Organizations can use fraud detection and prevention tools to detect and prevent fraudulent transactions online. These tools allow for continuous monitoring of multiple metrics, such as financial transactions and user behavior, orders, and many other factors. These tools can calculate risk scores and analyze them to identify suspicious or fraudulent activities, such as bot clicks, illicit purchase orders, and so on.
Ecommerce fraud has seen a significant rise worldwide due to rapid growth in online sales since the introduction of COVID-19. Cybercriminals carefully target merchants most at risk of falling prey to these frauds. Market newcomers and those who are not familiar with the details of the eCommerce marketplace, as well as enterprises without adequate security measures to prevent and combat fraudulent activity, are all affected.
The 2021 Online Payment Fraud Report by Juniper Research shows that eCommerce retailers will lose approximately $20 billion through fraud in 2021, compared to $17.5 million in 2020. This is an 18% increase. Although fraudsters may have enjoyed a free hand since the outbreak of the pandemic began, merchants can be equipped with the right tools to combat them. These tools help to prevent fraudsters from eCommerce and protect online merchants against large revenue losses.
Let’s take a look at the key features enterprises should be looking for when evaluating e-commerce fraud detection tools and prevention tools.
1. Built-in machine learning models (ML): Anti-fraud tools must include machine learning models (ML), which can detect fraudulent activity instantly when real-time insights are fed into them. These tools are more effective than manual reviews because machine learning allows for greater agility and coverage when detecting frauds online.
2. Automation of workflow: Automation is a key component in speeding up a business’s workflow. Automating online payment fraud checks can help businesses protect themselves by detecting and blocking any suspicious behavior or devices and canceling fraudulent orders.
3. Interface for real-time insights: A single dashboard can provide real-time insight and reports to help speed up the fraud screening process. This interface gives you better visibility into possible frauds and allows you to monitor these activities without switching between screens.
4. Chargeback feature: Make sure you choose an anti-fraud program that provides a chargeback guarantee. The tool should cover all orders that are affected by an attack by online fraud if a business is being victim to it.
5. Device identification: Fraud detection tools and prevention tools need to analyze various properties, including the browser and operating system as well as the language and location of the device used to access an enterprise’s eCommerce store.
6. Customization: This allows tools to detect and block fraud activities right at the root. The device’s fingerprint also hints at fraudulent activity. This feature allows businesses to instantly identify fraud devices and block them from being used.
7. Platform support: E-commerce fraud prevention tools must support certain platforms or interfaces (APIs), which make it easier for online shops to detect and identify fraud. It is therefore important that businesses understand which platforms are supported and choose the appropriate tool.
Fraud Detection Machine Learning Techniques
Machine learning and artificial intelligence will play a significant role in fraud detection in 2021 and beyond. The rules that programmers create are the basis of traditional rule-based eCommerce fraud detection methods. They are limited in their operation scope and cannot adapt to new fraud patterns. ML-based eCommerce Fraud Solutions can learn and improve over time by the inflows new information.
There are two main types of ML algorithms: unsupervised and supervised. Both algorithms can be used to detect and prevent fraud and are superior to traditional eCommerce fraud prevention methods. ML-based detection systems scan transactions and produce an evaluation score of 0-1. The evaluation score is then compared to a predefined threshold in order to determine if the transaction was fraudulent.
Let’s examine the nature and applications of some of these algorithms. Let’s start with the supervised algorithms
1. Supervised decision tree
Initial data is fed to the supervised module. It includes information on normal and fraudulent transactions. The supervised decision tree then performs a classification or prediction. After that, the tree is divided into root and child nodes.
The root node is where fraudulence score computation starts. As the tree splits into different nodes, Depending on the input variable value, each node is then further divided into child nodes.
After the tree has been constructed, a new input (transaction), is classified by going through each root of the tree starting at the root node. This classification is based on the feature value of the input.
2. Supervised support vector machine (SVM)
Support vector machine algorithms provide analysis of data for classification and regression analysis. SVM is used to classify input data. This is done in an n-dimensional space. Each input feature’s value is equal to the specified coordinate. The ideal hyperplane then determines which class is distinguished.
The main purpose of an SVM is to draw a line among classes to allow for as much margin between legitimate and fraudulent transactions. This will allow for high detection. Email phishing detection can be done using SVM, Naive Bayes, and Extreme Learning Machine. These models can be used to give a ‘yes or ‘no answer’ for suspicious transactions.
Let’s now understand unsupervised algorithms.
1. Anomaly detection: Autoencoder
When a customer has few fraudulent transactions, autoencoders can be used. The fraudulent samples are removed from the model training step. They can still be used for testing. All anomaly detection methods denote unanticipated events in data.
The neural autoencoder is a framework that can be trained to recognize one type of event and then used to notify others. The input and output units are equal, with some hidden layers between them. Based on the threshold value and distance between the input layer and its reproduced output layer, the transaction will be deemed fraudulent or not.
2. Outlier detection: Isolation forest
The outlier techniques class includes isolation forest. This technique can also be used to deal with cases in which there are few or no fraudulent transactions within a dataset. The principle of isolation forest is that an outlier can be identified by using fewer random splits (called the mean length) than data points belonging to the normal classes. Outliers are more common than normal samples, and they have values that are unusual for the average data set.
The algorithm selects a random value range from a feature and creates a split value. A tree is then created from this selection. To measure tree depth, the number of random splits required is used. The mean length number of such trees is calculated over all trees to determine normality and may also be used for tracing outliers. Outlier cases are found to have a shorter tree depth than normal data samples. This allows you to identify outliers.
Unsupervised machine learning algorithms like local outlier factor (LOF), one-class SVM, isolation forest, and principal component analysis (PCA) are useful for this anomaly detection challenge that helps detect abnormal patterns and suspicious actions by users.
These algorithms are most useful in the identity theft detection models. Credit card fraud detection can be done using both supervised and unsupervised algorithms.
Here are the Top 5 Ecommerce Fraud Prevention and Detection Tools
Merchants should use a combination of e-commerce fraud prevention best practices and tools to prevent fraud in all areas. There are many tools available, including fraud platforms, payment solutions gateways, and chargeback guarantee services. These are the top fraud prevention tools merchants can use to combat fraud.
Overview: Simility is a cloud-based fraud detection tool that analyzes millions of transactions daily and flags suspicious ones.
Features: Simility features include:
- Risk assessment: Simility can analyze fingerprints to determine the card fraud risk from mobile and desktop devices.
- Device identification: This tool examines many parameters of the device, including browser, OS, and language. It is used to detect fraud. Even if the device is brand new or has been blacklisted by the network, it is still effective.
- In-built ML Models: These ML models help to determine the likelihood of fraud associated with the device.
- ML Dashboard: Simility provides a single-pane view that allows customers and teams to answer questions
- about the underlying ML models.
- Updates: The tool can identify whether the updated and old fingerprints belong to the exact same device even if the device’s features are changed, such as the browser or OS.
- Platform Support: Any platform can access the API. Access to the tool is not possible via apps.
- No charge plan
- Free Trial: Available
Overview: Subuno can be customized to meet your fraud prevention needs.
Features: Subuno’s features include:
- Automation: The Subuno fraud protection tool provides 20+ fraud detection features that help automate your fraud checks and give a complete overview of your order reviews.
- Customer details: This tool allows customers to provide details about their location, contact details, and when they last used the email address.
- Interface to provide real-time insight: Each order is displayed on an interface that has a colored pattern. This can be used as verification.
- Built-in ML models: These advanced algorithms can analyze over 100 variables to detect fraudulent orders.
- Platform Support: These platforms include Magento and Shopify as well as Shopify and ZenCart.
- Free plan: Not available
- Get a free trial for 30 days
Overview: Riskified facilitates frictionless fraud management by identifying the individuals behind online interactions.
Features: Riskified features are listed below.
- In-built ML models: Riskified uses advanced ML algorithms for real-time insights into an organization.
- Device Identification: This tool uses a variety of analytic methods, including proxy detections, geolocation detections, and IP detections. Order linking, device fingerprinting, chargeback, and order linking are all possible.
- Riskified Dashboard: Riskified’s dashboard shows the order review time and tracks Riskified’s performance.
- Authentication: Riskified doesn’t provide scores or risk flags for every transaction, but rather gives an answer of ‘decline/approve’ for each one.
- Control: The authentication feature gives control in the organization’s hands to decide which transactions to review and approve to generate further sales.
- Platform Support: Magento and Shopify are supported platforms.
- Free plan: Not available
Overview: signifyd uses AL/ML algorithms to provide actionable commerce insight to merchants.
Features: Here are Signifyd’s features:
- Interface: signifyd offers a console that allows you to access all transaction orders and reports.
- Score Each transaction receives a score to indicate its order quality.
- Automation: This tool allows back-office automation for orders that are being fulfilled or canceled.
- Signifyd offers the possibility to either manage fraud in-house or to assign management to Signifyd’s team.
- Chargeback: In the case of a fraud chargeback insurance, payouts can be made in as little as two days.
- API Integration: Signifyd allows you to connect with any eCommerce platform’s API or platform without the need to modify it.
- Platform Support: Shopify and Magento are supported platforms.
- Free plan: Available
- Free trial: Available for 14 days
Overview: Forter provides comprehensive fraud protection as it accepts transactions from all countries, stores, and with a variety of payment methods.
Features: Forter features include:
- Automation is an automated tool that responds to every transaction in a split second.
- Customization: this tool can customize its ML model for the specific risk profile based on the requirement.
- Interface: This tool provides a single dashboard that gives real-time insight into customer behavior and geolocation, as well as details about each transaction.
- Target transactions: Forter was specifically created to target mobile transactions. It also offers fraud protection to protect phone orders.
- Payment Support: It supports a variety of payment media like PayPal and Google Wallet.
- Platform Support: Supported Platforms include Magento.
- No-cost plan
- Free trial: not available
A digital footprint is essential for all organizations, especially those that are eCommerce businesses. Organizations need to be aware that their digital footprint can make them more vulnerable to internal and external fraud. It is, therefore, crucial to develop a strategy to prevent such criminal acts.
Enterprises have the option to use a combination of anti-fraud tools as well as best practices to detect fraud and stop it from reaching its root. Enterprises can use fraud prevention tools to identify and block suspicious activity in the early stages. Businesses can also take full control of their operations, and protect their customers’ sensitive data as well as their own.