
How HyperPay Built a Scalable AML Program to Meet Regulatory Requirements
35%
Reduction in average alert handling time
45%
Reduction in false positives
Access the Full Case Study
HyperPay is a leading payment services provider operating in the Middle East. As the business expanded, it needed an AML compliance program capable of supporting high merchant volumes, complex payment activity, and evolving regulatory expectations.
The organization partnered with MOZN to establish a scalable compliance foundation aligned with regulatory AML/CFT requirements.
Building Compliance Under Tight Timelines
HyperPay needed to operationalize a complete AML compliance stack within a demanding project timeline.
At the same time, it had to accurately screen and onboard thousands of merchants, address the complexities of Arabic name matching, and configure AML controls suited to payment services and real-time transaction activity.
Drawing on its payments and AML expertise, MOZN’s professional services and customer success teams worked closely with HyperPay throughout the project, acting as an extension of its team to help build a robust and scalable compliance program.
Deploying an AI-Driven AML Compliance Program
HyperPay partnered with MOZN to deploy an advanced AI and NLP-driven AML compliance program aligned with regulatory requirements.
The implementation covered critical areas of the compliance lifecycle, including merchant screening and onboarding, customer risk scoring, transaction monitoring, and operational readiness.
Download the complete case study to learn how MOZN supported the firm through configuration, integration, testing, threshold tuning, and deployment.
Supporting Faster, Safer Merchant Onboarding
The new compliance program enabled HyperPay to accurately screen entities and onboard merchants at scale while maintaining the controls required to manage financial crime risk.
Its scalable foundation was also designed to support new products, increasing transaction volumes, additional payment rails, and evolving regulatory requirements.
Key Results
- 35% - Reduction in average alert handling time
- 45% - Reduction in false positives
