Manual reviews are the solution to fraud


The third fraud prevention myth we will examine is that organizations must manually review all transactions in order to maintain oversight and control, on the premise that fully automated decisions remove visibility and control. There are many reasons why this is not true, but to save time we will focus on the first four. We will also examine why fully automated fraud prevention solutions are superior to any solution that requires manual review of some or all of its decisions.

Automation does not remove the fraud team; rather it increases their efficiency

A common misconception faced by vendors of automated fraud prevention solutions is that they aim to replace a dedicated fraud team. That couldn’t be further from the truth.

Fraud teams too often work in hindsight – they individually judge reported transactions based on their reliability and legitimacy. Automation frees fraud teams from the constraints of a manual review process and allows them to work more efficiently. Machine learning and AI form a more holistic view of fraud that enables anti-fraud staff to work proactively. This approach enables teams to be more attentive to changes in business trends and allows team members to focus on designing innovative payment technologies or pursuing emerging opportunities.

Machine learning and AI are safer and more accurate than manual review

Human reviewers are trained to identify patterns in datasets. However, fraudsters regularly adopt new methods to successfully outsmart reviewers. As advanced methods become more accessible, the effectiveness of manual examinations is reduced.

Manual exams also create subtle problems that can get worse over time. People naturally introduce bias into their decision-making, and this often translates into their work. These biases create inconsistencies in payment verification criteria that can lead to pass and fail for two transactions with very similar attributes. An additional downside of manual review is exposing customer data to employees. The more customer information passes through the hands, the more security deteriorates overall.

Due to the problem of human bias, machine learning and AI are the future of fraud detection. Together, these advanced technologies can spot and prevent repeat fraudsters, identify patterns that would otherwise be missed, map and ultimately prevent new types of fraud. Using machine learning fraud detection tools, thousands of customer attributes can be assessed in seconds against known fraud patterns.

Manual reviews can’t keep up

Most online retailers experience business changes throughout the year. Retailers are busier at some times than others. For example, travel sites and hospitality industries can easily be inundated with summer travelers from June to September, Black Friday and Cyber ​​​​Monday sales draw in droves of online shoppers, and semi-annual sales increase. Requirement. Other times it may be more sporadic if a retailer announces a great deal on short notice.

The question for retailers who rely on manual ratings is: How do you manage a 35% increase in sales volume in such a short time?

Fraud teams are not equipped to control sudden fluctuations in transaction volume on their own. Additional contractors are only a partial solution as they may not have the full context to make accurate decisions. The pressure to process sales may lead examiners to approve riskier transactions to keep pace or halt operations as examiners tackle the growing backlog. None of these solutions are ideal and completely solve the problem – a high price to pay for a perceived sense of control.

Unsurprisingly, automated solutions avoid these pitfalls because hundreds or even thousands of decisions can be made in seconds while adapting effortlessly to business priorities. The volume of sales recorded by merchants during Black Friday and Cyber ​​Monday perfectly illustrates this point. In 2021, more than 40% off Black Friday sales were facilitated by mobile phones and more than half of online shoppers were first-time shoppers. These overlaps in consumer behavior create the perfect recipe for disaster for manual reviewers, but are easy to address with AI and machine learning-based fraud prevention solutions.

Manual exams hinder value-added services such as buy online, pick up in store (BOPIS)

The disruption caused by the pandemic has permanently changed consumer expectations about their shopping experiences. BOPIS has become a popular method for customers to receive their goods as contactless options have become necessary. The success of many of these value-added services, such as BOPIS, relies on rapid reliability assessments.

But consider what would happen if a customer made a transaction online and arrived at the physical store, only to find that the item they purchased had not been approved by the merchant?

This scenario is not hard to imagine as it happens many times in reality when fraudulent sellers do not automatically verify their transactions. Even some vendors that use machine learning still mistakenly review a small percentage of transactions manually to maintain normal chargeback and approval rates. Our advice to organizations wishing to take advantage of value-added services should find a solution that offers fully automatic decision-making to avoid false rejections.

Fraud teams are the unsung heroes of the e-commerce industry. Their efforts protect business bottom lines, but their work can often be a difficult balancing act. When working effectively and unhindered with fraud prevention technologies based on artificial intelligence and machine learning rather than manual reviews, customers are matched with the products they love faster and businesses continue to grow without risking losing to fraudsters.

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