APAC Insurers Embrace AI Despite Tech Hurdles

Asia-Pacific Insurers Sprint Ahead on AI While Others Hesitate

A fresh Moody's survey has shattered assumptions about sluggish insurance innovation in Asia-Pacific. Among 21 insurers across China, Hong Kong, Japan, Korea, and Taiwan, more than 80% are already using AI and seeing tangible benefits.

This isn't tentative experimentation. China Life Insurance and its peers are embedding AI into core operations, with Chinese insurers reporting the most diversified benefits across the region. The message for insurers in Singapore, Vietnam, Australia, or Malaysia is stark: regional competitors have moved beyond testing into mature deployment phases.

Motor and Health Lead the AI Charge

The survey reveals specific patterns in how insurers deploy artificial intelligence. Marketing, sales, and distribution top the use-case list, with firms using AI to identify prospects, optimise channel strategies, and personalise offers.

Motor insurance and health insurance dominate AI adoption among product lines. The reason is straightforward: massive data volumes from vehicle telematics, claims histories, and health records provide rich training material for AI models.

Chinese insurers appear furthest ahead, deploying AI not just in customer-facing functions but increasingly in underwriting, claims automation, and product innovation. For insurers across APAC's enterprise AI surge, these are valuable benchmarks for efficiency gains and enhanced customer experience.

"Regulators need to gain a deep understanding of AI to make informed policy decisions. Talent development and upskilling in the public and private sectors are crucial in the effective regulation of AI." Lucy Wong, Adviser, BIS Innovation Hub Hong Kong Centre

Data Quality Emerges as Primary Barrier

Despite widespread adoption, significant friction points remain. Data quality and accessibility ranked as the main barrier to AI deployment among survey respondents. Fragmented, outdated, or inaccessible data undermines even well-designed AI systems.

Model transparency concerns compound the challenge. Many insurers remain uncomfortable with 'black box' algorithmic decisions, particularly around underwriting or claims resolutions. This mirrors broader concerns about AI integration challenges across Asia's tech boom.

For non-adopters, two clear blockers emerged:

• Lack of skilled talent capable of building or operating AI systems
• Need to modernise IT infrastructure, ensure data compatibility, and eliminate obsolete technology silos
• Regulatory compliance requirements that are still evolving

Many Asian insurers operate with decades-old core systems, making AI integration particularly challenging. This points to a phased approach: prioritise data infrastructure and talent development before scaling AI use cases.

Regulatory Complexity Adds Governance Layer

The evolving regulatory landscape creates additional complexity across APAC markets. Regulators are actively reviewing frameworks around AI, cybersecurity, model governance, and data usage.

This dual challenge of technological transformation plus regulatory compliance means insurers must execute AI initiatives while ensuring dynamic rule adherence. Model explainability and audit trails are becoming increasingly important governance requirements.

"Insurers are considering including clearer language around AI risks across a range of policies to better understand the total cost of risk. However, the wordings could prove challenging given that AI is constantly evolving." Paige Cheasley, Canada National Technology Practice Leader, Gallagher

Recent developments highlight the pace of innovation. In June 2023, Simplifai launched InsuranceGPT, the first proprietary GPT tool for insurance via its no-code AI platform. AI Inside Inc. unveiled a solution using OCR-digitised health certificates to develop new life insurance products in January 2023.

Strategic Roadmap for Regional Insurers

Given these findings, insurers might consider a three-pronged approach. First, prioritise high-impact use cases where data volumes are substantial and outcomes measurable, particularly in motor and health insurance alongside marketing and distribution functions.

Second, invest in data infrastructure before AI deployment. Clean, accessible data anchored in modern systems is essential, alongside governance frameworks and internal talent development. This aligns with broader patterns in how executives are approaching generative AI adoption across Asian markets.

Third, treat regulation as integral to strategy rather than an afterthought. AI governance, model transparency, data ethics, and cyber-risk management must be built into roadmaps from the start.

For markets including Singapore, Malaysia, Indonesia, and Australia, the survey provides clear instruction: regional peers are already embedding AI and realising competitive advantages in efficiency, customer experience, and product innovation.

What specific AI applications show the highest ROI for insurers?

Marketing and distribution functions demonstrate the strongest returns, followed by claims processing automation in motor and health insurance. These areas combine high data volumes with measurable efficiency gains and customer experience improvements.

How are Chinese insurers outpacing regional competitors?

Chinese firms deploy AI across broader functions, extending beyond customer-facing applications into underwriting, claims automation, and product innovation. Their diversified approach creates multiple value streams rather than single-point solutions.

What infrastructure investments are essential before AI deployment?

Data quality improvement, legacy system modernisation, and talent acquisition form the foundation. Governance frameworks and regulatory compliance mechanisms must be established alongside technical infrastructure to ensure sustainable AI integration.

How do regulatory requirements impact AI implementation timelines?

Evolving compliance frameworks across APAC markets add 6-12 months to implementation cycles. Model explainability requirements and audit trail capabilities must be built into systems from the design phase rather than retrofitted.

Which product lines offer the best AI opportunities?

Motor and health insurance lead adoption due to rich data sources including telematics, claims histories, and health records. These product lines provide clear AI training opportunities and measurable outcome improvements.