Each year, around four percent of the global GDP is lost to fraud, with digital payment fraud up a reported 25% over the last three years. Payment gateways and payment service providers (PSPs) must adopt strategic measures to combat escalating fraud rates, especially as they try to capture larger market shares.
The rapid growth of global real-time payments has added more pressure on gateways and PSPs to block fraud with precision and speed. To ensure safe and sustainable business growth, assess your fraud exposure and implement frictionless solutions for prevention and detection.
What Is Payment Fraud?
Payment fraud involves unauthorized transactions using false or stolen payment information, compromising financial security. This fraud typically occurs when credit card details are stolen through data breaches or obtained from the dark web. Various payment methods, such as virtual checks, direct debits, and phone payments, are particularly susceptible when attackers have the necessary details.
Payment fraud can cause significant financial loss, damage credit scores, and erode trust for consumers and businesses. Cybercriminals exploit payment system vulnerabilities using phishing, malware, and social engineering. Therefore, robust security measures like encryption, tokenization, and multi-factor authentication are crucial. Continuous payment transaction monitoring, real-time fraud detection, and user education on safe online practices are essential to mitigate these risks.
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: To combat fraud, businesses must invest in advanced security systems, fraud detection technologies, and employee training, leading to increased operational expenses.
- Damage to Reputation: Frequent fraud incidents can undermine customer trust, leading to a decline in customer loyalty and potential long-term decreases in sales.
- 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 result in substantial fines and legal repercussions, adding further strain to the business’s financial and operational health.
Types of Payment Fraud
- Account Takeover (ATO): Colloquially known as hacked accounts, ATO is a form of payment fraud in which fraudsters gain access to a victim’s account, typically an online banking, an ecommerce account or a digital wallet. Using stolen or phished credentials to log in, the fraudster changes account details to seize control. Once in, they can make unauthorized transactions, transfer funds, and purchase using stored payment methods.
- 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.
- Stolen 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.
- Money Laundering: This crime involves hiding the origins of illegal funds through three steps: placement (introducing funds into the financial system), layering (concealing the source through transactions), and integration (making the funds appear legitimate). This process makes tracing the money’s criminal origin difficult.
- Identity Theft: The most common type of payment fraud, identity theft, involves the unauthorized use of someone’s PII to conduct fraudulent transactions – making purchases, withdrawing funds or opening new accounts – rendering the victim with financial losses and a damaged credit history.
- 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.
- Bank Identification Number (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.
- 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.
- Triangulation Fraud: Complex scam involving customers, fraudsters, and online stores. Fraudsters list items at low prices, use stolen payment data to buy them, and ship the goods to unsuspecting buyers. Meanwhile, the stolen payment information is retained for further fraud.
- 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.
Explore top solutions to detect and prevent banking fraud effectively. Protect your business with tools designed to safeguard transactions and minimize risks.
Comparison here
Payment Fraud Detection
To detect payment fraud, businesses need to verify if customers are who they claim to be by analyzing their data, behavior, and payment information. The most effective approach uses innovative technologies to monitor real-time transactions, including:
- Device intelligence: Identifies customer hardware and software configurations.
- BIN lookups: Verifies card validity.
- IP lookups: Pinpoints customer location and connection details.
- Reverse email and phone lookups: Confirms user identity and validity.
Fraudsters typically exploit three key account-based intersections: user signups, logins, and transactions.
User Signups
This step is the first time you can assess the traffic that lands on your website or app. At account creation, it is essential to check if the data a customer provides matches the intelligence you have on them. Email, phone and IP lookups can validate a customer’s details at lightning-fast speeds, cross-referencing real-time data from the account holder’s digital history; the more extensive that is, the more likely they are legitimate.
Having access to reliable customer data means you can weed out fraudsters right at the point of sign-up, meaning they have no chance of committing fraud or onboarding into your product or system. For digital wallets, cash apps and cross-border payment providers, frictionless onboarding for new customers is critical to delivering a great customer experience without subjecting them to lengthy identification verification (IDV) know-your-customer (KYC) checks.
User Logins
Each user login grants an opportunity to validate the customer’s identity. Using behavior analysis, IP lookup and device fingerprinting to set up risk rules to spot unusual user activity, you can filter out suspicious login attempts and stop ATO in its tracks.
User Transactions
Users enter credit or debit card information at the checkout and payment stage. At this step, checking payment validity against customer profile data is vital to mitigate the likelihood of unauthorized purchases. Practical and adaptable transaction monitoring includes machine learning that trains with the data you feed into it and enables you to stop these fraudulent transactions from going through. For payment providers offering real-time transactions, this must be delivered in a fast and frictionless manner.
Aggregate data from key steps and leverage a risk-scoring engine to quickly assess fraud likelihood at scale, adapting to your risk tolerance when expanding payment options or entering new markets. Machine learning powers the best solutions, delivering fast, accurate decisions while reducing customer friction and manual review costs. Combining transparent whitebox AI with traditional blackbox models ensures both clarity and speed in managing risks effectively.
Payment Fraud Prevention
Preventing payment fraud is about more than just checking payment details. Fraud detection software – like SEON – can help you examine relevant data to create a comprehensive profile of your customer before and during transactions so you can be confident with whom you are dealing with online. Here’s how:
Digital Footprinting
Digital footprinting grants access to your customers’ online identity and behavioral data in real-time by examining unique digital and social profiles to detect and prevent fraudulent user activities, including looking at:
- IP Analysis: Beyond verifying geolocation, checking if a user is hiding their connection behind a VPN, proxy, or emulator can indicate whether the user has a higher likelihood of being a fraudster.
- Email Analysis: A single data point, like an email address, can reveal important information. With reverse email search functionality, it can be deciphered if an account was created from a suspicious domain, a free or disposable address, or if it has appeared in any prior data breaches, indicating a fraud risk. Our email analysis also checks to see what digital and social media presence is connected to an email, giving you more contextual information to decide if you are dealing with a real customer.
- Phone Analysis: Similarly, a phone number can be readily checked against records to define a user profile. Looking at details such as whether it is a landline or mobile account, if the carrier location is close to the shipping address, or if it’s a disposable phone can serve to triangulate identity, among other markers. Like our email analysis, we check to see what digital and social media profiles or messenger services are connected to a phone number for more informed decision-making.
- Device Fingerprinting: Examining a user’s hardware and software is the best way to understand how users connect to your site. Our device intelligence identifies browsers, add-ons, extensions and tools designed to get around most other anti-fraud solutions. User connections can spot hidden links between accounts, helping you detect bot farms better or connected fraudulent accounts. By investigating hundreds of real-time data points, device fingerprinting enhances your defenses without slowing down your customers.
As a fraud prevention and AML solution, we combine advanced digital footprinting, proprietary device intelligence and a fully customizable machine learning rules engine to ensure real-time fraud prevention.
Find Out More
BIN Analysis
Extract as much information as possible about a card by checking its Bank Identification Number against specific databases. Identify fake prepaid cards, gift cards and fake credit cards to counter payment fraud effectively.
Machine Learning
A whitebox machine learning solution with transparency baked into its reasoning, blackbox AI offers the best of both worlds: speed and transparency.
Anti-Money Laundering (AML)
Designed to identify and prevent the process of making illegally gained proceeds appear legitimate, AML controls check for suspicious activities, monitor transactions and conduct due diligence on customers to ensure compliance with global AML regulations.
Choosing a Payment Fraud Prevention Solution
Using SEON’s comprehensive fraud prevention tools gives you the technology to stop fraud effectively and access to experts who help defend against attacks targeting payment providers and gateways. While point solutions may seem simple or cost-effective, they fail to protect your business from increasingly sophisticated fraud. An ideal solution is end-to-end, scaling with your business to prevent fraud at every stage.
SEON combines advanced digital footprinting, proprietary device intelligence, and a customizable rules engine powered by black- and whitebox machine learning. This API-first solution delivers real-time fraud prevention, offering precise insights and robust customer protection to safeguard revenue on a global scale.
API Integration
Many businesses start with basic fraud tools, adding advanced features as they grow. SEON’s end-to-end API-first approach enables flexible, fast integration. With detailed documentation and expert support, you can scale operations and customize solutions while minimizing development risks.
Whitebox Machine Learning
Unlike blackbox models, whitebox machine learning provides transparency, showing fraud risk scores and the logic behind them. This allows teams to customize detection rules, refine processes, and make informed decisions. By combining manual rules with AI-driven analysis, whitebox ML ensures precise insights and greater control over risk management.
Dynamic Friction
Dynamic friction balances light and heavy KYC checks to keep the customer journey smooth. While secure authentication is essential, excessive steps can drive users away. SEON runs background checks and triggers ID verification only for high-risk transactions.
Light KYC verifies data like email and social media presence, while heavy KYC includes passport checks and live verification. Accurate risk scores enable seamless progression for low-risk users and additional authentication for higher risks, ensuring security without unnecessary friction.
Frequently Asked Questions
A risk-scoring engine can block or trigger a review of suspicious transactions before approval to reduce online payment fraud.
Create a watchlist of customers who request multiple refunds and ensure that your customer success team is informed regarding past customer behaviors.
Generally, screening at signups, logins and transactions is a best practice. Additionally, initiate screening measures catalyzed by actions, including password or address changes.
The answer to this is dependent on your business. Chargeback guarantees can create a conservative system that blocks valid transactions.
You might also be interested in reading about:
