AI Compliance Expert (Finance)
Ensures financial institutions' AI systems (credit scoring, fraud detection, AML, automated lending) meet regulations — managing model risk, algorithmic fairness audits, and regulatory reporting. 2026 sees triple regulatory convergence: EU AI Act + DORA + MiCA. 70%+ banks use AI but lack governance. Singapore MAS has APAC's strictest AI regulatory framework, driving regional demand.
- Salary Range
- ¥30K–¥60K/month
- Hiring Companies
- JPMorgan · DBS Bank · Ant Group
- Hot Regions
- SingaporeEuropean UnionUSAU.K.AustraliaMalaysia
- Core Skills
- Financial compliance · foundation model · Risk management
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Job Description Analysis
Core Responsibilities
- Ensure financial institutions’ AI systems (credit scoring, anti-fraud, AML, automated lending) comply with regulatory requirements
- Manage AI model risk governance—model validation, performance monitoring, document management, and audit trails
- Conduct algorithmic fairness audits – ensuring credit and insurance AI does not discriminate against specific groups
- Prepare AI system compliance reports and respond to regulatory review and inquiries
- Track global financial AI regulatory developments (EU AI Act, DORA, MAS Guidelines, APRA Guidelines) and develop a compliance roadmap
Requirements
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- In-depth understanding of the financial regulatory framework: Basel III/IV, MAS Notices, APRA Prudential Standards
- Understanding AI/ML applications and risks in finance: model risk management (SR 11-7), algorithmic discrimination, black box issues
- 3+ years of experience in Financial compliance, auditing or risk control
- Master data analysis tools (Excel advanced functions, SQL, Python basics), and be able to understand model output and statistical indicators
- Excellent regulatory interpretation and report writing skills
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Nice to Have
- CFA, FRM or CPA certification
- Have experience in financial auditing from the Big Four accounting firms
- Learn about the use of Explainable AI (XAI) technology in Financial compliance
A Day in the Life
- Morning: Review the monthly performance report of the credit scoring model - check whether the scoring distribution drifts and the difference in approval rates among different groups
- Morning: Discuss with the MAS Compliance Team the impact of the new AI Governance Notice on our anti-money laundering systems
- Afternoon: Review model validation documentation for anti-fraud AI systems to ensure compliance with SR 11-7 requirements
- Afternoon: Discuss the application of the new LLM in customer service with the data science team - assessing data privacy and compliance risks
- Evening: Prepare quarterly AI compliance report to Board Risk Committee next week
Career Transition Guide
Suitable Backgrounds
- Financial Compliance Officer/Auditor: Have regulatory and auditing experience, supplemented by understanding of AI technologySee this career today: Auditor · Compliance Analyst
- Financial risk control manager: Understand the risk management framework, AI model risk is a natural extensionSee this career today: Risk Analyst
- The four major audit consultants: have the foundation of audit methodology in the financial industrySee this career today: Auditor
- Regulatory agency staff (in the industry): in-depth understanding of regulatory requirements
Transition Path
- Step 1: Learn the core applications and risks of AI/ML in finance – credit scoring, anti-fraud, AML
- Step 2: Understand the AI model risk management framework (SR 11-7, SS1/23) and fairness audit methods
- Step 3: Learn the finance-related provisions of the EU AI Act and MAS AI Governance Framework
- Step 4: Learn basic model evaluation and fairness testing in Python (Fairlearn Toolkit)
- Step 5: Prepare a financial AI compliance case and apply for an AI compliance position in banking, insurance or FinTech
Learning Roadmap
Phase 1: Basics of Financial AI (January-February)
Phase 2: Compliance Practice (February-April)
Phase 3: Professional improvement (4-6 months)
Common Mistakes
Only checks the compliance of the model before it goes online, without continuous monitoring
Financial AI models require continuous monitoring—data drift, fairness degradation, regulatory changes all require dynamic response
Rely entirely on self-auditing by the data science team
Compliance review requires an independent third-party perspective - you cannot review yourself
Only focus on technical compliance and ignore the compliance of business processes
AI compliance is not only a model issue - data collection, manual review process, customer notification and other business links must be covered
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