A peek inside the tech toolbox: How Asia’s life insurers are making use of Gen AI

Life insurers, long reliant on traditional processes, are rapidly adopting Generative AI as an ongoing technological transformation powers ahead.

And the impact is quickly becoming evident, with GenAI revolutionising the way insurers handle all aspects of the business, from handling underwriting, to risk assessment and claims processing. AI-driven automation is already reducing costs, improving efficiency and enhancing customer experiences.

But the companies that succeed in integrating this rapidly evolving technology into their operations are those that take a premeditated approach while accounting for challenges, including overcoming legacy systems and the slow pace of regulatory evolution.

AIA Group, Manulife and Ping An Insurance Group are among the large insurers tapping into AI to streamline operations. AI-driven approaches are, in one case, making it easier for policyholders to access and manage their life insurance policies. In another, the technology is speeding up claims while preventing fraud.

“We are seeing AI-driven straight-through processing (STP) for claims, which has increased efficiency across our markets,” noted Claudio Caula, Head of Group Data Analytics & AI at AIA Group in a recent column. A case in point is in South Korea, where claims that once took more than two days to process can now be completed in under 25 minutes.

Speaking to (Re)in Asia, Caula went on to explain that it can be difficult to overcome unexpected challenges, such as differences in payment processing times in disparate markets, which can be attributed to a variety of factors like integration issues with payment systems, any limitations that payment systems themselves might have, or the absence of any AI-driven adjudication and validation processes.

“This is a game-changer. It allows us to focus human expertise where it is most needed—on the more complex cases.”

Claudio Caula

Head of Group Data Analytics & AI at AIA Group

AIA’s Gen AI claims processing system facilitates payments in minutes in South Korea but the same is not true in other markets, not because the system can’t handle it but because payments processes in specific countries can’t keep up. “(South) Korea’s case is the best case,” said Caula.

For life insurers, speed and efficiency are critical. Policyholders expect claims to be processed quickly, especially in times of financial or medical need which is typically when their interactions with insurers happen. Traditional and time-intensive paper-based processes are giving way to AI-driven solutions with the potential to automate everything from document verification to fraud detection.

AIA has implemented a three-tiered AI system that enhances the life insurance claims process.

The first tier involves Optical Character Recognition (OCR) technology that allows customers to upload claim documents by simply taking a photo of them.

“Our AI system is capable of processing both typewritten and handwritten documents, capturing all required information with 97% accuracy,” Caula explained. “This functionality reduces the necessity for manual entry, thereby minimizing errors and enhancing efficiency.”

The second tier of AIA’s system uses Generative AI to summarize complex medical records, significantly cutting review time for assessors. In the past, human assessors had to manually go through medical reports, laboratory results and diagnostic scans to determine claims eligibility. Now, AI can scan, interpret, and provide concise summaries in minutes.

“This is a game-changer,” said Caula. “It allows us to focus human expertise where it is most needed—on the more complex cases.”

The third tier focuses on claims assessment and fraud prevention. For fraud prevention, AIA’s AI models analyze more than 20 risk indicators, identifying suspicious claims that may indicate fraud, waste, or abuse.

“We don’t automatically reject flagged claims, but we alert human assessors to investigate further,” said Caula. This proactive approach may strengthen risk management while speeding up the processing of legitimate claims.

At the same time, AI-based claims assessment can be accurate some 99.7% of the time for simpler claims, a rate that can be higher than human assessors, although it is hard to determine specific numbers.

A key driver behind the surge in GenAI adoption within the life insurance sector has been top-down leadership commitment to digital transformation. At AIA, Manulife, and Ping An, executive leadership has made technology-driven innovation a core strategic priority, recognizing its potential to reshape the industry.

This vision has been particularly evident in AIA’s extensive investment in AI, which stemmed from a company-wide initiative led by its management.

“Our senior leadership emphasized the importance of adopting AI as a key driver for efficiency and customer satisfaction,” said Caula. “This mandate accelerated our AI adoption, ensuring that we moved quickly from experimentation to real-world-implementation.”

By allocating significant resources to AI development and embedding it across multiple business functions, these companies have transformed their life insurance operations, achieving faster claims processing, improved risk assessment, and more personalized policy offerings.

GenAI is AIA’s key focus after its technology transformation. Manulife, for its part, has worked to embed AI across its entire life insurance value chain.

In 2024, the company launched ChatMFC, a proprietary AI assistant available to its entire global workforce. Designed to automate routine tasks, ChatMFC allows employees to focus on more complex, strategic work.

The insurer says that its GenAI capabilities has now been rolled out to 100% of its global workforce, with a 75% engagement rate.

“By embedding AI at scale, we’re not just optimising operations – we’re empowering colleagues to deepen customer relationships, improving advisor connections, and unlocking new revenue streams,” says Karen Leggett, Global Chief Marketing Officer, Manulife. “As a strategic enabler of long-term success, AI will continue to be a cornerstone of Manulife’s responsible and scalable growth.”

Manulife has also rolled out AI-driven risk assessment and policy underwriting tools. By leveraging machine learning and predictive analytics, the company can offer personalized life insurance policies based on real-time data. These AI systems assess an applicant’s risk profile with greater accuracy than traditional actuarial models, ensuring fairer pricing and better coverage options.

As part of its roadmap for 2025, Manulife said it is deepening AI-driven insights, enhancing personalisation for customers and advisors, and expanding investment in scalable, responsible solutions.

“AI is transformative,” said Jodie Wallis, Manulife’s Global Chief Analytics Officer. “By equipping our teams with AI tools, we’re enabling them to work smarter and move faster.”

Leaving legacy behind

But AI integration is not without challenges. Fast adoption may not always be the best way to go, despite widespread expectations of the rapid spread of AI-driven functions, slow and steady may be a better approach for many, particularly given that regulations and regulators are struggling to keep up.

Many insurers still rely on legacy systems that were never designed for AI integration. “One of the biggest barriers facing the industry is outdated technology,” said George Hindmarsh, Head of Sales, APAC at Clearwater Analytics. “AI can only be effective if it is fed with the right data inputs. Many insurers are still using spreadsheets to manage data, making it difficult to fully capitalise on AI’s benefits.”

Hindmarsh also pointed out that AI adoption raises concerns about bias and accuracy.

“There are risks attached to any new technology, particularly when AI is making hyper-personalized decisions,” Hindmarsh said. “That’s why human oversight remains crucial—to verify AI-driven decisions, detect anomalies, and maintain transparency in the decision-making process.”

Regulatory challenges are another factor slowing AI adoption. An Economist Impact Report noted that 75% of insurance executives cite a lack of understanding of the external environment, including regulations, as a major obstacle. “Insurers need to adopt better regulatory reporting tools to ensure compliance across different jurisdictions,” Hindmarsh said. “This is especially critical as AI becomes more embedded in investment and underwriting decisions.”

“AI can only be effective if it is fed with the right data inputs. Many insurers are still using spreadsheets to manage data, making it difficult to fully capitalise on AI’s benefits.”

George Hindmarsh

Head of Sales, APAC at Clearwater Analytics.

Then there are more practical technology concerns. For instance, OCR technology has evolved by leaps and bounds and it has become fantastic at reading fixed-form documents like IDs, but it can be less reliable when dealing with fluid documents like doctor assessments, said Caula.

“The challenge for issuers is the variety of medical invoice and document formats from different providers,” Caula said. “Each doctor, clinic and hospital has its own format. Adding to the complexity, most submitted documents are photos, not scans, with varying photo quality and lighting conditions.”

Caula said AIA’s new system represents a substantial competitive edge. Developed in-house by Caula and his team to address the company’s unique requirements, creating the system was a time-intensive process that yielded significant benefits. In practical terms, Caula noted that AIA was compelled to develop the solution internally because no external vendors could fulfill the company’s specific needs. “Given the complexity involved, no vendor offered a solution that could meet our requirements.”

AIA has in-house developers and a center of excellence in China with dedicated data scientists, so that is one advantage that other companies may not have. Another is support from top leadership.

Despite these hurdles, the future of AI in life insurance looks promising. AI-driven predictive analytics is expected to play a larger role in policy personalization, allowing insurers to tailor coverage to individual policyholders based on lifestyle factors, genetics, and health behaviors. Some companies are already experimenting with AI-driven dynamic pricing models, where premiums adjust in real-time based on policyholder activity, similar to how car insurers use telematics to price auto insurance.

Looking ahead, AI is also expected to improve the customer experience through advanced AI-powered virtual assistants. These systems can provide policy recommendations, explain coverage details, and even assist with retirement and wealth planning. AI-driven longevity models may eventually help life insurers better predict life expectancy, allowing them to refine pricing and offer more competitive products.

Ping An, one of China’s largest insurers, is taking AI beyond customer interactions and into workforce optimisation. The company’s Ping An Talent AI system uses machine learning to match job applicants to the right roles within the company. AI-driven recruitment tools analyse résumés, conduct video interviews and even negotiate salaries.

In announcing the system in early March, Ping An noted that it can provide for a flexible interview experience and make salary negotiations more efficient.

Using the technology, Ping An, alongside 10 of its subsidiaries, offered over 2,000 positions across eight major categories, the insurer says.

Changes from the roots up

And as AI adoption accelerates, it is reshaping not just how life insurance is sold and managed, but also how insurers operate internally. Companies that fail to invest in AI risk falling behind competitors that can process claims faster, assess risk more accurately, and deliver better customer experiences.

The life insurance landscape is evolving rapidly, and GenAI is playing a crucial role in shaping its future. While challenges remain, insurers that invest in AI-driven efficiency, fraud detection, and personalized risk assessment will be best positioned to meet the needs of policyholders in an increasingly digital world.

“There’s no doubt that AI is here to stay,” noted Caula. “The insurers that can effectively integrate AI into their operations will lead the industry into the next decade.”