to prove they are who they claim to be—quite the opposite. An ML-first approach rewards good customers with minimal friction, in addition to stopping fraud and abuse.
More importantly, it can help businesses take action against customer fraudsters who flagrantly break the rules. Many of our customers find that a simple warning message is all that’s needed to deter fraudsters before they become too troublesome. Others pursue tighter sanctions.
It is clear that growing businesses safely online depends on an automated, AI-first, predictive approach to fraud – whether committed by career criminals, or merchants’ own customers.
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