The financial services sector in Malaysia is undergoing a profound transformation. Bank Negara Malaysia (BNM), the country's central bank and principal financial regulator, has made it clear that artificial intelligence is not merely a technological upgrade — it is a strategic imperative for the industry's future competitiveness. In its Technology Risk Management framework and various policy papers, BNM has outlined expectations for how financial institutions should adopt, govern, and manage AI-related technologies.
For banks, insurance companies, asset managers, and fintech firms operating in Malaysia, the message is unambiguous: organisations that fail to build AI capabilities across their workforce will fall behind competitors that do. However, the regulated nature of financial services means that AI adoption must be approached with greater care, governance, and compliance awareness than in most other industries.
This is precisely why structured AI training — delivered by experienced providers and claimable through the Human Resources Development Fund (HRDF) — is essential for Malaysian financial institutions.
Bank Negara Malaysia has issued several directives that directly or indirectly affect how financial institutions use AI:
The TRM framework requires financial institutions to manage technology-related risks, including those arising from AI and machine learning systems. Key requirements include:
BNM's fair treatment guidelines are particularly relevant to AI applications in customer-facing processes. AI systems used for credit decisions, insurance underwriting, or customer segmentation must not introduce unfair bias or discrimination. Training programmes must cover how to audit AI outputs for fairness and how to document AI-assisted decisions.
BNM's AML/CFT requirements intersect with AI in areas such as transaction monitoring, suspicious activity reporting, and customer due diligence. AI tools can enhance these processes significantly, but staff must understand how to use AI within the regulatory framework and how to explain AI-generated alerts to compliance officers and regulators.
Fraud is one of the areas where AI delivers the most immediate return on investment for Malaysian financial institutions. AI-powered fraud detection systems analyse transaction patterns in real time, identifying anomalies that manual review would miss. Training programmes cover:
AI is transforming credit risk management in Malaysian banks and lending institutions. Traditional credit scoring models based on limited variables are being enhanced with AI models that consider a wider range of data points. Training covers:
Regulatory compliance is a significant cost centre for Malaysian financial institutions. AI can automate many compliance tasks, including:
Know Your Customer (KYC) and Anti-Money Laundering (AML) processes are prime candidates for AI enhancement. Malaysian financial institutions spend significant resources on customer due diligence, ongoing monitoring, and suspicious transaction reporting. AI training in this area covers:
Chatbots and virtual assistants are increasingly deployed by Malaysian banks and insurance companies. Training covers how to design, train, and monitor AI-powered customer service tools, ensuring they provide accurate information and escalate appropriately to human agents.
The Human Resources Development Fund (HRDF), administered by Pembangunan Sumber Manusia Berhad (HRD Corp), is a levy-based system that funds employee training in Malaysia. Financial services companies registered under the Pembangunan Sumber Manusia Berhad Act 2001 are required to contribute a monthly levy of 1% of their employees' monthly wages.
Financial institutions can claim back the cost of AI training through the following process:
| Scheme | Coverage | Notes |
|---|---|---|
| SBL (Skim Bantuan Latihan) | Up to 100% of course fees + allowances | Best for programmes of 3+ days |
| SBL-Khas | Up to 100% of course fees | Best for 1-2 day workshops |
| PLT (Pelan Latihan Tahunan) | Based on approved annual training plan | Best for organisations planning multiple programmes |
Financial institutions typically have larger HRDF levy balances due to their higher payroll costs. This means banks and insurance companies often have substantial funds available for AI training that would otherwise go unused. Many financial institutions discover they have accumulated six-figure balances that can fund comprehensive AI upskilling programmes across their entire workforce.
AI training for financial services professionals is structured differently from generic AI training. The content is tailored to the regulatory, operational, and ethical context of the financial sector.
Day 1: AI Foundations and Tools (7 hours)
Day 2: Industry Use Cases and Governance (7 hours)
Financial services AI training is most effective when tailored to specific roles:
Malaysian financial institutions that invest in structured AI training typically see measurable returns within 90 days of programme completion:
The first step for any Malaysian financial institution is to assess the current AI readiness of the workforce and identify the teams that would benefit most from training. Most institutions begin with a pilot programme targeting 20-30 participants from risk, compliance, and operations — the functions where AI delivers the fastest measurable impact.
With HRDF funding available, the financial barrier to AI training is minimal. The real question is not whether to invest in AI training, but how quickly you can build the capabilities your teams need to remain competitive in an increasingly AI-driven financial services landscape.
Yes, AI training for banks and financial institutions is fully HRDF claimable in Malaysia. Financial services companies registered with HRD Corp can claim under SBL, SBL-Khas, or PLT schemes, covering up to 100% of training fees. The training provider must be registered with HRD Corp, and the grant application must be submitted before the training date.
Bank Negara Malaysia requires financial institutions to manage AI-related risks under the Technology Risk Management (TRM) framework. This includes model risk management for AI systems used in credit scoring and fraud detection, data governance for AI tools, and third-party risk assessment for cloud-based AI services. Financial institutions must also ensure AI systems comply with fair treatment of consumers and AML/CFT requirements.
Malaysian banks use AI for fraud detection through machine learning models that analyse transaction patterns in real time, identifying anomalies such as unusual transaction amounts, atypical merchant categories, or suspicious geographic patterns. AI training teaches banking staff how to interpret AI-generated fraud alerts, reduce false positives, and work alongside AI systems in fraud investigation workflows.
Malaysian financial institutions use AI for regulatory change monitoring (scanning BNM circulars and policy updates), policy gap analysis (comparing internal policies against regulatory requirements), regulatory reporting automation, and KYC/AML processes (identity verification, transaction monitoring, adverse media screening). AI training covers how to deploy and govern these tools within the BNM regulatory framework.
A standard AI training programme for financial services teams runs over 2 days (14 hours total). Day one covers AI fundamentals, generative AI tools, and prompt engineering for financial tasks. Day two focuses on industry-specific use cases including fraud detection, credit risk, KYC/AML, and AI governance aligned with BNM expectations. Some institutions extend this to a 4-week blended programme for deeper skills development.
