Asian insurers are integrating AI in their operations to provide error-free, faster and satisfactory solutions that manage risks better. Ensuredit Technologies’ Mr Amit Boni speaks with Asia Insurance Review how the insurance sector in different parts of Asia are embracing AI and the value propositions they see.
In Asia, AI adoption by insurers varies significantly by country.
For instance, India is in the early stages of AI adoption, though the pace at which AI is being embraced is quickening. Insurers are increasingly keen on AI for fraud detection, customer service automation, claim processing, and predictive analytics.
Over in China, insurers are heavily investing in AI in underwriting, customer service (via chatbots and virtual assistants), claims processing and risk prediction. Government support of AI technologies and the sheer size of the market makes China a pioneer in this space.
As far as the rest of Asia is concerned, AI adoption varies significantly, with countries like Singapore, Japan and South Korea at the forefront. Most of Southeast Asia is still catching up, though there is growing interest in AI-based solutions like underwriting automation, customer engagement and claims management.
Speaking with Asia Insurance Review, Ensuredit Technologies founder and CEO Amit Boni said, “China is likely to remain the biggest market for AI in insurance over the next three years due to the combination of government support, technological infrastructure and the size of the market. India is also emerging as a significant market; especially as digital transformation accelerates but China’s scale and adoption trajectory give the East Asian country an edge.
Rapid adoption
Mr Boni said, “There are three key areas in Asia where we are seeing adoption increasing rapidly – customer service and customer experience (CX) management, leads and claims processing and fraud detection.
“In customer service and CX management, AI-driven chatbots and virtual assistants are widely used for customer interaction, reducing response times and improving efficiency. AI in leads and claims processing helps speed up the claims process by automating document processing, damage assessments and fraud detection. AI and automation should be used to reduce the time it takes for customers to receive claims payouts, enhancing customer satisfaction.”
“In fraud detection, AI algorithms analyse patterns in claims data to detect fraudulent activities. This is particularly relevant in regions with high fraud risks.”
He said, “Customer engagement and onboarding should be streamlined to ensure the customer’s experience from policy purchase to support, with a strong focus on seamless, intuitive, experiences like native insurance on WhatsApp.”
WhatsApp native insurance refers to WhatsApp-based insurance services like getting insurance quotes, buying insurance or submitting claims.
He said, “We also anticipate much wider acceptance or proof of concept (PoC) of AI in reinsurance as well as underwriting.”
Value propositions
Mr Boni said, “The AI roadmap will increase across a wider array of insurance use cases including co-pilots for selling and buying insurance products.”
Speaking about what offers the best value for insurers in investing in AI, Mr Boni said, “The clearest and most proven value proposition today is customer service. AI-powered chatbots and virtual assistants provide cost-effective, 24/7 customer service, improving customer satisfaction while reducing the need for human agents.”
Insurers also need to invest in personalisation by using AI to provide personalised insurance solutions, including product recommendations, based on customer data, thereby improving the relevance of products offered.
“Effective cost savings are significant and deliver very high ROI on the AI investment.”
He added that another value proposition is around claim adjudication, with significant reduction in turnaround times, and right decision-making thereby improving customer experience significantly.”
Less immediate impact
Human-intensive processes that include functions which require significant human judgment, empathy, or personal interaction (e.g., complex claims negotiations or legal advice) may not benefit as much from AI. Regulatory compliance and legal functions are where AI can assist with data processing; however, regulatory interpretation and legal decision-making often require human expertise. These are operations where AI may have less immediate impact.
Mr Boni said, “Insurers also face several challenges in the adoption of AI in Asia. These would include:
- Data privacy concerns: The sensitivity of customer data, especially in markets with strict regulations like India and China, poses challenges in AI adoption.
- Lack of skilled talent: There is a shortage of data scientists and AI experts, which hampers the successful deployment of AI solutions.
- Integration with legacy systems: Many insurers rely on outdated IT infrastructure, making it difficult for them to integrate AI technologies smoothly.
- Cost of implementation: High initial investment and unclear ROI can discourage some insurers, especially in emerging markets.
He said, “For the wider adoption of AI in the Asian insurance industry, insurers need to ensure adequate investment in data infrastructure to enable high quality, structured and secure data to train AI models effectively.
“Insurers should also be looking at collaboration with technical startups and research institutions to enhance AI capabilities and share knowledge. Also fostering AI expertise within the insurance sector through education and training programmes should be a priority.”
Regulatory support
Mr Boni said, “Clear regulatory frameworks to ensure AI deployment is both safe and beneficial. Government policies in Asia vary by country. China has an early lead with highly supportive policies. India is still evolving in terms of AI regulations, but initiatives like the National Strategy for Artificial Intelligence indicate increasing support.
“In other parts of Asia, such as Japan and Singapore, there is growing government backing for AI in insurance, with clear frameworks and funding incentives for innovation.”
Speaking about some of the regulatory gaps with reference to AI in insurance, he said, “The regulatory space is evolving as the AI landscape evolves and solves more problems in the industry. In general, the biggest conversation will be about data privacy. Regulated industries may want ‘on-prem’ deployments rather than cloud native deployments.
“There are insufficient or inconsistent data protection laws across different regions, particularly in South and Southeast Asia.”
More affordable premiums
In spite of the use of technology in underwriting and claim settlement, insurance premiums are rising.
Mr Boni said, “While technology should theoretically lead to lower costs, rising premiums can still be attributed to various other factors. Increasing risks like natural disasters, surging lifestyle diseases and health crises and new risks like cyber risks, drive up premiums.
“Claims frequency and incident severity also impact insurance premiums. As more claims are processed and the industry faces rising loss ratios, premiums tend to rise despite technology-driven efficiencies.”
He said the approach to making insurance affordable should include the following:
- Focus on AI-driven risk segmentation: Offering tailored premiums based on more accurate risk assessments through AI and data analytics.
- Cost-effective claims management: Using AI to streamline claims handling and reduce fraud, lowering operational costs that can be passed onto consumers.
- Enhanced education and information access for customers: Using technology to offer clearer, more transparent policy options and help consumers make informed choices, which can increase satisfaction and trust. A