How to Identify and Prevent Chargeback Fraud

Transaction errors often take place with the rise of digital marketplaces. Particularly big ones can be quite an issue for organizations. By allowing customers to make a chargeback claim, banks and credit card firms safeguard their clients when these mistakes happen.

Some consumers, however, take advantage of this system with different fraudulent chargeback types. Chargeback fraud is what's happening here, and as more people use their credit cards for online purchases, it's becoming a major issue for online vendors.

In this article, we will discuss different types of chargeback fraud as well as ways to detect them. You will also learn about effective methods to prevent chargeback fraud using cutting-edge AI-driven technologies.

Understanding Chargeback Fraud

A typical chargeback fraud happens when someone purposefully uses a credit card to make a transaction and then disputes the transaction with the credit card issuer. Generally, users make it on purpose to commit a so-called chargeback abuse. They are lying either about not approving a payment or about not obtaining a good or service, it is a type of fraud.

The key challenge here is to decide if the company is dealing with a legitimate customer or a fraudster. Data enrichment is necessary. Scammers develop their techniques and come up with more advanced approaches to perform fraudulent transactions.

So, it is extremely important to define different types of chargeback fraud. Besides, not all chargeback disputes are illegal. Sometimes they happen due to technical errors and other issues that have nothing in common with online criminals.

Types of Chargeback Fraud

There are three broad types of chargebacks. The type of chargeback can influence how strongly or weakly it is associated with fraud and how much the customer thinks it is justified.

To lessen the effects of chargeback fraud, organizations must have effective chargeback fraud prevention tools that automatically improve the overall decision-making for businesses. So, here are the three main chargeback fraud types.

Chargebacks from Criminal Fraud

This type of fraud is generally associated with stolen credentials. A fraudulent actor gains access to a credit card and uses it to make illegal purchases. This can involve taking possession of the actual card, copying its data to make a phony card, or breaking into an account that has the card's data on file. A criminal uses the same card to request a chargeback.

Chargebacks from Friendly Fraud

A friendly fraud happens when a client files a chargeback claim without any reason. Sometimes, it is nothing but an accident. However, if a user makes it on purpose, these actions are associated with chargeback fraud.

‍#### Chargebacks from Merchant Error

‍Sometimes errors in the refund or purchase procedures result in chargebacks due to merchant issues. For instance:

• users pay more than what their purchase is worth;
• an incorrect sum was charged due to an error made by the merchant;
• a client did not receive an order that was supposed to be delivered;
• a customer received an order that was defective or entirely different from what he or she ordered.

These chargebacks are typically valid. They do not result from a third party's malicious intent. This is why they are not generally associated with chargeback fraud. Rather, they usually take place due to the merchant's poor business practices (or, in rare instances, fraud).

‍#### Chargeback Fraud vs. Friendly Fraud

A particular kind of chargeback fraud known as "friendly fraud" occurs when the actual cardholder knowingly seeks an unauthorized chargeback refund. Although cardholders may inadvertently commit this, friendly fraud occurs when they do it on purpose.

Friendly fraud takes place only when the cardholder engages in any misuse of a chargeback process. To proceed, a criminal steals credit card credentials and other sensitive data to request a fraudulent refund.

As a result, companies lose revenues and customer loyalty. The impact can be disastrous. This is why proper chargeback fraud prevention solutions are essential. Otherwise, online merchants can face serious problems.

The Impacts of Chargeback Scams on Companies

The latest stats show that 90% of merchants thought acknowledged being affected by chargeback fraud. However, companies that want to remain at the forefront of the battle against financial crime must practice good fraud risk management. If not, the impact can be tremendous.

The Direct Costs of Chargeback Fraud

Chargebacks can result in substantial expenses for the firm, including lost income and chargeback fines of up to $50 (fines can be even bigger). Beyond this, some businesses would refuse from doing business with a blacklist merchant who receives an excessive number of chargebacks, which would further hurt profitability.

The Reputational Risks of Chargeback Scams

As stated earlier, the more chargebacks a merchant receives, the higher the chances to get into the blacklist. On the one hand, businesses can lose strategic partners. On the other hand, it will decrease customer loyalty while the majority of clients will leave for a more trusted and time-tested vendor.

Higher Operational Costs

Fraudulent chargebacks can encourage other criminal conduct, such as money laundering and similar financial crimes, in addition to the direct costs to businesses. Consequently, this adds to the growing compliance issues that businesses worldwide are dealing with. So, with every new chargeback fraud, operational costs keep growing.

Juicyscore’s Solution Against Fraudulent Chargebacks

Chargeback fraud prevention strategies are the essential component of a more comprehensive, well-built risk management system. These consist of appropriate customer onboarding and documentation, particularly customer screening through KYC procedures. It lets businesses know who they are doing business with.

However, fraudsters have enough room to develop their scamming approaches. This is why having a reliable transaction monitoring system is essential. Since a large number of chargeback fraud is repeatable, machine learning and artificial intelligence can identify hidden patterns and trends.

JuicyScore is an ultimate all-in-one fraud protection solution. Driven by advanced AI and machine learning technologies, it ensures a full-range anti-fraud coverage across different business verticals.

Leveraging AI to Detect Chargeback Fraud

JuicyScore has advanced artificial intelligence and machine learning technology at its core. It is a comprehensive suite of fraud prevention tools made to help businesses prevent all types of illegal chargeback manipulations:

• Modern AI-Powered Algorithms. The software uses hundreds of different device authentication characteristics to create an accurate end-user profile based on a variety of tech data and behavioral aspects.
• ML-Powered Techniques to Lower Fraud Risk. Instant risk identification happens automatically. To compute technical data, the system leverages hundreds of user device's settings, characteristics, and indicators. They support network infrastructure surveillance, analysis, and assessment.

Comprehensive Data Analysis

Using the JuicyScore data vector, we build an adaptable scoring system. It facilitates the collection of sufficient data to evaluate customer accounts, operating system infrastructure, connectivity between Android and iOS devices, etc. It helps businesses elevate the effectiveness of decision-making is its main objective.

The program serves as a digital safety guide. It is a part of a more comprehensive technological stack for preventing fraud. The system comes with integrated algorithms to find randomization and device manipulation while the scoring model-based system produces values.
Broad tracking is automatically achieved by our system through the usage of authentication procedures. Fraudulent behaviors such as numerous accounts, data randomization, device virtualization, and credential theft can be promptly detected and blocked.

Key Features

JuicyScore functions as an electronic fraud aide. It automatically evaluates integrated parameters and indices. By utilizing many data sources, technology adoption lowers the chance of fraud. In addition to automatically evaluating any potential cyber threat, the software generates in-depth technical and behavioral fingerprints needed to create an extensive digital profile.

The solution fully complies with the AML, CCPA, and GDPR. Our team constantly works on process automation referred to as data retention policies. We conduct routine integrated compliance assessments. It saves time, lowers the risk of fraud, and ensures that your business verification processes don't conflict with existing regulations.

Device Fingerprinting

The system tracks numerous primary and secondary parameters that may be associated with possibly fraudulent activity. JuicyScore can monitor key device-assisted data for optimal device fingerprinting.

Our data vector includes essential features like RAM, screen size, display quality, kind of carrier (tablet, desktop, laptop, or mobile), and more that allow accurate fingerprinting.

Behavior Patterns Analysis

Online merchants receive alerts generated by the system in case of any anomaly that can be a sign of a chargeback fraud claim. Teams can learn about different routing techniques, remote access, device cloning, randomization, etc. It helps them quickly detect and prevent all kinds of suspicious actions based on behavioral patterns.

How It Works

JuicyScore classifies high-risk flows into niche-specific groupings using filters based on device features (OS, browser version, Internet connection quality, behavioral variables, etc.).

Network Monitoring

The system assesses the behavioral and technological datasets. It leverages AI and ML technologies to constantly improve its fraud detection algorithms. Various aggregated criteria are processed, such as dwell/flight times, average typing or content reading rates, devices used for prolonged periods on the same source, duplicated or randomly picked devices, and other notable behavioral data.

Risk-Based Authentication

The system instantly detects potentially stolen credentials. Using 214 settings, properties, and device-assisted indicators, it creates and computes technical and behavioral metrics. They address the quality of the Internet connection, the network infrastructure, and other elements that are thoroughly processed and assessed.

Automated Chargeback Responses

We can create the most comprehensive chargeback proof in the world, specifically suited to your store. The software transmits chargeback evidence by leveraging Big Data along with powerful and seamless integration with your company.

Finding possible abnormalities that aren't related to a certain user reveals different routing techniques, remote access, device cloning or randomization, and more. Businesses will be able to identify scammers early on.

Get Started with JuicyScore Today

Our knowledgeable staff is pleased to help at any point during the JuicyScore implementation procedure. Utilize our cutting-edge anti-fraud solution in just three easy steps:

1. Install the SDK or JavaScript on your platform.
2. Finish basic configurations according to your company's requirements.
3. In just a few seconds, begin gathering and evaluating the data.

Please get in touch with us to arrange a demo.

FAQs

What are some common chargeback fraud schemes?

There are three major types of chargeback fraud. They involve third and first-party frauds depending on techniques used by scammers. Friendly fraud is another common type of chargeback abuse when a client claims a refund with no valid reason.

How can AI and machine learning help detect chargeback fraud?

AI and ML-driven technologies use previous merchants’ experience. They can evaluate processed user data and define if his or her actions are potentially fraudulent. Most of these anti-fraud solutions automatically create a user profile based on collected tech and behavioral information.,

What data points are analyzed to identify potential chargeback fraud?

Major data points include technical and behavioral characteristics. For instance, device-assisted attributes help to identify remote access or device randomization. Behavioral patterns show anomalies in typical user behavior, which is also a sign of fraud risk.

What authentication methods help combat chargeback fraud?

A systematic approach is needed to combat chargeback fraud. The all-in-one solution should come with advanced technological features not just to collect but also to process and analyze generated data. This is how JuicyScore works with a broadly automated data vector at its core.