Today, almost any kind of search, communication, or commercial transaction can be performed online in just a few seconds: paying bills, buying groceries, selling old furniture– all of these transactions and interactions take just a few seconds. But what we often don’t realize is that digitalization breaches the walls of our lives.
Absent or outdated fraud prevention mechanism can worsen your customers’ experience.
One side of the coin is convenience; on the other, though, is the inherent dangers of all things digital. Cyber frauds of different stripes have emerged, sparing no individual or industry. According to a 2016 report by the Association of Certified Fraud Examiners, a typical business loses at least five percent of its annual revenue to fraud.
But it isn’t just revenue. In the long term, businesses also lose customer trust and loyalty if they can’t prevent fraud. Don’t let an absent or outdated fraud prevention mechanism worsen your customers’ experience.
Identity fraud has hit an all-time high with Javelin Strategy & Research reporting an increase by eight percent (totaling $16.7 million U.S. consumers) in the last year. The most recent event making massive headlines is the Facebook data breach. In an interview with The Guardian, Sandy Parakilas, an ex-Facebook insider, says that “all of the data that left Facebook servers to developers could not be monitored by Facebook, so we had no idea what developers were doing with the data.”
A digitally enabled ecosphere feeds and grows on data fetched from users. On one hand, data enhances the Facebook search experience (enter name, city, and organization name and you can look up anyone who’s on Facebook), but on the other hand, giving away all this data leaves you vulnerable to hackers.
Impersonation is a widespread fraud scheme on social media sites. A scammer impersonating your profile can extract valuable information from your friends or family, including your credit-card details.
And then there are money-flipping scams targeting Instagram users. Scammers draw the attention and interest of people in need of money by showing them pictures of cash, drugs, or luxury items. They then promise a huge payout in return for a small initial investment. Zerofox has published an extremelyinsightful study on how they tracked down 4,574 unique Instagram scams.
All such frauds aimed at culling out and misusing personal information result in a compromised customer experience. The quizzes and games that are meant to keep users engaged, entertained, and loyal are often traps laid by scammers. Seemingly innocent questions can trick users into sharing tidbits of confidential information, such as emails, location, or job.
Instagram shopping, a digital trend quickly picked up by millennials because of its ease and visual appeal, hasn’t been spared of scams either. The Insta-verse is also rife with stories of lost money, lost trust, bad customer experience, and a forever lost user.
The user journey is no longer linear. A potential buyer might find a product through a mobile search, but buy it from a desktop. She might buy a product in a brick-and-mortar store that she got an Android notification about just a few minutes earlier on her way to the office. And she might pay using a PayPal account.
This buying behavior requires retailers to create a unified and seamless user experience. According to the2017 Global Payments Insight Survey, 79% of surveyed merchants and retailers say omnichannel payments are key to creating a seamless customer experience.
But as omnichannel evolves and becomes prevalent, so does the risk of online fraud. Whether card-not-present fraud or mobile-payment scam, the onus of providing a secure shopping experience to customers lies on the retailer.
Companies deploy many fraud-prevention measures to combat cybercrime at all levels and across multiple industries. But do these efforts suffice and do they provide a secure customer experience?
While preventing fraud is obviously important, an overly aggressive fraud detection mechanism could end up badly affecting customer experience, for example with slow transaction speeds, access or permission denied, holdups at checkout, or card not being accepted. These scenarios could happen if a company’s online fraud-prevention approach:
Take digitally enabled bank deposits as an example. To the consumer, it might seem like a very simple process. All that has to be done is accept all good deposits and block out the bad ones. But banks don’t just have to skim the bad from good; they also have to make sure that good customers aren’t denied access to their money or face any delay in the process of fighting fraud. The onus of creating an exceptional service experience for depositors lies on banks.
Improved security and privacy don’t have to be a bottleneck for online customers. In fact, putting the right fraud-control solutions in place can enhance the overall experience.
A 2017 report titled “Financial Institution Fraud Trends: ATO and Application Fraud Rising Rapidly” states that 44% of financial institutions have reported customer experience as an extremely important component in the business case for a new fraud-detection solution. That’s why customer-experience enhancement teams are now actively involved in selecting such solutions.
Machine-learning solutions have made fraud detection smarter. You can now leverage visitors’ online behavior and network patterns to detect and disarm fraud. A good machine-learning approach to fraud detection can use data and information from past incidents of fraud, analyze patterns of fraudulent behavior, and predict future possibility of fraud.
An interesting example is the application of machine learning to money laundering. A robust fraud prevention solution allows banks to identify customer banking patterns online, use data collected along with events triggered at multiple touchpoints in a user’s omnichannel journey, and analyze all this information to detect fraud. All this happens with the help of an advanced data analytics engine.
An outdated fraud prevention system is often intrusive and can flag legitimate transactions as well. A modern machine-learning system uses a rule-based approach to detect scams. For example, it can create:
A user with a high score on both of these parameters gets flagged as a scammer. By dramatically increasing the number of correctly flagged scams, machine learning automatically leads to increased customer satisfaction and trust between the company and legitimate customers.
The ability to dive deep into data, where large volumes of transactions are involved, is what makes machine learning a truly new-age solution for fighting fraud. We at Simility are building the future of fraud prevention with machine learning. Schedule a demo for a quick product walk through.
Note: This post was originally published on Simility's blog here.