Fraud Risk Analyst, Strategy & Analytics - Credit

Monee; Singapore; On-site; SGD 60,000 - 80,000; Full time

Job summary

Monee is looking for a candidate in Singapore for a regional role focusing on fraud risk strategy in ASEAN markets. The position involves developing fraud risk strategies, applying data analytics and machine learning, and working closely with various teams to inform strategy decisions. Ideal candidates should possess a Bachelor's degree in a related field and have experience in data analytics or applied data science. Strong SQL and Python skills are also required, among other qualifications.

Qualifications

• 1+ year of hands-on experience in data analytics or applied data science.
• Experience in finance, banking, or fintech is a plus.
• Basic understanding of machine learning methods.

Responsibilities

• Support development of fraud risk strategies across key areas.
• Apply data analytics and machine learning methods to identify patterns.
• Analyze daily risk exposure and prepare insights for review.

Skills

Data analytics

SQL

Python

Machine learning

Statistical reasoning

Verbal communication

Problem-solving

Collaboration

Education

Bachelor’s degree in Computer Science, Engineering, Business Analytics, Information Technology, Finance, Statistics

Tools

SQL

Python

AI coding assistants

Job description

About The Team

This is a regional role covering ASEAN markets, supporting fraud risk strategy for Monee’s consumer credit products. You will work closely with product, engineering, and business teams to help design and scale risk controls in a fast-growing environment, with exposure to business impact and opportunities to contribute to strategy decisions across multiple markets.

Job Description

• Support the development and refinement of fraud risk strategies across key risk areas, including transaction misuse, merchant-related risks, coordinated fraud activity, and scam-related threats across the credit lifecycle
• Assist in structuring ambiguous fraud problems into clear hypotheses with defined success metrics
• Apply data analytics and basic machine learning methods to identify patterns, quantify exposure, and evaluate strategy performance
• Help establish and track key indicators across the four scenes; monitor trends and support ongoing strategy iteration
• Analyze daily risk exposure and fraud loss P&L; prepare insights and recommendations for review by senior team members
• Support analysis of local market dynamics and cross-market fraud patterns to inform regional strategy frameworks
• Develop KPI reports and contribute to continuous improvement of business policies and processes

Requirements

• Bachelor’s degree in Computer Science, Engineering, Business Analytics, Information Technology, Finance, Statistics, or a related field
• Minimum 1 year of hands-on experience in data analytics or applied data science
• Experience in the finance, banking, or fintech sector is a plus
• SQL and Python — working proficiency; used for analysis, data preparation, and basic prototyping
• AI coding assistants — familiarity with tools such as Claude Code, Codex, or similar is a plus
• Machine learning methods — basic understanding and some hands-on exposure to supervised learning, anomaly detection, or related techniques
• Statistical reasoning — foundational knowledge of hypothesis testing and experimentation
• Written and verbal communication — able to present data insights clearly with guidance
• Problem-solving ability — able to break down problems into manageable components with support from senior team members
• Domain interest — exposure to payment fraud, merchant risk, or related areas is preferred
• Collaboration — ability to work effectively with cross-functional teams in a fast-paced environment