Financial services organizations face unique AI implementation challenges. Regulatory requirements from MAS, BNM, and other authorities demand explainable, auditable AI systems. Model risk management frameworks must evolve to accommodate new technologies. Data privacy and cross-border restrictions add complexity to every deployment.
We help banks, insurers, and asset managers navigate this complexity. Our approach ensures AI implementations are compliant from day one while delivering measurable business value. From credit decisioning to fraud detection, we've helped financial institutions across Southeast Asia deploy AI responsibly.
Credit decisioning, fraud detection, and customer service automation for retail and corporate banking.
Claims processing, underwriting support, and risk assessment for life, health, and general insurance.
Portfolio analytics, research automation, and client reporting for asset managers and wealth advisors.
Transaction monitoring, compliance automation, and customer experience optimization.
We work with financial institutions through four integrated approaches, adapting each engagement to your regulatory environment and organizational readiness.
Hands-on workshops covering AI governance, model risk management, and regulatory compliance for banking and insurance teams.
Map and optimize credit decisioning, compliance, and customer service workflows for effective AI integration.
Custom AI tools for regulatory compliance checking, document analysis, and model validation workflows.
Ongoing guidance on MAS, BNM, and regional AI governance requirements as regulations evolve.
HOW WE CAN HELP
Navigate MAS, BNM, and regional AI governance requirements with ongoing advisory support.
Build AI literacy across leadership teams responsible for model risk and compliance decisions.
Assess current capabilities, compliance gaps, and readiness for AI-powered financial services.
Test AI solutions in controlled environments with appropriate model validation frameworks.
Maximize training subsidies and government grants available for AI transformation in financial services.
Corporate training in Malaysia is increasingly focused on AI and digital transformation. With HRDF claimable programmes, companies can upskill their entire team in AI at zero net cost. This guide covers the best corporate AI training options, HRDF claim process, and how to choose the right programme for your business.
The HRDF levy is a mandatory contribution that funds employee training in Malaysia. Understanding how the levy works, how much your company contributes, exemption rules, and how to maximise your levy utilisation is essential for HR leaders and finance teams. This guide covers everything about HRDF contributions.
Singapore SMEs qualify for enhanced training subsidies through the Enhanced Training Support for SMEs (ETSS) scheme. This means 90% course fee subsidies for all employees regardless of age, higher Absentee Payroll rates, and priority access to SkillsFuture Enterprise Credit. Here is how to maximise your SME training budget.
FEATURED INSIGHTS
AI courses designed for financial services companies. Banking, insurance, and fintech-specific modules covering compliance-safe AI use, MAS/BNM guidelines, and practical applications.
The Bank of Thailand (BOT) released mandatory AI Risk Management Guidelines in September 2025 for all financial service providers. Built on FEAT-aligned principles, they require governance structures, lifecycle controls, and fairness monitoring.
The Philippines National Privacy Commission issued Advisory Guidelines on AI in December 2024, requiring organizations to identify and limit algorithmic bias, prohibit AI washing, and comply with the Data Privacy Act for all AI data processing.
Vietnam's Law on Artificial Intelligence, effective March 2026, is the first standalone binding AI law in Southeast Asia. It introduces risk-based classification, registration requirements, and penalties up to VND 2 billion for non-compliance.
Thailand's PDPA imposes strict data protection requirements on AI systems. With a draft AI law expected in 2026 and new BOT AI guidelines for financial services, companies must prepare for an increasingly regulated environment.
PERTAMA TOOLS
Beyond advice, we design and build custom AI tools for financial services organizations. These are illustrative examples—each engagement produces solutions tailored to your specific needs.
Automated review of AI use against internal policies and regulatory requirements
Solves: Manual policy compliance checks are slow and inconsistent. This tool scans AI use cases against your policy framework and flags gaps before regulators do.
Real-time visibility into AI model performance and risk indicators
Solves: Model risk teams lack consolidated views of model health. This dashboard surfaces performance drift, bias indicators, and audit trails in one place.
Intelligent routing of customer inquiries to appropriate handling paths
Solves: Customer service teams waste time manually triaging inquiries. This tool classifies queries by type, urgency, and complexity for faster resolution.
Extract compliance requirements from regulatory circulars and guidelines
Solves: Compliance teams spend hours reading new regulations. This tool extracts actionable requirements and maps them to your existing controls.
AI is reshaping financial services across credit risk assessment, fraud detection, regulatory compliance (RegTech), customer onboarding, and personalised wealth management. In Asia-Pacific specifically, institutions are leveraging AI to navigate complex multi-jurisdictional regulations and serve increasingly digital-first customers.
Key challenges include model explainability requirements from regulators (MAS, APRA, HKMA), data privacy across jurisdictions, algorithmic bias in lending decisions, and maintaining audit trails for AI-driven decisions. A robust AI governance framework is essential before scaling AI initiatives.
A pilot project typically takes 6-8 weeks. A full-scale implementation with governance frameworks, team training, and production deployment usually spans 4-6 months. The key is starting with high-impact, lower-risk use cases and building organisational confidence before tackling more complex applications.
ROI varies by use case, but common outcomes include 30-50% reduction in manual compliance review time, 20-40% improvement in fraud detection rates, and significant reduction in customer onboarding time. The fastest payback typically comes from automating repetitive, document-heavy processes.
We stay closely aligned with regulatory frameworks from MAS (Singapore), APRA (Australia), BNM (Malaysia), and other APAC regulators. Our governance frameworks are designed to meet current and emerging regulatory expectations, and we regularly brief clients on regulatory developments that affect their AI initiatives.
Let's discuss your specific challenges and how AI can drive measurable results for your organization.
