Imagine this scenario: a prospective new customer begins the online application process to open a bank account. The site is designed with customers in mind and its user interface results in a workflow without friction. Yet this customer, like so many of us, now works from home and is trying to get her application finished on a lunch break… while parenting… and feeding the dog. With these distractions, she accidentally types her last name incorrectly and the site’s fraud detection system inaccurately flags her input as fraudulent. This is a textbook example of customer friction.
She’s unable to finish the application, and, after a few choice words uttered under her breath, returns to work and abandons that financial institution (FI) as an option.
Here, a false positive or friction results in the loss of the lifetime value she represents as a customer.
Fraud In The Time Of Coronavirus
With online activity in e-commerce and banking growing exponentially in the COVID-19 era, fraud attempts have increased. According to Feedzai’s 2021 Financial Crime Report, 2020 saw a significant increase in stolen credentials being sold on the dark web. This proliferation of stolen credentials, along with the exponential rise in online transactions, provided ideal conditions for fraudsters to blend in with legitimate consumer traffic without being detected.
In 2018 alone, banks stopped $22.3 billion in fraud. With so much at stake, in trying to prevent these fraudulent online behaviors, there have inevitably been real consumers caught in the crossfire. While financial institutions of all types try to decrease fraud exposure and limit losses, the collateral damage from those efforts can be the decrease in real customer conversions.
False positives, when a genuine customer is marked as a fraudulent bad actor and blocked from further site interaction, happen even with the best user experience design but reducing them is crucial. Not only do businesses with a high false positive rate lose potential revenue but they also lose the good faith of customers. The ramifications of driving away people who could have been loyal and even lifetime customers who are caught in the dragnet and declined may be worse than the fraud itself. Not to mention the wasted spend of millions of dollars in marketing expenses that generated those declined potential customers.
What Is The Real Cost Of Fraud False Positives?
In 2020 there was a nearly 250% increase in fraud attempts which coincides with the recent explosion of mobile banking use. Because of this significant increase in fraudulent activity, there is a clear need to tighten up the verification process; however, using legacy systems to reduce fraud generally means a higher number of false positives and lower conversion rates. Fraudsters and cyber-shoplifters are deploying extremely sophisticated fraud tools and techniques to try and fit in with the massive shift to digital channels.
Yet an overzealous response with rigid fraud protection strategies can tarnish a genuine customer’s experience. And that customer might never come back. The aversion to false declines and its impact on a customer’s attitude is particularly strong with tech-savvy millennials, where nearly 60% revealed to an Aite Group report commissioned by the fraud prevention experts at ClearSale that “[millennials] would be very or somewhat likely to leave their financial institution due to a credit card false decline.” Further data found by research advisory firm Javelin shows that “58% of declined transactions are legitimate orders.”
The Aite report, titled, “The Ecommerce Conundrum: Balancing False Declines and Fraud Prevention,” states that 62% of merchants have noticed increased false decline rates. According to the report findings, “Losses due to ecommerce fraud are projected to reach $6.4 billion by 2021. But losses due to false declines are projected to reach $443 billion by 2021.”
So false positive losses result in almost 70x more than fraud losses! These numbers are too staggering to dismiss.
False Positives Lead To Lost Revenue And Marketing Dollars But Also Lost Customer Lifetime Value
One of the first casualties of false positives is lost sales revenue but that loss extends to an opportunity cost for marketing and sales. Those department’s combined efforts, and all the time and money spent internally, can swell a sunk cost with each frustrated customer. As one Harvard Business Review article points out, it is extremely important to conduct a risk and benefit analysis of any AI-backed fraud detection system. Determining if that tool fits your company needs is a strong first step toward reducing these false positive losses.
When conducting your risk/benefit analysis it helps to ask these questions:
- Lifetime Value of a Customer (LTV): What is the long-term revenue an average customer brings our way?
- Customer Acquisition Costs (CAC): How much does it cost us to get a potential customer’s eyes on our site and eventually convert them?
To effectively weigh the risks and benefits of a fraud detection strategy and answer these questions, companies need to look at the challenge of the false positive ratio in fraud detection from a new perspective, with new tools, and perhaps more importantly… with new data.
How To Reduce Your False Positive Rate (Without Increasing Fraudulent Activity)
The two goals of reduced fraud and higher conversion rates don’t have to be mutually exclusive. In fact, they can work together to provide an ideal environment, balanced to where fraud and risk is minimized while conversion is optimized.
The answer to this growing problem is this: integrate behavioral data gathering and analysis into your current fraud prevention system. This new source of data provides a game-changing lens into what’s actually happening on the other side of your online form. Using behavioral analytics, users’ digital body language is available to organizations immediately and can give companies valuable insight into which customers are real and valuable…and which are fraudulent or risky, from day-one.
Use Behavioral Analytics To Increase Customer Conversions And Reduce Fraud
You don’t need to ditch existing security or take away protocols but instead look to integrate an added layer of behavioral data and analytics. Battling false fraud positives, just like fraud itself, is a process requiring nuance instead of a black-and-white approach.
At Neuro-ID, we partner with companies and help supplement existing fraud systems with cutting-edge behavioral analytics data to preserve client loyalty and reduce friction. What would you do if you could accurately separate your genuine and fraudulent customers? Tap into your behavioral data and find out today.