Leveraging AI to Combat Credit Card Fraud in Hospitality: A Proactive Approach to Protect Revenue

The rapid evolution of online payments has led to significant convenience in hotel bookings but also brings with it a growing threat: credit card fraud. Today, fraud in card-not-present transactions, like online bookings, is a rising challenge for the hospitality industry. According to Mastercard, online fraud is nearly seven times more prevalent than in-person credit card fraud. This growing threat requires a shift to sophisticated fraud detection methods, with artificial intelligence (AI) and machine learning (ML) as key components of a proactive defense strategy.

Hotels that haven’t yet integrated AI into their fraud prevention systems may be missing out on efficient, reliable, and automated tools that help reduce costly chargebacks and preserve revenue. Many AI-powered fraud solutions—like Sertifi’s partnership with Kount, an Equifax company—offer critical support by analyzing data points to identify potentially risky transactions well before a guest’s arrival. This enables hotels to make more informed decisions, confidently accept secure bookings, and minimize the risks of fraudulent transactions.

Understanding the Impact of AI and Machine Learning in Fraud Detection

Machine learning, a subset of AI, allows computer systems to learn from vast amounts of data and recognize patterns. Unlike traditional rule-based systems, machine learning models continually adapt, refining their accuracy with each new data point. For fraud prevention, these systems can scan vast amounts of data in real time, identifying anomalies and risky behaviors that might signal fraudulent activities.

With Sertifi’s integration of Kount’s fraud prevention tools, hotels can streamline their credit card authorization process through advanced machine learning. Here’s how it works:

1. Transaction Data Collection
When a customer submits a credit card authorization form, the system gathers various data points, including credit card details, IP address, and geographic location, alongside historical data linked to that card.

2. Data Analysis Using Machine Learning Models
The system evaluates collected data with two types of machine learning models: supervised and unsupervised. Supervised models are trained on historical data to spot familiar patterns of fraud, while unsupervised models instantly detect unusual patterns in real time.

3. Risk Scoring
The AI assigns a risk score to each transaction on a scale from A (lowest risk) to F (highest risk). For instance, a transaction with a high risk score might involve a single IP address initiating multiple bookings—a red flag for potential card-testing fraud.

4. Real-Time Decision Making
Based on the risk score, hotels can decide whether to accept or flag the transaction for further review, empowering them to protect revenue without jeopardizing guest experience. This score serves as a guide, allowing hotel staff to act on significant warnings while maintaining the flexibility to verify legitimate bookings.

Machine Learning’s Role in Reducing Chargebacks

Chargebacks often present a financial drain on hotels, not only impacting revenue but also straining operational resources. By integrating AI into fraud prevention, hotels can significantly cut down on criminal-related chargebacks. Machine learning engines detect fraudulent patterns that would otherwise lead to chargebacks due to unauthorized transactions. For example, if a fraudster uses stolen card data for a no-show booking, the fraud detection system identifies this risk early, preventing the loss of both revenue and potential occupancy.

It’s worth noting that not all chargebacks are the result of criminal activity. Some guests, for instance, may dispute legitimate charges simply because they forgot about the transaction or opted for a chargeback rather than a direct refund request. While machine learning models excel at detecting fraud, additional best practices and customer support measures can help mitigate non-criminal chargebacks.

Supervised vs. Unsupervised Learning in Fraud Detection

Machine learning models used in fraud prevention rely on two main types of learning:

1. Supervised Learning: This is the model’s “memory.” It uses historical data to understand previous patterns and outcomes. For instance, it can recognize that a particular email address or IP address has been linked to fraudulent bookings in the past. This predictive memory allows it to flag transactions that match familiar fraud markers.

2. Unsupervised Learning: Acting more like “instinct,” unsupervised learning models detect irregularities without any previous examples. By identifying anomalies in real-time, such as unusual geographic activity or device mismatches, these models can detect emerging fraud schemes, often quicker than supervised models.

Enhancing the Guest Experience While Preventing Fraud

AI-powered fraud prevention doesn’t have to come at the expense of guest experience. By flagging only the most suspicious transactions, these systems reduce the need for manual verification, streamlining the booking process and allowing legitimate customers to book quickly and securely. Additionally, if a transaction is flagged, hotel staff can reach out to guests to verify their identity, providing an opportunity for personalized engagement and trust-building.

Best Practices to Maximize Fraud Prevention Effectiveness

To fully benefit from AI-driven fraud prevention, hotels should integrate these systems with broader payment security protocols. Here are some steps to enhance fraud protection:

• Encourage Direct Bookings: Direct bookings not only reduce dependence on third-party platforms but also minimize the risk of fraud due to controlled booking channels.
• Train Staff on Fraud Patterns: Regular training on emerging fraud trends enables hotel teams to spot potential red flags manually when needed.
• Offer Flexible Verification Options: When transactions are flagged, verify with the guest through additional methods, such as confirming billing information or personal identification.

Future-Ready Fraud Prevention for Hotels

In the fast-evolving landscape of online fraud, traditional methods simply cannot keep up. By adopting AI-based fraud prevention solutions, hotels can stay ahead of emerging fraud schemes, reduce operational costs linked to manual reviews, and protect both their revenue and their reputation. AI-powered tools like those from Sertifi and Kount represent the future of secure, seamless guest interactions, giving hotels the confidence to expand while providing a safe, trustworthy experience. As the digital payment environment continues to advance, AI will play an increasingly essential role in helping hotels minimize risk and enhance guest satisfaction.