Payment Fraud Detection & Prevention: Full Guide

As real-time payments grow globally, payment gateways and service providers (PSPs) face increasing pressure to outpace fraudsters. In 2024, scam-related fraud surged by 56%, now accounting for 23% of fraudulent transactions. With eCommerce fraud expected to rise from $44.3 billion in 2024 to $107 billion by 2029, businesses must adopt intelligent, adaptive strategies to balance security and user experience.

In this article, you’ll learn how to detect and prevent payment fraud across the entire customer journey. We’ll break down key techniques, such as digital footprint checks, AI, device intelligence and behavioral analytics, and explain how to build an adaptable fraud prevention approach that reduces false positives and scales as your business grows.

What Is Payment Fraud?

Payment fraud refers to unauthorized transactions made using stolen, fabricated or otherwise illegitimate payment credentials. It occurs when cybercriminals exploit security gaps to manipulate or hijack digital payments, often for financial gain.

For example, a fraudster might purchase stolen credit card details from the dark web and use them to buy high-value electronics online, leaving both the business and the cardholder to deal with the fallout. These attacks can involve various payment methods, including credit cards, virtual wallets, direct debits and mobile payments.

The damage goes beyond money. Victims often suffer from reduced credit scores, while businesses face chargebacks, financial losses and damaged customer trust. Fraudsters use tactics like phishing emails, malware infections and social engineering to access sensitive information.That’s why businesses need more than firewalls. Payment transaction monitoring, combined with encryption, tokenization and multi-factor authentication, is essential to detect suspicious activity before it leads to losses.

How Does Payment Fraud Affect Businesses?

Payment fraud has several negative effects on businesses:

• Financial impact: Companies face the financial burden of fraudulent transactions, including chargebacks and lost goods, which can significantly affect their profitability.
• Higher operational costs: Businesses must invest in advanced security systems, fraud detection technologies, and employee training to combat fraud, leading to higher operational expenses.
• Damage to reputation: Frequent fraud incidents can undermine customer trust, leading to a decline in customer loyalty and a potential long-term revenue loss.
• Operational challenges: Addressing and investigating fraud cases diverts resources and focus away from core business activities, disrupting daily operations and reducing overall efficiency.
• Regulatory risks: Non-compliance with security regulations can lead to substantial fines and legal consequences, further straining a business’s finances and operations.

Types of Payment Fraud

1. Card testing: This is when fraudsters use stolen credit card information to make small online transactions to verify if the card details are valid and active. Having confirmed that a card is functional, fraudsters use it for more significant fraudulent transactions or sell it as validated information to other criminals. This form of fraud creates unauthorized charges for the cardholder, as well as chargebacks and processing fees for a business.
2. Credit card fraud: From the physical theft of card information to using a card’s skimmed details from an illegal device, stolen card fraud involves the unauthorized use of credit or debit card information. Fraudsters use card details to make unauthorized transactions or cash withdrawals, leading to financial loss for legitimate cardholders and the financial institutions involved.
3. Chargeback fraud: Frequently referred to as friendly fraud, chargeback fraud occurs when a cardholder makes an online purchase and then requests a chargeback from the issuing bank after receiving purchased goods or services. Legitimate chargebacks are meant for unauthorized use or defective products. Chargeback fraud is committed when the request is made despite there being no actual issue with the transaction.
4. Refund fraud: Another common type of fraud, refund fraud, occurs when money is illicitly obtained from a business through deceptive means. In other words, when an individual makes a purchase (either legitimately or using fraudulent means), then manipulates or deceives a merchant’s return policy to gain a refund or credit they are not entitled to, resulting in a financial loss for the business.
5. BIN attacks: A brute force type of attack in which fraudsters use the first six digits on a credit card to algorithmically try to guess the other legitimate numbers in an attempt to generate a usable card number. Once a valid card number is obtained, fraudsters use it to make unauthorized transactions or create counterfeit cards.
6. Gift card fraud: Gift card fraud involves using stolen or fake gift card details to make unauthorized purchases, often through scams or account takeovers. Fraudsters exploit the anonymity and ease of using gift cards for illicit transactions, including chargebacks, pay-with-gift-card scams, and generating or stealing card numbers for resale or laundering.
7. Authorized Push Payment (APP) Fraud: Refers to fraudulent activity where victims are coerced into executing real-time payments to fraudsters, often through social engineering tactics, including impersonation. These authorized fraudulent schemes can encompass investment scams, where victims are deceived into transferring funds for fictitious investments, as well as romance scams, where the fraudster tricks the victim into believing they are in a romantic relationship.

Payment Fraud Detection & Prevention

Payment fraud can appear at any stage of the payment journey, which is why most organisations rely on a mix of detection and prevention rather than a single control. Detection highlights unusual behaviour in real time, while prevention reduces opportunities for fraud before a transaction is made. Together, they create a stronger defence without adding unnecessary friction.

To achieve this, businesses combine different analytical methods to understand user behaviour, payment context and the credibility of submitted details. When layered effectively, these signals form a more accurate and adaptable fraud strategy.

Below is an overview of the methods commonly used to identify and reduce payment fraud.

Digital footprint analysis

A simple way to gauge user credibility is to assess the quality of the information they provide. Email addresses, phone numbers and online profiles all leave traces. Weak or throwaway details — or none at all — can suggest a higher likelihood of fraud.

IP and network signals

Examining IP addresses, network routes and location consistency helps uncover mismatches between a user’s apparent origin and the details attached to their payment. Sudden geography changes or signs of anonymisation tools may warrant closer attention.

Device-level insights

Most users behave consistently across devices. Tracking device types, browser characteristics and configuration patterns helps detect emulators, spoofed setups or multiple accounts tied to the same environment.

Card and issuer information

Checking the Bank Identification Number (BIN) helps confirm card issuer details, region and type. Inconsistencies here often appear alongside card-not-present fraud or testing activity.

Real-time transaction observation

Monitoring transactions as they happen allows businesses to compare live behaviour with what is typical for a given customer or segment. Unusual spending velocity, abrupt pattern shifts or high-risk combinations of signals can prompt additional verification.

As Husnain Bajwa, SVP of Product for Risk Solutions, notes: “Every leap in payments innovation comes with a trade-off; the faster money moves, the less time there is to stop it from moving in the wrong direction.” It’s a reminder that faster payments leave less time to intervene. Learn more in our piece on real-time payments risk.

Machine-learning indicators

Machine learning complements rule-based systems by spotting emerging behaviours and subtle patterns that humans may overlook. These models evolve as new data becomes available, helping to highlight risk that may not fit traditional profiles.

AML and screening controls

For businesses handling regulated payments, screening users and transactions against sanctions lists and monitoring for unusual financial movement adds another layer of protection.

Behavioural analysis

How users navigate, type and interact with a platform provides continuous behavioural context. Bots, account takeovers and synthetic identities often leave behind interaction patterns that differ from genuine user behaviour.

Reverse email & phone lookups

Enrichment tools like reverse email lookup or reverse phone lookup help validate whether a contact detail belongs to a real person with an active online presence. These checks add contextual clarity and improve risk assessments where primary customer information is limited.

Adaptive rule sets

Configurable rules allow businesses to account for the nuances of their industry, traffic patterns and fraud exposure. Rules can evolve over time as new tactics appear or user behaviour changes.

Case review and investigation tools

When payments require human assessment, analysts benefit from consolidated views of user histories, signals and past decisions. Clear context helps reduce false positives and maintain consistent outcomes.

By combining these approaches, organisations gain a more detailed understanding of payment behaviour, enabling them to spot irregularities earlier and refine their defences as threats evolve.

Key Account-Based Intersections Targeted by Fraudsters

Fraudsters typically strike at predictable stages in the user journey, where sensitive data or account control is most vulnerable. Recognizing these high-risk moments helps businesses apply targeted defenses without disrupting the user experience.

• User Signups: At account creation, attackers may use fake or stolen information to set up fraudulent profiles. By analyzing digital signals like email, phone and IP data, fraud detection tools can assess whether the user appears genuine, stopping many threats before they gain access.
• User Logins: Login events are a key point for account takeover (ATO). Monitoring behavior, IP consistency and device patterns helps flag suspicious activity and enables timely intervention.
• User Transactions: At checkout, fraudsters may use stolen card data or manipulate payment flows. Real-time monitoring and risk scoring evaluate each transaction against known patterns, identifying anomalies such as mismatched details or sudden spending spikes.

Together, these touchpoints offer critical opportunities to detect fraud early. AI and machine learning enhance this process by spotting subtle patterns humans can miss, enabling faster, more accurate decisions at scale.

Choosing a Payment Fraud Prevention Solution

In today’s high-speed digital economy, payment fraud is a constant threat that demands a twofold resIn In the fast-moving digital world, businesses must tackle payment fraud with both real-time detection and proactive prevention. Effective fraud protection means addressing threats at every stage of the customer journey, ensuring fraud is caught early and stopped before it can do damage.

A successful strategy combines detection and prevention, offering businesses a comprehensive view of risks while maintaining a smooth user experience. Solutions with features like digital footprint analysis, device intelligence, and customizable rules can help enhance detection accuracy and reduce fraud.

Key aspects to consider when choosing a solution include:

• Easy integration with existing systems
• Transparency in fraud risk scoring
• Adaptive approaches to user verification based on real-time risk

By selecting a solution that integrates these elements, businesses can improve fraud detection and prevention while minimizing disruption to legitimate customers.

Frequently Asked Questions

You might also be interested in reading about:

• SEON: Payment Gateway Fraud: Detection & Solutions
• SEON: Guide to Transaction Monitoring Software | Tools & Tips
• SEON: 7 Types of Ecommerce Fraud and How to Protect Against Them