The third fraud prevention myth we will examine is that organizations have to manually review all transactions in order to maintain oversight and control, with the assumption that completely automated decisions remove visibility and control. There are plenty of reasons why this is not true, but for the sake of time, we will focus on the top four. We will also take a look at why fully automated fraud prevention solutions are superior to any solution that requires some or all of its decisions to be manually reviewed.
Automation does not remove the fraud team; rather it augments their effectiveness
One common misconception automated fraud prevention solution providers face is that they are aiming to replace a dedicated fraud team. This could not be further from the truth.
Fraud teams far too often work in a retrospective manner — they individually judge flagged transactions based on trustworthiness and legitimacy. Automation liberates 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 perspective of fraud which allows anti-fraud personnel to work proactively. This approach allows teams to be more alert to changes in business trends and lets team members focus on designing innovative payment technologies or pursuing emerging opportunities.
Machine learning and AI are safer and more precise than manual review
Human reviewers are trained to identify patterns within datasets. However, fraudsters routinely adopt new methods to successfully outsmart reviewers. With advanced methods becoming increasingly accessible, the effectiveness of manual reviews is reduced.
Manual reviews also create subtle problems that can become magnified over time. People innately introduce biases into their decision-making, and it often translates into their work. These biases create inconsistencies in verification criteria for payments which can lead to one pass and one fail for two transactions with very similar attributes. An additional drawback of manual review is exposing customer data to employees. The more hands customer information passes through, the more security deteriorates overall.
Because of the issue human bias brings into the mix, machine learning and AI are the future of fraud detection. Together, these advanced technologies can spot and prevent repeated fraudsters, identify patterns that would otherwise be missed, map, and ultimately prevent new types of fraud. By employing machine learning fraud detection tools, thousands of client attributes can be evaluated within seconds against known fraudster patterns.
Manual reviews can’t keep up
Most online retailers experience shifts in business throughout the year. Retailers are busier at certain times rather than at others. For example, travel sites and hospitality industries can easily become inundated with summer travelers from June to September, Black Friday and Cyber Monday sales bring flocks of shoppers online, and semi-annual sales increase demand. Other times it can be more sporadic if a retailer announces a stellar deal on short notice.
The question for retailers that rely on manual reviews is: How do you handle a 35% jump in sales volume within such a short timeframe?
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 possess the full context to make accurate decisions. Pressure to process sales may lead to reviewers approving riskier transactions to keep with the pace or grind operations to a halt as reviewers tackle the growing backlog. None of these solutions are ideal, nor do they solve the issue completely — a hefty price to pay for a perceived sense of control.
Unsurprisingly, automated solutions avoid these pitfalls as hundreds to thousands of decisions can be made in seconds while effortlessly scaling to match business priorities. The sales volume experienced by retailers during Black Friday and Cyber Monday perfectly illustrates this point. In 2021, over 40% of Black Friday sales were facilitated by mobile phones and over 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 tackle with AI and machine learning-based fraud prevention solutions.
Manual reviews hinder value-add services like Buy Online, Pickup In-Store (BOPIS)
Disruptions caused by the pandemic have permanently altered consumer expectations for their shopping experiences. BOPIS has become a popular method for customers to receive their goods as contactless options became necessary. The success of many of these value-added services, like BOPIS, relies on quick evaluations of trustworthiness.
But consider what was to occur if a customer completes a transaction online and arrives at the physical store, only to discover that the item they purchased had not been approved by the merchant?
This scenario is not hard to imagine because it occurs many times over in reality when fraud vendors don’t automatically verify their transactions. Even some providers who employ machine learning still mistakenly review a small percentage of transactions manually to maintain normal chargeback and approval rates. Our advice to organizations wanting to take advantage of value-added services should find a solution that provides fully automatic decision-making to avoid false declines.
Fraud teams are the unsung heroes of the e-commerce industry. Their efforts protect businesses’ bottom lines, but their work can often a be difficult balancing act. When they work efficiently and unimpeded with AI and machine learning-based fraud prevention technologies rather than manual reviews, customers are matched with the products they like faster, and businesses continue to grow without the risk of losing out to fraudsters.
