Travel and hospitality digital ecosystems represent some of the most financially lucrative targets in consumer-facing digital commerce. A single compromised account can hold a stored payment card, accumulated loyalty miles, passport details, and a history of saved travel preferences. These details can be accessible to an attacker in the absence of ironclad security. For criminals who treat account access as a commodity to be extracted and monetised, the travel sector offers a combination of stored value and weak security hygiene that is difficult to match in other verticals.
Why travel and hospitality accounts are prime targets
The loyalty programme dimension sets travel accounts apart from most other account types. The global value of outstanding loyalty points and miles exceeds $200 billion, and the volume of fraudulently redeemed loyalty currency has been estimated at $3.1 billion annually. Unlike payment card fraud, which can trigger an alert within hours, loyalty fraud typically takes between 150 and 180 days to investigate, giving attackers a considerable window to extract value and disappear.
The security habits of travellers compound the risk. Accounts are frequently created in haste at the end of a booking session, with passwords chosen for convenience rather than security. Most users do not treat a hotel loyalty login with the same caution they apply to a banking credential. The result is an account holding genuinely valuable assets that may already be compromised through an unrelated breach.
How account takeover attacks work
Most account takeover fraud attacks are a combination of customer complacency and innovative entry vectors.
Credential stuffing and credential theft
When usernames and passwords are exposed through large-scale data breaches, those credential pairs routinely appear on dark web markets within days. Attackers purchase these lists and test them against target websites using automated bots, brute forcing thousands of combinations at a speed that no manual process could approach. This technique is credential stuffing. It exploits the widespread habit of password reuse. At scale, it yields access to approximately 8% of targeted accounts.
Phishing and social engineering
Phishing messages are crafted to impersonate legitimate travel companies and trick recipients into surrendering their login details. It remains a consistent entry vector. More targeted variants use personal information gathered from public sources to construct communications that appear to come from a familiar contact, making the deception significantly harder to detect. In some cases, attackers compromise third-party partner accounts rather than customer accounts directly, then use that access to reach customers with fraudulent payment requests authenticated by real booking details.
Infosys BPM fraud detection and analytics services help organisations build and run fraud prevention programmes that bring together identity verification, behavioural analytics, and real-time detection to protect customer accounts and loyalty assets across the full travel lifecycle.
Prevention: why standard defences fall short
Web application firewalls are designed to protect servers from application-layer attacks, not to identify the subtle patterns of credential testing and account access that characterise account takeover fraud.
CAPTCHAs have a longer history but have been defeated. Automated systems now solve them with approximately 90% accuracy, while harder variants frustrate legitimate users without meaningfully deterring attackers. Signature-based detection systems struggle against distributed traffic routed through residential proxies, which makes automated credential testing indistinguishable from normal user activity by volume and geography alone. Stealth browser adoption: tools that mimic real user behaviour to evade detection; grew elevenfold during 2025.
Here are the prevention methods that can still hold their own against modern modus operandi:
Risk-based identity verification and multi-factor authentication
Applying the same level of authentication friction to every login creates barriers for genuine customers. In travel, logging in from a different country is routine, not suspicious. Risk-based identity verification reserves step-up challenges for sessions that combine multiple warning signs: an unfamiliar device, an IP address that has never accessed the account, or login timing outside any established pattern.
Applied this way, additional verification reaches the sessions that warrant it without degrading the experience for the vast majority of customers who pose no risk.
Behavioural signals and device fingerprinting
Once an attacker has entered valid credentials, the password is no longer a defence. Browser fingerprinting builds a per-user baseline from device characteristics, network metadata, and interaction behaviour, then flags sessions that deviate from it in instances such as:
- A session that bypasses all browsing activity and navigates directly to loyalty transfer controls the moment it logs in is exhibiting behaviour that no genuine returning customer produces.
- A device presenting as a standard browser but operating in a headless server environment is another clear signal.
These patterns are invisible to credential-only checks and CAPTCHA systems, but detectable through behavioural analysis.
Protecting account recovery flows
Many businesses focus their authentication investment on the login page and leave account reset flows comparatively unprotected. Attackers who fail to break a login will frequently attempt the password recovery route instead, particularly against loyalty accounts, which are often managed by separate systems with lighter verification requirements. Rate-limiting reset requests, requiring multi-channel verification, and applying the same risk signals to account recovery as to initial login all reduce this attack surface.
Real-time fraud detection and response
When a suspicious session is identified, the value of that detection depends on the speed and scope of the response. Effective fraud operations for travel businesses include the ability to challenge a session in progress and notify the affected customer promptly. The account status can be investigated while freezing high-risk account actions, such as:
- Loyalty transfers
- Payment method changes
- New booking creation
Reviewing what actually changed during the suspicious session narrows the recovery effort and informs future detection rules. It could be a new email address, transferred points, modified traveller profiles, etc.
The combination of direct losses, chargeback costs, and customer attrition makes a compelling case for investment in layered fraud prevention. Preventing account takeover fraud at scale requires integrated analytics, proactive monitoring across customer touchpoints, and the operational depth to investigate and respond when fraud occurs.
Frequently asked questions
Account takeover fraud happens when an attacker gains control of a customer account using stolen credentials, phishing, or other deceptive methods. In travel and hospitality, these accounts are especially valuable because they may store payment details, loyalty points, passport information, and booking history.
Travel accounts are attractive to fraudsters because they can contain high value loyalty balances and sensitive personal data. They are also frequently protected by reused passwords and weaker security habits, which makes them easier to compromise through credential stuffing and phishing.
Businesses can detect account takeover fraud by monitoring unusual login behaviour, unfamiliar devices, suspicious IP patterns, and rapid changes to account settings. Behavioural analytics and device fingerprinting help identify activity that looks different from a genuine customer session.
High risk actions such as loyalty point transfers, payment method changes, password resets, and new booking creation should have stronger verification. These actions are often the first targets after an account is compromised, so step up checks can help prevent financial loss.
The most effective approach is layered fraud prevention that combines risk based authentication, behavioural analysis, stronger recovery controls, and real time response. This reduces friction for genuine customers while making it much harder for attackers to complete fraud at scale.
