Fraud controls and abuse prevention in credit card reward systems

Reward fraud is different from payment fraud

Remember - incentives change behaviour, including bad behaviour

Payment fraud focuses on unauthorised transactions.
Reward abuse focuses on authorised but manipulative behaviour.

Examples include:

• users gaming reward thresholds
• coordinated low-value transactions
• repeated reward farming
• exploiting loopholes in trigger logic

Because transactions are legitimate, traditional fraud engines often miss reward abuse.

Common reward abuse patterns in credit card systems

(Patterns seen at scale)

Typical abuse scenarios include:

• transaction splitting to hit minimum thresholds
• repeated micro-transactions in short time windows
• category hopping to bypass caps
• coordinated activity across multiple cards
• reward redemption resale behaviour

Abuse usually starts small but scales quickly if unchecked.

Rule-based prevention as the first line of defence

Most reward abuse can be prevented using clear, deterministic rules:

• minimum transaction value thresholds
• per-user daily and monthly caps
• reward frequency limits
• cooldown periods between rewards
• exclusion of reversal or failed transactions

These controls are predictable, auditable and regulator-friendly.

How to control how fast rewards can be earned?

Use velocity and frequency throttling

Velocity controls prevent users from earning rewards too quickly.

Common techniques:

• maximum rewards per hour/day
• transaction count limits within a window
• burst detection (sudden activity spikes)

Velocity throttles are especially effective against automated or semi-automated abuse.

Category and merchant-level controls

Hoe you can narrow where rewards apply

Reward abuse often concentrates in:

• low-value categories
• predictable merchants
• utility or wallet transactions

Controls include:

• excluding high-risk categories
• setting category-specific caps
• limiting merchant eligibility
• rotating eligible categories periodically

This reduces incentive predictability for bad actors.

Redemption-side abuse detection

Remember, fraud doesn’t stop at issuance

Abuse can also occur during redemption.

Common signals:

• unusually fast redemption after issuance
• repeated redemption of the same brand
• resale-linked brand patterns
• abnormal redemption velocity

Monitoring redemption behaviour is critical for long-term control.

Separation from payment risk systems

Or why reward fraud should not touch card risk engines

Reward abuse controls should operate:

• independently from payment risk engines
• outside settlement and clearing flows
• without blocking legitimate transactions

This separation ensures:

• no impact on payment success rates
• simpler audit explanations
• faster iteration on reward logic

Audit and compliance considerations

Why controlled reward systems pass audits more easily

Auditors typically look for:

• predictable issuance logic
• documented caps and limits
• clear audit trails
• separation from monetary benefits

Rule-based reward systems:

• are easier to explain
• produce cleaner reports
• reduce regulatory scrutiny compared to cashback

How to balance abuse prevention with user experience?

Simple. Don’t punish genuine users.

Overly aggressive controls can:

• frustrate high-value users
• reduce trust
• suppress genuine engagement

Best practices include:

• conservative caps for new users
• relaxed caps for proven users
• transparent reward terms
• gradual limit increases based on behaviour

So, who owns reward fraud prevention?

Reward fraud prevention typically involves:

• product (rule design)
• risk (abuse patterns)
• engineering (implementation)
• finance (budget protection)

Remember, ownership should be shared, with clear escalation paths.