If Julia Robert’s character from Pretty Woman had been shopping online instead of at the boutique, she would have had no problem getting the dress she wanted. After all, she did say that she had money to spend. It was her appearance that kept the saleswoman from waiting on her. Appearances- something ecommerce websites do not see.
This is great and not so great.
While no one will be turned away from a website just because they aren’t dressed well, the convenience of making payments online increases the risk of fraud.
In 2017 alone, over 1.16 billion people shopped online from across the world. The ecommerce industry is predicted to reach a valuation of US$5 trillion by the year 2021. As the industry grows so do the ways in which fraudsters can scam their way to make easy money. In 2018, card-not-present fraud in the USA cost ecommerce retailers $19 billion. This was almost double the losses causes by such fraud transactions in 2014.
Fighting ecommerce fraud is something that every ecommerce venture must take seriously. An unprotected website can not only cause losses for the company but also put customer data at risk.
Make Data Smarter
Data is essential for every ecommerce transaction. Information about the customer is collected at various stages of interaction. When an account is created, customers enter in their name, date of birth, email id, phone number, etc. Similarly, when a customer makes a purchase, they enter their credit card details as well as personal details such as their shipping address. Without this data, no transaction can be completed. This data is also used by companies to personalize their services and improve customer relations. However, that’s not all this data is capable of doing.
The data provided by each individual helps create a unique profile for the person. This data can be used smartly to detect and prevent ecommerce fraud by verifying identities before completing transactions. It can keep fraudsters from gaining access to ecommerce accounts and simultaneously authorize legitimate transactions. Thus, it maintains the balance between protecting the company’s interest and the ease of making purchases.
How can Smart Data be used to Detect Fraud
Card-not-present transactions are the most common type of fraud plaguing ecommerce websites. This refers to transactions that take place online wherein the merchant is not presented with a physical credit/debit card. These fraud transactions typically occur when hackers gain access to someone else’s card details. This stolen data is then used to create fake online accounts for bogus transactions.
Smart data can help verify accounts and identities without complicating the process. Here are a few ways this can be done.
An email can be used for more than just sending weekly newsletters. It can play a vital role in verifying accounts at the time of their creation.
The process is simple and involves sending a notification link to the email address. Only when the person opens the link in the email will his/her account be activated. Similarly, transactions can be authenticated by emails containing codes associated with the activity. In such cases, the transaction may be completed only on entering the code received by email. If no code is entered, the transaction is void.
At the same time, if a person receives an email with such a code without having initiated a transaction, the fraudulent transaction may be identified before it can be completed.
People aren’t just shopping on their laptops today. Almost 80% of all people who own a smartphone have shopped through their phones at least once. Thus, mobile verification is essential for fraud prevention.
Like emails, mobile numbers can be used to verify user identity through unique codes sent via messages. Transactions will be completed only on entering this code. This process is also known as two step authentication or two factor authentication.
Mobile data can be used in other ways as well. For example, it may be cross referenced against mobile network operator information to get an individual’s location. If the transaction is being made from another location, it may be flagged to prevent fraud.
Every individual has a number of documents to verify his/her identity. For example, a passport, a driver’s license, government issued identity cards, etc. In the physical world, these can be shown to anyone to prove one’s identity.
In the digital word, manual verification may be difficult but these documents can still play a role in fraud prevention. Customers may be asked to upload a photograph of the document along with a ‘selfie’ when setting up accounts. This data can then be compared with data from other sources to validate the same.
AI analysis of customer data can boost machine learning and prevent fraud. For example, say a transaction as being made in the wee hours of the morning. If the AI analysis of the customer’s usual behavior showed that other purchases were usually made during the day, the transaction could be flagged for further verification.
While protecting the company and consumers against fraud is essential for an ecommerce company, security cannot be viewed in absolute terms. Flagging a legitimate transaction as fraudulent can be just as expensive as a fraudulent transaction. For example, rejecting a legitimate transaction can make customers turn to other websites for their retail needs instead of becoming lifelong customers at your website. Thus, smarter data or the smarter use of data is the only true solution.
Customer data is the lifeblood of your organization. But it goes bad (up to 25% per year). Much of it is incorrect, outdated, or unstructured and fragmented across multiple systems – preventing operational efficiency, successful marketing and sales efforts, and a rewarding customer experience.
For more than thirty years, Melissa has been a leading provider of global identity verification and data quality solutions. Using cutting-edge technology and multi-sourced global reference data, we provide the solutions to support your Know Your Customer (KYC) initiatives, prevent fraud, reduce costs and improve fulfilment – at every point of the data chain.