Financial Crime Systems Analyst – UAT & AI/ML Validation

ACE Money Transfer Limited; Lahore; On-site; PKR 1,200,000 - 1,800,000; Full time

Job summary

ACE Money Transfer Limited is seeking an Associate – Financial Crime Systems to support the delivery, testing, and enhancement of financial crime technology platforms. This role emphasizes system validation, data analysis, and change delivery, collaborating with Product, Development, and Ops teams.

The ideal candidate has experience with enterprise systems, UAT, and data-driven platforms, plus exposure to rules-based and AI/ML-driven financial crime detection models, to help ensure accurate

Qualifications

• Experience in financial services technology or enterprise systems.
• Experience with UAT and data-driven platforms.
• Exposure to rules-based and AI/ML-driven financial crime detection models.
• Strong analytical and problem-solving skills.

Responsibilities

• Support financial crime systems including transaction monitoring, screening, and case management platforms.
• Participate in UAT, regression testing, and pre-production validation for system changes and releases.
• Validate business and functional requirements against system behavior and outcomes.
• Assist with testing of rules, thresholds, workflows, and alerting logic.
• Perform data validation and reconciliation to ensure accuracy of system inputs and outputs.
• Support testing and validation of AI/ML and analytical models, including scenario and threshold changes.
• Collaborate with Product, Development, and vendor teams to manage defects and delivery timelines.
• Maintain testing, release, and system documentation.

Skills

Data Analysis

Python

SQL

Excel

Education

Bachelor's degree

Tools

JIRA

Confluence

Job description

ACE Money Transfer Limited is seeking an Associate – Financial Crime Systems to support the delivery, testing, and enhancement of financial crime technology platforms. This role emphasizes system validation, data analysis, and change delivery, collaborating with Product, Development, and Ops teams.

The ideal candidate has experience with enterprise systems, UAT, and data-driven platforms, plus exposure to rules-based and AI/ML-driven financial crime detection models, to help ensure accurate