> ## Documentation Index
> Fetch the complete documentation index at: https://www.marqeta.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# June 2026 Risk, Fraud, and Disputes Release Notes

> Release notes for the Risk, Fraud, and Disputes platform: new features, enhancements, fixed issues, and notable documentation changes.

To follow new Risk, Fraud, and Disputes release notes with RSS, subscribe to the current release notes feed:

`https://www.marqeta.com/docs/developer-guides/risk-fraud-disputes-release-notes/rss.xml`

**New features**

## Real-time fraud decisioning on token provisioning

The Marqeta Real-Time Decisioning (RTD) engine now integrates directly into the token provisioning flow, letting issuing programs detect and block fraud during token provisioning before a response is returned to the card network.

If you choose to activate this feature for your program, every token provision request for Visa and Mastercard is evaluated by RTD's rule engine before the network response is issued, providing you with real-time fraud decisioning.

RTD enables you to set transaction thresholds to detect systematic primary account number (PAN) enumeration from specific token requestors or devices. RTD then automatically blocks token requestors and devices after they exceed configurable thresholds. RTD also enables you to set rules on when you want to unblock requestors and devices after an attack occurs.

When RTD declines a token provision request due to suspected fraud, the Token Provisioning service returns a standardized network response. This makes declined invalid PANs indistinguishable from fraud-triggered declines to prevent enumeration signal leakage.

To activate this feature for your program, contact your Marqeta representative.

**Enhancements**

## Enriched data in Real-Time Decisioning

The Marqeta Real-Time Decisioning (RTD) platform now supports additional transaction and merchant data beyond standard authorization fields, giving programs more context to detect fraud as transactions happen.

Requests can include order details, account metadata, device information, shipping and billing information, geolocation, and behavioral signals. By combining this additional context with the Marqeta fraud models, you can detect suspicious activity more accurately, reduce false positives, recognize trusted merchants, and improve approval rates for legitimate transactions.

> [!NOTE]
>
> This data might not be available for all transactions. Enriched merchant data is sourced from third-party data partners and matched against transactions that flow through RTD. If there is no match to an existing transaction in RTD, the data will not be available.


## Related topics

- [June 2026 Card Issuing and Processing Release Notes](https://www.marqeta.com/docs/developer-guides/card-issuing-processing-release-notes/2026-06.md)
- [June 2026 UX Toolkit Release Notes](https://www.marqeta.com/docs/developer-guides/ux-toolkit-release-notes/2026-06.md)
- [January 2026 Risk, Fraud, and Disputes Release Notes](https://www.marqeta.com/docs/developer-guides/risk-fraud-disputes-release-notes/2026-01.md)
- [February 2026 Risk, Fraud, and Disputes Release Notes](https://www.marqeta.com/docs/developer-guides/risk-fraud-disputes-release-notes/2026-02.md)
- [March 2026 Risk, Fraud, and Disputes Release Notes](https://www.marqeta.com/docs/developer-guides/risk-fraud-disputes-release-notes/2026-03.md)