The increasing popularity of artificial intelligence (AI) technology applications is rewriting the development logic of the insurance industry.

On the business side, the rise of intelligent driving, embodied intelligence (humanoid robots), and smart factories is putting forward a series of new requirements for the connotation of insurance protection services. On the risk control side, the associated risks arising from the popularization of AI technology, such as computing power risks, model security, data security, and industrial application responsibilities, require insurance companies to quickly find risk control response strategies. On the operational side, whether AI can significantly improve management efficiency for insurance companies is becoming a golden key for them to break through business development bottlenecks.

As the Vice President of China Pacific Insurance (Group) Co., Ltd. (referred to as "China Pacific Insurance"), Yu Bin deeply feels that the application of AI in the insurance field has become ubiquitous and always present.

On April 27, 2026, Yu Bin pointed out in an interview with the Economic Observer that this year, China Taiping Insurance has identified five categories and more than 30 first batch AI strategic projects through strategic decoding. At the same time, it will also establish five ecological joint construction laboratories, a three-level organizational structure, and a hierarchical classification decision-making mechanism to clarify the overall investment strategy of AI.

I expect that China Taiping Insurance's investment in AI will double year-on-year this year. ?He revealed. Behind this is a major transformation that insurance companies are undergoing from "+AI" to "AI+". Don't underestimate the subtle differences between the two, as the latter will bring fundamental changes to the logic and development model of the insurance industry. "Yu Bin said that compared to"+AI "which is adding engines to carriages," AI+"is building airplanes.

Photo provided by the interviewee

AI associated risks: challenges and opportunities coexist

Economic Observer: With the increasingly widespread application of AI in the insurance industry, what new changes will AI bring to insurance services? Will there be new accompanying risks on this journey of change?

Yu Bin: Currently, we have seen that AI is having three impacts on insurance services.

One is the universal application of artificial intelligence, which is changing consumers' cognition and needs. For example, residents' demand for insurance is shifting from basic life insurance protection to a comprehensive demand for "health management+wealth planning+inheritance services", and the requirements for service professionalism and response efficiency are also significantly increasing. In addition, more and more residents' demand for insurance has shifted from "passive purchase" to "instant satisfaction". Therefore, if insurance institutions cannot achieve intelligent shopping guidance, automatic claims processing, and 7x24 hour proactive services through AI, they will quickly lose the trust of the younger generation of customers, leading to the marginalization of market competitiveness.

Secondly, artificial intelligence is driving the process reshaping, model upgrading, and cost optimization of insurance companies. In the past, insurance agents had to manually organize KYC (Know Your Customer) information, search for information, benchmark gold medal cases, etc. when they returned to the office after meeting with clients, which consumed a lot of energy and time. Now, they can use AI to complete intelligent data entry, 360 degree customer profiling analysis, and draw marketing experience from top sellers on their mobile devices. In this way, they can free up more time, serve more customers, and achieve an increase in per capita production capacity. In addition, in the process of accident investigation, underwriting, and compensation, insurance companies have traditionally adopted the practice of "master apprentice" work experience inheritance, which takes a long time and the effect is unknown. Now, through AI deep learning technology, we can transform the experience of these experienced professionals into practical intelligent Q&A and homework process assistance, helping numerous employees better complete these tasks.

Thirdly, we must acknowledge that artificial intelligence brings new quality productivity to insurance services, while also giving rise to a series of new associated risks. For example, new risks in areas such as computing power risk, model security, data security, and industrial application responsibility. I think this is not only a challenge, but also an opportunity. If insurance institutions do not immediately deploy AI, they will be unable to understand and quantify these new risks, thereby losing their first mover advantage in pricing and coverage of "AI+industry" insurance products, and will inevitably lose their core voice in the digital economy era.

Economic Observer: We have also noticed that the rise of AI technology is driving the flourishing development of intelligent driving and embodied intelligence industries. The demand for insurance protection in these fields is increasing. How should insurance companies define the risk exposure of these new technologies and build forward-looking risk response strategies?

Yu Bin: While new technologies such as intelligent driving and embodied intelligence create enormous commercial value, they also bring about "AI associated risks" that run through the entire lifecycle. These risks have characteristics such as rapid dynamic evolution, complex liability determination, and difficulty in quantifying losses.

Taking embodied intelligence as an example, it has the characteristics of high technological barriers, novel application scenarios, and special risk modes. The market generally has concerns about "not daring to use, fearing damage, and unable to afford compensation", which has become a major bottleneck restricting the development of embodied intelligence industry. In this situation, the need to hedge industry risks through insurance mechanisms and help commercialize embodied intelligent technologies has become urgent. But we also see that insurance intervention in the embodied intelligence industry provides risk protection, but also faces challenges such as insufficient long-term risk data accumulation in the humanoid robot industry, lack of mature rate pricing reference system, and accelerated iteration of humanoid robot technology leading to constantly dynamic changes in risk characteristics, further increasing the difficulty and uncertainty of insurance pricing.

In response to the new risk exposure brought by these new technologies, insurance companies not only regard insurance as a financial compensation tool, but also position insurance as an important "safe haven" to serve the real economy and safeguard digital security. Through three measures, they build forward-looking risk assessment and product design capabilities.

One is to establish industry standards and build a quantitative defense line from identification to evaluation. By establishing a feasible risk classification and hierarchical evaluation system, we need to classify and break down the risk elements of these new technologies according to areas such as computing power risk, algorithm risk, data risk, and application responsibility risk. We not only establish standards, but also develop corresponding risk assessment systems and model tools at the practical level to ensure that risks are measurable and preventable. This data-driven "objective quantification" will provide scientific basis for product pricing, avoid pricing inaccuracies caused by cognitive biases, and make risk management more systematic.

The second is to upgrade the service paradigm, shifting from "post compensation" to "full lifecycle reduction". Traditional insurance business often stops at "post accident claims", but in the fields of intelligent driving and embodied intelligence, insurance companies should prioritize the "source of risk occurrence". Before underwriting, we conduct risk assessment and compliance diagnosis of the enterprise's intelligent system through professional risk assessment services, and use the assessment conclusions as the basis for underwriting pricing; In underwriting, we provide continuous risk reduction support to assist enterprises in improving the security level of model algorithms, optimizing performance, and eliminating hidden dangers in their infancy. This mechanism of "pre insurance evaluation, mid insurance intervention, and post insurance claims" enables insurance companies to transform from mere economic compensators to partners in enterprise security governance.

The third is to deepen ecological collaboration and create an innovative cluster of "industry university research application" linkage. The risks brought by new technologies cannot be solved by the insurance industry alone. To this end, China's Taiping Insurance is actively building a cross industry and cross domain "ecological community".

The value logic of the insurance industry is undergoing a leap forward

Economic Observer: With the continuous popularization of AI technology, we are seeing more and more intelligent factories operating efficiently through intelligent systems. What changes will this bring to traditional products such as enterprise property insurance and liability insurance? Will traditional risks be replaced by occasional new risks, and how will insurance companies respond to this new situation?

Yu Bin: With the intelligent reconstruction of production methods in various industries by AI technology, the value logic of the insurance industry is also undergoing a historic transition from "physical asset compensation" to "digital system protection".

In recent years, China Taiping Insurance has gained a deep understanding of this risk evolution trend and plans to strategically restructure through three dimensions to address the new challenges of the "unmanned and intelligent" era:

One is to redefine the asset boundary and upgrade the strategic value from "protecting physical assets" to "protecting resilience". In highly automated unmanned factories and enterprise intelligent operation and maintenance systems, the physical damage risk that traditional enterprise property insurance focuses on will significantly decrease with the improvement of equipment intelligence and the reduction of human errors. However, in contrast, the "AI associated risks" and "business continuity risks" caused by AI model performance failure, intelligent equipment misoperation, and technical infrastructure service interruption are becoming the core of new risks. Therefore, we are shifting the focus of insurance protection services from compensating for physical asset losses to providing comprehensive protection for enterprise digital resilience. By developing new insurance products and services that mitigate risks associated with technology, we are delving into risk assessment dimensions such as algorithm performance, system intrinsic security, and emergency recovery of automated production lines. The transformation of enterprise property insurance not only ensures the continuity of insurance functions in the context of intelligent production, but also upgrades insurance companies from asset "damage compensators" to "defense guardians" of enterprise operation logic, thereby achieving value reconstruction while ensuring the digital survival capability of enterprises.

The second is to upgrade the pricing paradigm, transforming it from an actuarial paradigm based on empirical statistics to one relying on simulation testing. The traditional insurance pricing logic relies on statistical analysis of historical accidents. However, in the face of highly sporadic and unpredictable systemic failures in smart factories, traditional data accumulation often faces a "failure dilemma". We are committed to building an "AI associated risk assessment system and model", using simulation testing technology to conduct comprehensive risk assessment tests on production processes in the digital space. By deducing algorithm performance and failure scenarios in extreme scenarios, we can accurately quantify low probability and high impact risks. Through this paradigm shift from "data statistics" to "scenario computing", we can construct a scientific and dynamic rate pricing system, effectively resolving the pricing difficulties faced by insurance in the face of new technological risks, and ensuring that even in situations where risk forms are highly uncertain, we can provide an insurable foundation for cutting-edge technologies.

The third is to extend the depth of services and build an ecological governance system from "single point claims compensation" to "full chain risk management". In the era where AI robots replace humans, the core significance of insurance is to nip potential systemic risks in the prevention stage. Therefore, we are extending our service tentacles to the front end of the production link, and providing enterprises with a package of risk reduction services covering "AI security compliance audit, network security reinforcement, and real-time risk monitoring" by establishing in-depth ecological cooperation with algorithm security review agencies, industrial Internet platforms, and third-party technical partners. I think this means that insurance contracts are no longer a cold policy, but a governance plan that includes proactive risk management commitments - by deeply integrating insurance services into the operational lifecycle of smart devices, achieving "participatory risk management" of insurance on the production process.

Economic Observer: China Taiping Insurance has clearly stated that "AI+" is one of the three major strategies, and the budget investment in AI will continue to increase. Behind this strategic upgrade, what insights does China Taiping Insurance have for the future AI transformation of the insurance industry? Why is the transformation of insurance institutions towards AI becoming so urgent?

Yu Bin: The essence of China Taiping Insurance's "Artificial Intelligence+" strategic upgrade is the deepening of the "Digital Taiping Insurance", which is driven by three main factors:

One is the endogenous demand for transitioning from "scale growth" to "value creation". The traditional insurance business model highly relies on human driven and offline touchpoints, with high marginal costs and low reach efficiency. Through the "Artificial Intelligence+" strategy, we aim to break through this growth bottleneck, fully utilize the computing power advantages of AI technology in personalized pricing, intelligent underwriting, and precise pricing, effectively reduce operational friction costs, continuously improve underwriting quality and profitability resilience, and achieve a paradigm shift from "low profit, high-volume sales" to "refined management".

The second is to build a differentiated "moat" under stock competition. Homogenization of insurance products has always been a major obstacle to the high-quality development of the industry. We are attempting to transform from a single risk protection provider to a full scenario "risk management expert" through the implementation of the "AI+" strategy. By deeply integrating AI into life insurance, property insurance, health insurance, investment, pension and other sectors, we provide real-time data and intelligent driven business decisions, differentiated customer service, and efficient and low-cost operational control, achieving a paradigm shift from "post compensation" to "full cycle service" and calmly responding to industry stock games.

The third is to proactively respond to compliance challenges and social responsibilities in the digital age. In recent years, regulatory policies have raised higher standards for the compliance and transparency of information disclosure in life insurance sales. We have elevated AI to a strategic level, not only to ensure stable and compliant operations in complex regulatory environments, but also to support social stability through technological means and demonstrate the sense of responsibility of insurance as the cornerstone of national finance.

Economic Observer: Insurance companies have always been committed to improving internal management efficiency, building stronger insurance service capabilities and differentiated development capabilities. What specific impacts will the deep application of AI technology bring to insurance companies to break through internal management bottlenecks?

Yu Bin: Empowered by AI technology, the intelligence of internal processes will be the path to solving industry efficiency bottlenecks. The empowerment of internal management by AI is not only about improving efficiency, but also about completely restructuring the profit logic through automated underwriting decisions, intelligent anti fraud, and personalized pricing models. Especially in the era of stock competition in the insurance market, leaders can optimize their cost structure through AI, while laggards who still adhere to traditional operating models will face exponential growth in operational pressure and even the risk of being eliminated.

For example, China Pacific Insurance has strengthened overall coordination and improved management efficiency through intelligent review tools in team management. The intelligent review meeting tool combines the team's morning meeting system, intelligently forming team review, customer review, production and service review, activity review, minute level analysis of front-line hotspots, monthly extraction of a large number of typical cases, and helping the entire team improve work collaboration efficiency and performance management efficiency. In addition, the headquarters can also use intelligent review tools to timely identify the problems faced by frontline business implementation strategies, promote excellent cases from the frontline, and help improve the overall insurance service capabilities of the team.