AI in Malaysian Banking

AI in Malaysian banking refers to the deployment of artificial intelligence and machine learning systems by financial institutions licensed by Bank Negara Malaysia (BNM) to improve customer service, manage risk, fight financial crime, and increase operational efficiency. Malaysian banks have been among the earliest large-scale enterprise adopters of AI in Southeast Asia, driven by competitive pressure from regional digital banks, the digitalisation imperative of the MyDIGITAL Blueprint, and the maturing availability of AI cloud services within the Malaysian jurisdiction.

The Malaysian banking sector is dominated by eight domestic anchor banks — Maybank, CIMB, Public Bank, RHB, Hong Leong Bank, AmBank, Affin Bank, and Alliance Bank — together with foreign-licensed banks including HSBC, Standard Chartered, OCBC, and UOB. A new generation of digital banks licensed under the BNM Digital Bank Framework — including GXBank (Grab and Kuok), Boost Bank (Axiata and RHB), AEON Bank, KAF Digital Bank, and YTL-Sea Digital Bank — has accelerated AI adoption across the sector since 2022.

Major investments and partnerships

Maybank announced an MYR 10 billion (approximately USD 2.5 billion) five-year technology, data, and AI investment in 2025 as part of its M25+ strategy, of which a 1 billion ringgit five-year strategic partnership with Microsoft covers Azure as the primary cloud platform and Microsoft 365 Copilot for 44,000 employees. Maybank reports that digital sales contribute 78 percent of total sales, supported by the MAE mobile platform serving 8.7 million active digital users; the apps 360 Digital Wealth feature uses AI to personalise product recommendations based on customer objectives and risk profile.

CIMB, with 18 million consumer clients and 11 million digitally active users across ASEAN, deploys WhatsApp-based AI advisory and AI-enabled welcome desks. Industry analyses report CIMB chatbot accuracy of 94 percent across customer interactions. Public Bank, RHB, and Hong Leong Bank have invested in AI-powered fraud detection and credit decisioning. The digital banks operate on cloud-native architectures with AI embedded across onboarding, underwriting, and customer service.

Application categories

| Category | Examples | Typical AI techniques | |---|---|---| | Fraud detection | Card-not-present fraud, account takeover | Anomaly detection, graph neural networks, autoencoders | | Customer service | Chatbots, voicebots, agent assist | Large language models, retrieval-augmented generation | | Credit decisioning | SME loans, personal loans, BNPL | Gradient boosting, alternative-data scoring | | Anti-money laundering | Transaction monitoring, KYC | Rule engines plus supervised classifiers | | Wealth advisory | Robo-advice, portfolio personalisation | Reinforcement learning, recommendation systems | | Operational AI | Document processing, contact centres | OCR, speech recognition, summarisation |

Fraud detection is the most mature application, with all major banks operating real-time scoring on card and online transactions. The 2024 BNM industry data shows scam-related losses exceeding RM 1.5 billion annually, prompting industry-wide investment in AI-driven scam detection and the National Scam Response Centre (NSRC) coordinated by BNM, the police, and telcos. Customer service automation is the second-most visible application, with AI agents handling routine inquiries, password resets, statement requests, and product information.

Regulatory framework

Malaysian banking AI operates under several overlapping rules. The Risk Management in Technology (RMiT) policy document issued by BNM in 2020 and updated in 2023 sets minimum standards for cyber resilience, third-party risk, cloud adoption, and model risk management. RMiT applies to all licensed financial institutions and prescribes requirements such as model validation, independent review, and incident reporting that directly constrain AI deployment.

In 2024 BNM issued a Discussion Paper on Artificial Intelligence in the Financial Services Industry setting out proposed principles for fair, accountable, transparent, ethical, robust, and human-centric AI. The principles align with the broader Malaysia AI Governance Framework and ASEAN AI Governance Guide. The Personal Data Protection Act 2010 (PDPA), strengthened by 2024 amendments, governs personal data processing in AI systems and includes a data breach notification requirement and a Data Protection Officer obligation.

The Financial Services Act 2013 and Islamic Financial Services Act 2013 provide the prudential foundation. The Securities Commission Malaysia (SC) regulates capital-markets AI, including robo-advisors operating under the Digital Investment Management framework. Cybersecurity Malaysia and the National Cyber Security Agency (NACSA) provide cross-sector cyber guidance applicable to AI systems.

Digital banks

The five digital banks licensed under the BNM Digital Bank Framework are AI-first by design. GXBank, owned by a consortium including Grab and the Kuok Group, launched commercially in 2023 and operates on a Google Cloud foundation with AI used throughout onboarding and customer service. Boost Bank, owned by Axiata and RHB, focuses on micro-merchants and SMEs. AEON Bank operates as an Islamic digital bank serving B40 and middle-income segments. KAF Digital Bank and YTL-Sea Digital Bank target specific market segments under BNM Foundational Phase obligations.

References

1. Bank Negara Malaysia. (2023). Risk Management in Technology (RMiT) Policy Document. https://www.bnm.gov.my.
2. Bank Negara Malaysia. (2024). Discussion Paper on Artificial Intelligence in the Financial Services Industry. https://www.bnm.gov.my.
3. Maybank. (2025). Maybank Forges Strategic Partnership with Microsoft to Accelerate Digital Transformation and AI-Driven Innovation. maybank.com.
4. Tech Wire Asia. (2025). ASEAN Banks AI strategies: How DBS, Maybank, and CIMB stack up against JPMorgans US 18B bet. techwireasia.com.