Over the past few years, the landscape of retail and services has undergone a seismic shift. In the wake of COVID-19, businesses across the globe scrambled to migrate online. They wanted to stay competitive when in-person transactions suddenly became risky for health.
Now, a McKinsey 2022 report states that nearly nine in ten Americans prefer online payment options.
Yet, this shift has also opened floodgates for fraudsters. And they’ve been quick to exploit vulnerabilities in online transactions.
Payment fraud refers to illegal payment transactions attempted by fraudsters to scam people. This involves unauthorized use of payment info and complex scams targeting online retailers. However, it’s not all bad news.
Technologies like ML and Natural Language Processing (NLP) have changed the tides. They provide smart solutions to detect and prevent illegal transactions better than ever. Before we break them down, let’s take a look at the various types of payment frauds that we’ll be addressing in this blog.
Types of Payment Fraud
- Real-time payments (RTP) fraud
- Bank Identification Number (BIN) Attacks
- Chargeback Fraud
- Authorized Push
- Payment (APP) Fraud
- Phishing Attacks
- Card Not Present (CNP) Fraud
Payment Fraud Detection & Prevention with Next-Gen Tech
Relying on manual methods for detecting payment fraud has become outdated. This is primarily due to their time-consuming nature. Every second wasted on manual checks is an invitation for more fraudulent activities to go undetected. To tackle these issues, about 17% of businesses already use AI and machine learning to identify fraud. This signals a shift towards more efficient, technology-driven strategies.
Here’s how leveraging such technologies can help you too:
1. Behavior Analysis and Anomaly Detection
Behavior Analysis explores the typical patterns of user interactions. Conversely, anomaly detection focuses on singling out transactions that deviate from established norms. Together, these technologies work around the clock, monitoring transactions in real-time.
Real-time payments (RTP) fraud
RTP frauds involve scammers exploiting vulnerabilities in real-time payment systems, taking advantage of the speed and finality of transactions to carry out unauthorized or malicious transactions.
For such frauds, behavior analysis scrutinizes user behaviors such as login frequencies, devices used, and transaction types. It establishes a baseline for normal activity and flags any deviations, like logging in from unfamiliar locations, enabling swift responses like locking accounts or alerting users and security.
2. Machine Learning (ML) Models
ML models are smart algorithms that learn from past data. They recognize and adapt to changing fraud tactics. Unlike fixed systems, these models keep evolving and learning from new data. This improves their ability to predict fraud.
Chargeback fraud
Chargeback fraud is when customers falsely claim dissatisfaction to get a refund. This has always been a problem for retailers. ML models analyze past data to detect patterns of fake chargebacks. They look at how often a customer requests chargebacks. They check for differences in billing and shipping info. They also note unusual buying habits.
3. Natural Language Processing (NLP)
Using NLP tech in fraud prevention is a smart, efficient approach. It helps detect and prevent APP Fraud and Phishing Attacks. NLP allows systems to read and understand human language. It’s very good at looking at words and phrases in financial messages. It can also detect unusual patterns that might mean fraud.
Authorized Push Payment (APP) Fraud
With APP Fraud, people are tricked into sending money to fraudsters. NLP checks the text in payment requests or messages. It looks for anything that doesn’t match how we usually talk. This includes sudden, urgent demands for money or odd sending instructions. On detecting these warning signs, NLP can alert banks and customers.
Phishing Attacks
In Phishing Attacks, fraudsters often pretend to be someone else. They do this to get your personal information. NLP helps by scanning emails, messages, and social media. It looks for signs that something’s ‘phishy’. It watches for strange links, or unusual requests for info. By catching these clues, NLP can warn people before they click or share.
4. Advanced Device Intelligence And Entity Analysis
Entity Analytics in fraud detection systems fights CNP Fraud and Card Testing Scams. Effectiv’s advanced device intelligence solution, DeviceIntel, uses these technologies to tackle payment fraud. The solution leverages a unified entity data graph across all payment methods.
Card-Not-Present (CNP) Fraud
CNP Fraud occurs when someone uses stolen card details to buy without the physical card. DeviceIntel handles this by examining the full context of every transaction. It looks at who’s buying, what’s being bought, and the transaction’s location.
If a card used for groceries in Ohio suddenly buys watches in Paris, the system flags this as odd. It’s not just about what and where; it’s also about when and how. It compares these transactions against known patterns to find potential fraud.
Building Blocks For Online Payment Fraud Prevention
Setting up a unique plan to tackle payment fraud in your business is a bit like putting together a custom security system for your home. It’s all about figuring out where you’re most vulnerable and then picking the right tools and practices to keep the bad guys out.
Let’s walk you through the nitty-gritties step-by-step:
1. Checking the locks (Risk assessment)
Take a good look around your business to find its vulnerabilities. Just like checking doors and windows in a house, we look at where you might be an easy target for fraudsters. This could be anything from the type of transactions you process to how your customers interact with your website.
2. Setting the alarm systems (Prioritization)
After identifying all potential entry points, you decide which ones are most likely to be targeted. If your back door (mobile transactions) looks more inviting to a thief than your front (in-store purchases), you’ll prioritize setting up sensors and alarms there first.
3. Strategy Development
With your priorities in mind, you plan out exactly how to fortify your defenses. This might mean installing a state-of-the-art alarm system (advanced fraud detection software) or reinforcing doors and windows (enhancing encryption and security protocols on your website and payment systems).
4. Resource Allocation
Next, you would want to allocate resources for onboarding the technologies and tools you’d need to implement your fraud prevention strategy. You consider what you can DIY (like training your team on fraud awareness) versus when you need to call in the professionals (outsourcing to security experts).
5. Nightly Patrols (Monitoring and Evaluation)
After everything is in place, you regularly check your security footage and ensure all systems are operational, much like regularly reviewing transaction data and fraud alerts. Similar to how you would adjust a camera to eliminate blind spots, you refine your fraud detection strategies based on what you learn from these reviews.
Ethical Training for Advanced Fraud Detection & Compliance
Dealing with payment fraud means you’re in charge of protecting customers’ payment details. This is crucial to ensure this information stays safe from fraudsters. There are also strict laws that guide data handling. But since the jurisdiction differs from place to place, you’ll need to comply carefully.
Keep an eye for the following laws:
- General Data Protection Regulation (GDPR)
- California Consumer Privacy Act (CCPA)
- Virginia Consumer Data Protection Act
- Colorado Privacy Act
Now when you’re using tech to detect fraud, being transparent about the process is essential. Sometimes, this tech might stop a transaction or lock an account if suspicious. In such cases, your team must explain the situation to the customer clearly. No one likes uncertainty, especially about their finances. So, train your team to communicate plainly about these situations.
Tailoring Fraud Detection Technologies to Fit Your Business Needs
Setting up a payment fraud detection system for your business can feel like navigating through a maze. This is quite common if you’re not well-versed with the latest technologies. There are a plethora of fraud prevention tools ranging from ML algorithms to behavior analytics and beyond.
Each of these technologies has its strengths, but knowing which one fits your specific business needs is crucial. If you choose a solution that doesn’t align with your business model, you might end up complicating your operations instead of streamlining them.
Moreover, these advanced technologies come with a significant price tag, making the investment decision even more critical. If you’re seeking guidance on the path ahead, Effectiv can be an invaluable resource. They can help align you with optimal technologies for securing your business’s payment gateways.
FAQs
How is transaction fraud detected?
Transaction fraud is usually detected by analyzing patterns in how and where purchases are made. If something out of the ordinary pops up, it triggers an alert. Advanced tech like AI can also help by quickly comparing transactions against known fraud patterns.
How do banks do fraud detection?
Banks use a mix of software and surveillance techniques to catch fraud. They monitor account activity 24/7, looking for unusual patterns or transactions that don’t match your usual spending habits. They also use sophisticated algorithms and machine learning to improve detection over time.
How long does it take for a bank to detect fraud?
The time to detect fraud can vary significantly. Some fraudulent activities are caught almost instantly thanks to AI-based fraud detection systems in banking. These systems analyze transactions in real-time, flagging suspicious activity based on patterns and historical data.
However, more complex or subtle schemes, particularly those involving smaller amounts, might take days or even weeks to detect. Banks continuously refine their monitoring systems, including AI algorithms, to catch fraud as quickly.
What happens after a bank fraud investigation?
After a fraud investigation, if the bank finds that fraud did occur, they usually refund the stolen amount to your account. They might also issue new cards or account numbers to prevent future fraud. You’ll often be kept in the loop with updates as the investigation progresses.
Recommended reading: The Guide to Fraud Detection and Prevention in Banking
