Why are airline loyalty programs attractive targets for fraud?

Loyalty points represent real economic value and can be converted into flights, upgrades, and rewards, making them attractive targets for attackers.

What are the most common types of airline loyalty fraud?

Common types include account takeover, unauthorized redemption of points, internal misuse of loyalty systems, and exploitation of program rules.

How can airlines detect loyalty fraud earlier?

Airlines detect loyalty fraud using transaction monitoring, behavioral analytics, anomaly detection models, and machine learning-based fraud detection systems.

How can airlines reduce loyalty program fraud risk?

Airlines can reduce fraud risk by implementing stronger authentication, monitoring redemption behavior, auditing internal system access, and using modern loyalty platforms with built-in fraud detection capabilities.

How are travel loyalty programs evolving beyond points and transactions?

The PDF describes a shift toward lifestyle-led loyalty that adds value beyond points—through a broader set of member value touchpoints and experiences, not only transactional earning and burning.

What do airlines and travel brands need to prioritize to modernize loyalty?

The PDF emphasizes customer-centricity, offering recognized value and access that members are willing to exchange data for, and designing program experiences around how members want to travel today.

How can loyalty programs use data to improve relevance without creating new silos?

The PDF frames data as a critical currency for loyalty and highlights bringing data together to enable a 360-degree customer view, which supports more relevant personalization and offer decisions.

Why do partnerships matter in travel loyalty upgrades?

The PDF highlights that loyalty value increasingly comes from ecosystems and partnerships across sectors, enabling richer redemptions, experiences, and everyday value beyond a single brand or sector.

How can loyalty teams address fraud risk as programs scale?

The PDF includes an AI-powered fraud detection use case focused on observing transaction patterns, identifying suspicious activity, and supporting measures such as detection and prevention during transactions.