OTP Bank Details How It Detects Professional Cashback Hunters and Card Rental Schemes

OTP Bank has outlined its approach to identifying clients who systematically exploit cashback and loyalty mechanics without committing classic fraud. In an interview, Alexander Shirokov, head of information security control and financial operations, described the boundary between advantageous product use and abuse.

Shirokov noted that simply receiving benefits is not abuse. The key factor is whether operations reflect genuine need or are engineered exclusively to maximize rewards. Large volumes of bonuses or deep knowledge of tariffs alone do not trigger action; instead, the bank examines whether behavior deviates from the product’s intended purpose.

The bank acknowledges a distinct category of non-fraudulent yet highly active clients, including professional cashbackers and individuals who allow others to use their cards. This activity places additional strain on loyalty programs and can concentrate rewards among a small group, prompting potential revisions to program economics.

Card rental cases occur when owners share cards or details so others can make purchases while the owner collects cashback. Although not always fraudulent, such use violates personal-use terms and distorts the program’s design assumptions.

To differentiate rental from normal sharing with family, OTP Bank applies algorithms that compare current transaction patterns against historical profiles. Frequency, locations, timing, and technical signals are analyzed. Regular payments for relatives do not automatically indicate abuse unless multiple indicators suggest the card is used by several people over time.

Professional cashbackers study tariffs, categories, and bonus rules as meticulously as fraudsters study anti-fraud systems. Some participate in communities that exchange optimization tactics. While financially literate, their behavior can still affect program sustainability when it becomes the primary goal of card usage.

The bank also encounters collusion between clients and merchants to generate artificial cashback. Detection relies on joint analysis of client and merchant patterns, looking for sustained anomalies rather than single transactions.

Privilege resale, such as transferring taxi, restaurant, or lounge access, is identified through deviations from a client’s normal usage profile. No single factor leads to conclusions; decisions require aggregated data over extended periods.

Shirokov stated that professional abuse can pose an economic risk comparable to traditional fraud because it gradually erodes program viability. Automated models flag unusual behavior, but analysts, risk specialists, and product teams review complex cases. In some instances, the bank deliberately avoids action on isolated signals to prevent inconvenience to legitimate users.