Ever since people have engaged in commercial activities, they’ve had to deal with fraudsters. The only way business owners can stay ahead of bad actors is to deploy equally sophisticated tactics that detect and block those unlawful efforts.
With eCommerce thriving since the pandemic, however, it’s getting more challenging to keep up. Retailers are offering more products and services through an increasing number of consumer touchpoints and are enabling new payment options through more platforms. As innovation and variety enhances the customer experience, it also creates more openings for suspicious activities.
To address the issue, organizations are turning to computational power and advanced algorithms, and leveling up their fraud prevention platforms with machine learning capabilities.
How does machine learning work?
To understand how computers can help weed out fraud, prevent monetary losses and protect customer relationships, we have to peek under the hood of machine learning systems.
A machine learning engine is a collection of algorithms trained on specific data that applies that training to make assessments about new data. In fraud prevention, the system is trained on a retailer’s historical transaction data, and detects patterns associated with fraudulent activity. When the system is used during a new transaction, the system assesses the data points for that transaction, performs a risk calculation, and assigns a risk score to that customer based on its training model. According to the risk threshold set by the merchant, that transaction either goes through as usual, is blocked, or is flagged for further assessment.
The programming behind machine learning might be complex, but the principle is based on human learning. Just as people draw on a range of facts and knowledge about past situations to make predictions about what will happen in new circumstances, machine learning systems for fraud prevention use everything they have learned about fraudulent and non-fraudulent activities for specific merchants to make highly accurate risk assessments about each and every transaction.
Most importantly, machine learning systems can provide those all-important risk assessments in an instant. When retailers are selling to customers around the globe, 24/7, across a range of online platforms, this degree of enhanced fraud prevention power cannot be underestimated.
Better rules, better protection
The goal in fraud prevention is to detect and prevent as many fraudulent transactions as possible, and to let the maximum number of genuine transactions pass through. This is a delicate risk calculation that involves many variables.
Older fraud prevention techniques were built around rules. Merchants established payment limits, for example, or required extra authorizations for subsets of transactions they’d identified as unusual or risky for their sector. When implemented face-to-face, a sales associate might say, “We don’t accept payments over $1000 by phone.” Or, “We need to ask for ID with credit card payments over $500.” In eCommerce, these rules are implemented through software. When you’re asked to enter to PIN code on a credit card purchase over $100? That’s a fraud prevention rule in action.
The problem with ad hoc rules is too many false positives. “True” positives are actual fraudsters detected and blocked, while “false” positives are honest customers – also detected and blocked. While fraud prevention is important, overly stringent protections can cause problems for your customers, especially those earning you the most profit through large and frequent purchasing. Falsely flagging high-value customers as potential criminals, or burdening them with cumbersome authentication procedures can damage your relationship and impact your revenue stream.
When rules are not stringent enough, on the other hand, they generate too many false negatives – the fraudulent transactions that pass through undetected – the whole reason for fraud prevention in the first place.
Because suspicious activity can take a myriad of forms, it’s hard for businesses to know what rules to establish, and how to apply them. That’s where the power of machine learning algorithms comes in. They can read across thousands of data points to detect many things at once, and correlate seemingly unrelated details or behaviours. By surveying the data and learning the patterns specific to your transaction types and your customers, those algorithms can actually “discover” the rules that best protect your business.
Machine learning brings added benefits
As with fraud prevention software, merchants have the flexibility to set risk thresholds higher or lower according to their comfort level. Retailers can also use machine learning systems as a first layer of protection, then choose to manually review individual transactions with high risk scores. Alongside increased accuracy and flexibility, businesses benefit in other ways from machine learning for fraud prevention:
Instant risk assessments: Technically speaking, people could perform the same risk assessments as computers do. Data analysis is math, after all. Given the size of the transaction data and all the information involved, however, it would take eons. Machine learning algorithms speed up the fraud prevention task, so businesses stay ahead of fraudsters and save their time for genuine customer transactions.
Efficiency through automation: Most transactions are above board and don’t merit manual review. Retailers need fine-tuning with those fraudulent outliers, but that’s labour intensive work. Letting an advanced system analyze shopping behaviour and pinpoint abnormalities on your behalf saves time and resources.
Adaptivity: Machine learning systems aren’t designed to be static, but to incorporate new data into their modeling. More data makes for better modeling and more accurate risk assessments. As business is transacted, therefore, and more data is collected, the system is continuously improved to make better predictions.
Cost savings and scalability: Other fraud prevention tactics can be expensive, especially if they involve hiring risk assessment specialists or having your associates perform manual assessments. Training a platform to perform fraud prevention is far less expensive and can scale to fit the ebbs, flows, and growth objectives of businesses of any size.
Full-time fraud prevention: Fraudsters don’t take holidays, and neither do computers. Machine learning systems for fraud prevention generate risk scores for every single transaction – any time of day and night. Business owners know each transaction has been veritably assessed, and can have those same assurances – to use for compliance purposes, or to meet the expectations of stakeholders and partners.
For businesses, fraud is a fact of life
With eCommerce becoming the norm for businesses of all sizes and types, no merchant is immune from transaction fraud. Retailers and service providers in every sector – from multinational enterprises to micro-merchants – need to think about leveling up their fraud prevention strategies using the best available technologies.
Thankfully, machine learning is making payment security easier for online retailers by identifying suspicious transactions and blocking fraudulent activity with cutting-edge accuracy. Business operations proceed as normal with fraud prevention running at top capacity with machine learning capabilities behind-the-scenes, without disturbing the customer experience. Best of all, the systems are getting better all the time.