Credit Risk Analyst, Retail Lending - Maribank

Monee SG Private Limited; Singapore; On-site; SGD 70,000 - 120,000; Full time

Job summary

Monee SG Private Limited in Singapore is seeking a Credit Risk Analyst to help build and monitor credit risk frameworks across SEA markets. You will work with data scientists, product, and operations to improve underwriting rules and decision processes for Retail lending.

You will analyze delinquency, roll rates, and risk indicators, propose policy adjustments, and ensure compliance with local regulations. The ideal candidate has 2+ years in credit risk and strong analytical and communication

Qualifications

• Bachelor’s or above in Finance, Economics, Math, Statistics, Data Science, or related fields.
• 2+ years of credit risk experience in digital/direct lending is preferred.
• Experience with underwriting, credit policy, portfolio monitoring or risk analytics is desirable.
• Experience in fintech/banking is highly preferred.

Responsibilities

• Develop end-to-end credit risk strategies for Retail direct lending across SEA markets.
• Utilize multiple data sources to support data-driven credit decisions and risk evaluation.
• Monitor portfolio performance including delinquency, roll rates and risk indicators.
• Propose risk mitigation and policy adjustments.
• Collaborate with data science, product, business, and operations teams.
• Provide risk insights and recommendations to senior management.
• Ensure credit policies comply with local regulatory requirements.

Skills

Analytical thinking

Problem-solving

Communication skills

Cross-functional collaboration

Education

Bachelor’s or above in Finance, Economics, Math, Statistics, Data Science, Business Analytics or related fields

Tools

Excel

SQL

Python

Job description

About Monee

Monee is a leading digital payments and financial services provider in Southeast Asia, with a growing presence in Latin America. Its mission is to better the lives of individuals and businesses in the region with financial services through technology.

Monee delivers a comprehensive range of digital financial products and services, including mobile wallets, payment processing, credit, banking, and insurtech. By leveraging technology and strategic partnerships, Monee aims to drive greater digital financial inclusion, convenience, and accessibility for individuals and small businesses across Southeast Asia and Latin America.

Monee is part of Sea Limited, a global technology company.

• Develop end-to-end credit risk strategies for Retail direct lending products across SEA markets, covering underwriting, credit policy, risk-based pricing, loan size and tenure.
• Utilize multiple data sources to support data-driven credit decisions and risk evaluation.
• Monitor portfolio performance including delinquency, roll rates and risk indicators; propose risk mitigation and policy adjustments.
• Establish and track KRIs and early warning signals to identify emerging portfolio risks.
• Perform portfolio risk and profitability analysis to refine credit policies and optimize risk‑return.
• Collaborate with data science, product, business and operations teams to improve scorecards, underwriting rules and decision processes.
• Provide risk insights, analysis and recommendations to senior management and stakeholders.
• Ensure credit policies and processes comply with local regulatory requirements.

• Bachelor’s or above in Finance, Economics, Math, Statistics, Data Science, Business Analytics or related fields.
• 2+ years of credit risk experience and above in digital/direct lending, preferably with exposure to Retail lending.
• Hands‑on experience in underwriting, credit policy, portfolio monitoring or risk analytics.
• Familiarity with delinquency, roll rate or vintage analysis is a plus.
• Proficient in Excel and SQL, Python skills are a plus.
• Strong analytical and problem-solving skills.
• Good communication and cross‑functional collaboration skills.
• Experience in fintech / banking / lending is highly preferred.
• Strong self‑motivation and ability to work independently