By Shahinez Guetal, Area Front Office Manager at Marriott International
The hospitality sector is vulnerable to many fraudulent transactions, which can occur in different forms, from bogus bookings to stolen credit card details. These activities have an impact on revenue and erode customer confidence. Machine learning (ML) is useful for detecting this fraud in hotel transactions. The article explores the intricacies involved in fraud detection within the hotel industry and presents a comprehensive ML framework that may be used to address such issues.
Fraudulent Activities
Digitalization of the hospitality industry has allowed guests to book and make payments online, saving time and opening avenues for people who want to practice fraud. The following are some examples of fraudulent activities that can occur during hotel transactions1.
Reservation Fraud: This includes producing fake reservations using stolen credit card information or bots that reserve many rooms only for them to be canceled later.
Chargeback Fraud: Here, legitimate charges are frequently disputed after a stay. Hotels may experience complaints about lost cards or other fabricated cases.
Identity Theft: The use of personal details purloined to book or gain access to guest accounts.
Payment Fraud: Using counterfeit payment means such as fictitious credit card information when making payments.
These fraudulent practices can have several financial implications for hotels, including2:
Loss of Revenue: Not-paid reservations and chargebacks directly affect net income. Hotels have to pay workers and other reservations utilized.
Operational Costs: Investigating and solving cases connected with fraudulent transactions demands time.
Damage To Reputation: Cases involving fraud will likely discourage subsequent bookings by other travelers.
Thus, implementing efficient anti-fraud measures is not an option but a necessity for hotels to protect their sales volumes, maintain operational efficiency, and retain customer trust.
Machine Learning Systems for Detecting Frauds
Machine learning employs algorithms that process huge volumes of transaction data to identify patterns indicative of fraudulence, making it more reliable than traditional rule-based systems. As a hotel owner/operator, you enjoy having an ML framework to monitor/detect fraud. The following are the associated benefits3.
Adaptability: Machine learning models can adapt to changing fraud patterns with time, unlike static rules, which may become outdated with a change in tactics.
Accuracy: An ML algorithm can spot even slight anomalies humans may ignore by analyzing intricate data connections.
Scalability: These models efficiently process large transaction volumes, making them suitable for hotels with a high turnover of clients.
Machine Learning Models to Counter Fraud
The following are the common machine learning models you can use to detect and monitor fraud4.
Neural Networks: These deep learning models can learn complex patterns from data. They require large amounts of labeled data and computational resources. Neural networks are widely used for fraud detection, adopted by 50% of institutions.
Decision Trees: Decision trees create a flowchart-like structure to make decisions based on feature values. They are useful for detecting patterns in transaction data, especially when cards are used for payments. Depending on the decision tree’s complexity, 70-90% accuracy levels are achievable.
Random Forests: An ensemble method that combines multiple decision trees to improve accuracy. Random forests are robust and handle noisy data well. These are more accurate than decision trees, with over 95% accuracy and precision.
Logistic Regression: This algorithm is widely used for binary classification tasks. It models the probability of a binary outcome (fraudulent or not) based on input features. This is among the most accurate, with 99% and above, and hence increasingly reliable.
Support Vector Machines (SVM): SVMs find a hyperplane that best separates different classes. They work well for linear and non-linear data, flagging transactions as fraudulent or not fraudulent. Like logistic regression, they have accuracy above 99% and hence reliable.
Machine Learning Framework Steps
To mitigate the possibility of fraud related to hotel transactions, you can adopt a thorough system. The ML framework, in particular, includes a series of consecutive important steps for detection and prevention.
Data Collection and Preprocessing
This step involves collecting information from reservation systems, payment software, client profiles, and previous fraudulent activities. In the end, extensive data cleaning and readying care for null values, outliers, and data integrity. Conversely, a careful combination of domain expertise and rigorous data analytics may result in new properties being developed.
Model Selection and Training
The next turn-key point is to employ the appropriate machine learning algorithms matching the fraud type and the data features. For instance, supervised learning pedestals such as random forests and support vector machines (SVMs) are proficient in cheating classification. In contrast, non-supervised learning algorithms such as isolation forests and autoencoders are the most efficient in discovering anomalous transaction behavior. Once such models are selected, they are trained with labeled data, including fraud falcons, and their performance metrics, such as accuracy, precision, recall, and F1 score, are evaluated.
Model Deployment and Monitoring
Learning models are trained by machine learning systems and deployed into a production area where the models perform real-time transaction analysis. Continuous monitoring of models’ performance is essential, meaning periodic retraining with fresh data is of great necessity to maintain accuracy and be flexible to emerging fraud trends. A warning system designed to put hotel staff on alert whenever a booking activity is observed to be abnormal is established, making it easier to respond to and investigate.
Ongoing Improvement and Optimization
In a highly dynamic and iterative process, it is important to carry out continuous improvement and optimization. Continually checking false positives and negatives results in better identification of what to enhance. Considering adjustments to model parameters, implementation of various algorithms, and their impact on detection precision is one of the required actions. Additionally, integrating other safety features, such as fraud prevention instruments and identification verification services, enhances the machine learning system’s effectiveness against the dynamic development of security threats.
Data Privacy and Security
Compliance with the data privacy regulations, GDPR, and CCPA at any process stage is necessary. Well-reinforced security features should be implemented to protect customers’ data from illegitimate use and exposure. Data integrity and security are set to be the building blocks of the ML framework that facilitate an ethical representation of the fraud detection process and fully responsible for using personal data.
Conclusion
Fraud detection in hotel transactions is a critical challenge with significant financial and reputational implications. Machine learning offers a powerful tool for combatting this issue by providing an adaptable, accurate, and scalable solution. By continuously refining and improving ML systems, hotels can protect their revenue, streamline operations, and maintain a positive customer experience.
Sources
1. Alemar, S. (2023, January 31). The most common types of fraud in the hotel industry & how to prevent them. Canary Technologies. https://www.canarytechnologies.com/post/types-of frauds-in-hotel-industry
2. Kassem, R. (2024). Spotlight on fraud risk in hospitality a systematic literature review. International Journal of Hospitality Management, 116, 103630.
https://doi.org/10.1016/j.ijhm.2023.103630
3. Fraudcom International. (2023, January 25). The advantages of Machine Learning in Fraud Prevention. Fraud.com. https://www.fraud.com/post/the-advantages-of-machine learning-in-fraud-prevention
4. Afriyie, J. K., Tawiah, K., Pels, W. A., Addai-Henne, S., Dwamena, H. A., Owiredu, E. O., Ayeh, S. A., & Eshun, J. (2023). A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions. Decision Analytics Journal, 6, 100163. https://doi.org/10.1016/j.dajour.2023.100163
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