Generation Z – Gaining Loyalty Through Fraud Protection

Gen Z – The Youngest Generation Targeted by Fraud

Generation Z includes those born between 1997 and 2012 – a group otherwise known as the first true ‘Digital Natives’. In 2022, Gen Z-ers will range from ages 10 to 25, and while many would assume their tech-savvy, lifelong relationship with technology would protect them from online fraud, they are, in fact, proving to be the youngest and most vulnerable target group for this growing trend.

In fact, their familiarity with technology is creating a false sense of security, and as a result, Gen Z is facing more unexpected challenges than any other, with a record level of negative customer experiences.

Gen Z-ers are shrewd consumers but far from safe from cyber-criminals. As new generations build unique relationships with technology, predatory fraudsters stay keenly on the prowl, tailoring their tactics to meet the behaviour of those they wish to target.

A 2021 study found that consumers in this generation are more likely to fall victim to phishing scams and are victims to more unauthorised bank and credit card charges than any other – a surprising fact, considering their acquired title.

Merchants Need to Protect Gen Z’s Consumer Power

Online payment fraud remains a top concern for merchants – falling victim to fraud results in a loss of credibility, reputation, and customer numbers. For the customer, it means losing confidence in a merchant that may decline their order.

In order to mitigate generational fraud, maintain customer loyalty, and ensure protection from other cyber scams, merchants should be prepared to give the consumer power of Gen Z top priority. This means that the onus is on merchants to provide a series of safeguards, which will preserve the customer shopping experience and create a stronger sense of loyalty during Gen Z-er’s prime consumer years.

Fine-Tune Screening Rules for Each Sales Channel

The first step merchants should take is to monitor each sales channel to detect a range of specific metrics. Data showing good orders approved, good orders rejected in error, blocked fraud, and successful fraud should be analysed to pinpoint the fraudsters’ preferred current movement.

This will determine which channel (for example, mobile, online or social media) has the highest number of fraudulent attempts and which has the highest count of false rejections. This information can then be used to adjust and fine-tune order-screening regulations to better fit each individual sales channel. Rules can be used to allow as well as prevent, but they can also be robust, enhancing the protection of consumers and reducing fraud-related costs for merchants.

Identify Good and Bad Consumer Behaviour

Basic fraud-screening tools can often generate the automatic rejection of flagged orders. Although a merchant may feel that this process is enough to weed out good consumer behaviour from the bad, automatic rejection of a good customer may occur, leaving an honest customer labelled as ‘risky’, resulting in a lost relationship between consumer and merchant.

Artificial Intelligence (AI) offers a more robust analytic screening process that can be used to prevent a huge number of true fraudulent attempts. Order data are analysed against datasets including geolocation and device ID, a customer’s purchasing history, the velocity of their online purchases, and other important markers.

It is important to note that even AI can occasionally miss factors and may flag an order as being suspicious. It is therefore important for merchants to implement fraud-analysts to manually review orders which have passed through the AI, to create a fine level of clarity to ensure that those orders flagged are indeed suspicious and to allow the good customers to retain their order. This will protect consumer trust as well as the merchant’s costs.

Use Machine Learning Systems as a Way of Keeping up With Fraudsters

Fraudsters are stealthy. Obtaining stolen credentials on the dark web is easier than ever, and impersonating generational buyer behaviour by passing Know Your Customer (KYC) checks is becoming second nature. Merchants should implement sophisticated machine learning systems (ML) to keep up with fraudsters.

These automated systems use trained models that can clarify all order decisions that have passed through an AI system and manual spot checks by learning, adapting, deploying new algorithms, and refining fraud control at the ultimate speed.

Over time, a clear resolve to distinguish the good customers from the bad will become more precise, resulting in the ultimate fraud-detection process.

Understand the Unique Challenges of Gen Z

For merchants, it pays to invest in fraud detection – now, more than ever. Generation Z makes up 30% of the world population, and as they interact with the internet, they are more inherently trusting when it comes to sharing personal data, meaning their consumer power hangs in the balance.

A fight-back against fraudulent activity, by leveraging the right tools, will protect merchants from falling prey to those deliberately targeting Gen Z, and in return, will protect Gen Z from becoming victims of fraud. The overall benefit of a crackdown on fraudulent activity will result in a positive customer experience at a lower cost to the merchant.