Essential Tools and Technologies for First-Party Fraud and Chargeback Protection

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Whether you’re an online bank, a credit card issuer, or a telco financing costly devices, first-party fraud is a threat that’s difficult to detect. It can be opportunistic, executed by a single fraudster, or organized by criminal gangs and fraud rings. Friendly fraud, like address fronting or claiming a defective product, is even harder to identify because it doesn’t look fraudulent. Luckily, predictive analytics can help.


First-party fraud, committed by a customer using their account, is an increasingly costly post-transaction fraud threat that businesses must be prepared to face. Aside from wreaking financial havoc on companies, it can hurt customer confidence and damage brand reputation.

To prevent first-party fraud, companies should use fraud detection software that combines multiple layers of verification, including digital identity trust signals such as IP addresses, device IDs, and location data, with supervised and unsupervised machine learning. This can help identify phishing attempts, account takeovers, and more.

AI can also help prevent friendly fraud, in which customers initiate disputes over transactions they recognize on their bank statements. Detecting this fraud in real-time can help reduce the impact of these costly chargebacks and fees on business profitability. Lastly, companies can use analytics software to systematically scan different data sources, uncovering links between people, identities, and places that may indicate connected fraud and money laundering campaigns.

Behavioral Detection

Fraudsters’ goal is to file as many chargebacks as possible, resulting in significant revenue loss for eCommerce platforms. This is why a comprehensive fraud solution that allows for more authentic transactions is so important.

Behavioral detection is an essential tool for first party fraud and chargeback protection because it detects patterns that are not typical. For example, when a consumer constantly purchases large amounts of an item or repeatedly buys things in small quantities, it may indicate a skewed pattern. This can also be a sign of friendly fraud, wherein the customer files a chargeback because they didn’t receive an item or it wasn’t as advertised.

A holistic platform will identify these suspicious behaviors without needing historic labels or rules, reducing the time and resources required for fraud prevention. This helps lower the cost of chargeback fees and keeps eCommerce transactions moving smoothly for customers.

Behavioral Analytics

Behavioral analytics tools are designed to detect anomalous behavior by correlating data and identifying relationships. They can help detect cyberattacks and malicious activity against users, devices, networks, and applications.

As people conduct more of their financial activities online, bad actors have recognized new opportunities for fraud. This includes fronting – using someone else’s identity to save money on car insurance or bank accounts. It also provides chargeback fraud or friendly fraud – customers buying products they know they’ll return to get a refund from their credit or debit card company.

Behavioral analytics tools can identify these new threats by looking at patterns of account activity over time. This includes the transaction type, the frequency, and where the account is being used. Many solutions use a combination of rules and machine learning models to catch new fraud patterns that simple rule engines may not discover. This approach reduces the number of false positives that the system would otherwise generate.

Machine Learning

The best way to protect yourself from first-party fraud is to prevent it. But that’s only sometimes feasible with today’s fraudsters, especially if your customers are large spenders or big-ticket purchases are made in digital goods.

As such, merchants must implement preemptive measures like criminal fraud management and fight back with representations. And that’s where machine learning comes in.

Fraudsters use sophisticated schemes to misrepresent their identity for financial or material gain. This can be opportunistic, such as when a fraudster snags someone’s personal information to steal their credit card or bank account. Or it can be planned and organized, such as when criminal gangs target out-of-country students to buy their identity credentials and cash in on their return home. Either way, a lack of contextual data can lead to false positives that increase a merchant’s chargeback rates and reduce profitability. That’s why solutions like dCube’s machine learning capabilities are key to preventing fraud and minimizing risk for your business.

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