A guide to fraud analytics
The total cost of ecommerce fraud to merchants will surpass $48 billion in 2023, up from slightly more than $41 billion in 2021, according to a study from Juniper Research. Fuelling this growth, according to Juniper, will be "the increased use of alternative payment methods, such as digital wallets and BNPL (buy-now-pay-later), which are creating new fraud risks."
It's never been more important for businesses operating in the digital economy to focus on mitigating the risk of online payment fraud. But, to achieve this effectively, these businesses need access to data that empower them to uncover patterns of malicious behavior and stop fraud in its tracks.
Here we provide an introduction to fraud analytics, highlighting:
- What fraud analytics is
- How machine learning and artificial intelligence aid fraud detection
- The benefits of leveraging fraud analytics
- How Checkout.com gives merchants access to a wealth of fraud analytics
What is fraud analytics?
Fraud analytics uses data analytics to help organizations detect and prevent online fraud. Fraud analytics entails collecting, storing, and analyzing relevant data to find patterns and anomalies that may signal suspicious or risky behaviors, then turning those discoveries into insights that merchants can use to help manage fraud.
Online businesses must apply fraud detection analytics to combat bad actors because employees cannot manually analyze the massive number of transactions that flow through their systems daily. Fraud analytics enables merchants to identify fraud patterns in order to create new interventions to block future fraudulent attacks.
The role of machine learning in fraud detection
As online fraud becomes ever more present, it's also becoming more sophisticated. And as fraudsters use new technologies and tactics, businesses must do all they can to stay one step ahead of these threat actors.
That means accessing and analyzing data—and lots of it. That's where fraud detection machine learning models come into play. They can work around the clock to analyze huge amounts of data in real-time.
Fraud detection uses machine learning to enable online retailers to quickly detect suspicious transaction activity as well as spot patterns of behavior before any fraud occurs. When it comes to fraud analysis, machine learning is a set of artificial intelligence (AI) algorithms that can analyze huge amounts of data and suggest risk rules based on patterns that they identify.
Merchants can then deploy the rules to allow or block specific activity, such as fraudulent transactions, suspicious logins, and identity theft.
Since it's easier for machines to process large amounts of data than it is for humans, the system can more quickly identify suspicious behaviors and patterns that would likely take employees months to detect.
In addition, machines take much less time to analyze all the transaction data that continually flows into merchants' systems. And machine learning is better able than humans to assess customer behavior in real-time, analyzing typical activities and blocking or flagging anomalies for review.
Gathering and processing data from user activity and payment transactions gives merchants better insight into fraud attempts. And machine learning and artificial intelligence enable these online retailers to take corrective actions when threats are detected.
The benefits of fraud analytics
Online businesses that implement fraud detection analytics can expect to realize several benefits, including
Fraud prevention services that employ machine learning and artificial intelligence enable online retailers to save hundreds of hours of research that their employees would have to do manually during a fraud investigation.
More sales with good users
Using the data gathered by a fraud detection system can help merchants better understand their customers, including how often they shop, the average value of the items in their shopping carts, and what types of items they purchase. That means online retailers can send promotions to regular customers, encouraging them to spend more than they usually would by offering higher-value items they may be interested in. With a fraud detection solution, merchants can accurately make these decisions. Additionally, the risk that these good orders will be flagged as fraudulent is low.
Boost authorization rates
An authorization rate is the percentage of transactions online retailers submit that are reviewed and approved by the cardholder's bank. Issuing banks are not too keen on merchants that process a high number of unauthorized transactions. Consequently, the issuing banks will consider those merchants high risk and decline their authorization requests when they're submitted. However, online businesses that deploy proper risk management and control fraud can boost their authorization rates and earn more revenue.
Enhance customer satisfaction
Fraud can quickly damage merchants' relationships with their customers, causing those customers to lose trust in them. For example, if a bad actor compromises customer accounts and makes off with their loyalty points, those customers will likely blame the merchants for not adequately securing their accounts. In addition, those customers might decide not to purchase from those businesses again. However, companies implementing fraud detection and prevention solutions can stop cyber criminals from breaching their systems and compromising customer accounts, ensuring a better customer experience.
Fraud analytics can help merchants prevent costly chargebacks. Chargebacks occur when legitimate customers contact their card issuers to refund their purchases, saying they don't recognize the charges or didn't make them. This may happen because fraudsters stole their card details and made unauthorized charges.
In many cases, though, those legitimate customers did make the purchases and are trying to scam the merchants—this is sometimes called friendly fraud (or chargeback fraud). Chargebacks allow those customers to keep their purchases without paying for them, meaning merchants lose out on the revenue from those purchases and may even experience lower authorization rates and have to enroll in a monitoring program. But fraudulent or not, chargebacks hurt merchants' bottom lines.
By preventing fraud, online retailers can stop the resulting chargebacks, friendly or otherwise. Fraud analytics can detect anomalies in transactions that could indicate that fraudsters are trying to make unauthorized transactions. Analytics also help online businesses determine if their legitimate customers are trying to engage in friendly fraud. That's because fraud analytics can detect anomalies in customers' orders that might suggest a risk of friendly fraud—for example, if customers' order amounts are larger than usual or if customers are making more frequent purchases.
Read more: How to prevent chargebacks
How Checkout.com’s fraud analytics work
According to Checkout.com's State of Retail report, 25% of ecommerce companies worldwide are experiencing a significant increase in fraud and chargebacks. And, as economic conditions tighten, that number is likely to grow.
Checkout.com's Fraud Detection Pro's machine learning engine studies billions of transactions, enabling merchants to benefit from Checkout.com's global network alongside a set of sophisticated risk tools to block fraud and identify legitimate customers. In 2022, Checkout.com's fraud detection solution saved merchants over $1.95 billion in potential losses due to fraud.
Checkout.com's Fraud Detection Pro is a state-of-the-art fraud detection solution that uses machine learning and advanced rules to monitor transactions for suspicious activity. As such, the more online retailers use the solution, the better it will identify fraud.
With Fraud Detection Pro, Checkout.com gives merchants the necessary features to combat fraud effectively, improve their authorization rates, and make it easier for legitimate customers to do business with them.
Checkout.com's Fraud Detection Pro uses advanced machine learning to detect new fraudulent trends based on data analysis across the entire Checkout.com network. The tool's machine learning feature is trained on billions of hard and soft data points from Checkout.com’s global network of merchants. And it also learns from patterns of real fraud across multiple industries and countries, applying these insights to detect and stop suspicious activity at the points of the transactions. Merchants would otherwise be more exposed to fraud because they don't have broader insights and historical data about existing and emerging fraud patterns.
Set and customize rules
Using Checkout.com's data and specific rules, Fraud Detection Pro is fully customizable. It can easily adjust to new threats, so merchants have one less thing to worry about. Checkout.com's solutions allow merchants to completely control their customer journeys to deploy either rules-based or dynamic machine-learning authentication strategies.
Risk analytics dashboard
Fraud Detection Pro’s analytics dashboard offers merchants a single source for monitoring and analyzing all their payments. They can get their key performance indicators, payment history, details, and analytics — all in one place.
The dashboard helps merchants monitor the effectiveness of their risk strategies and identify areas they can optimize to operate more efficiently. These include:
- Total fraud.
- Fraud rate split by card scheme (Visa, Mastercard).
- Declines (pre-authentication and post-authentication).
- Payment lifecycle overview (Sankey diagram).
- Granular fraud report (customizable by date range).
- Decline rule performance (shows the impact of each rule that can decline a transaction, including trendlines to show how often each rule is triggered on different days).
- Risk assessment timeline at a payment level via the dashboard, including ML score breakdown and detail on the contributing factors.
Learn more about Fraud Detection Pro or get in touch with us to find out more about how it can help your business.