The discovery of customer intention from dialogue plays an important role in automated support system. However, traditional text clustering methods are poorly aligned with human perceptions due to the shift from embedding distance to semantic distance, and existing quantitative metrics for text clustering may not accurately reflect the true quality of intent clusters. In this paper, we leverage the superior language understanding capabilities of Large Language Models (LLMs) for designing better-calibrated intent clustering algorithms. We first establish the foundation by verifying the robustness of fine-tuned LLM utility in semantic coherence evaluation and cluster naming, resulting in an accuracy of 97.50% and 94.40%, respectively, when compared to the human-labeled ground truth. Then, we propose an iterative clustering algorithm that facilitates cluster-level refinement and the continuous discovery of high-quality intent clusters. Furthermore, we present several LLM-in-the-loop semi-supervised clustering techniques tailored for intent discovery from customer service dialogue. Experiments on a large-scale industrial dataset comprising 1,507 intent clusters demonstrate the effectiveness of the proposed techniques. The methods outperformed existing counterparts, achieving 6.25% improvement in quantitative metrics and 12% enhancement in application-level performance when constructing an intent classifier.

Insurers are no stranger to large-scale technology changes, but 2020 was unique in the magnitude of change. Incumbent carriers have spent much of 2020 year moving complex IT infrastructures into the cloud and instituting new remote risk assessment technologies, in order to continue serving their customers safely and efficiently. While individual lines of P&C insurance have experienced differential impact from COVID, carriers generally appear to be coming through the pandemic with their financial health intact, as well as an enhanced appetite for digitization. Personal Auto has become more profitable due to less overall miles driven, Business Owners' Policies (BOP) are navigating potential litigation regarding business interruption, whereas homeowners' insurance has seen a relatively muted impact. Overall, carriers have been compelled to accelerate certain digitization initiatives, and have managed them relatively effectively, resulting in an accelerated motivation and increased confidence in their need and desire to adopt emerging digitization technologies. As carriers rapidly increase their comfort level with digitization, what emerges is an increasingly mounting need to sift the mounting piles of available data for actionable insights, in other words: finding more needles, not creating more haystacks.

Dedicated to pioneering the AI-empowered property insurance sector while offering perfect customer service, Ping An Property Insurance, as one of "Leading Biosphere Companies", is aiming to unleash the potential of artificial intelligence with its acute insight into the emerging technology, leveraging AI application and big data systems to transform the insurance industry. The FACEKYD extended the traditional driving risk ranking algorithm by leveraging state-of-the-art deep learning networks to extract facial driving risk factor. Different from the face recognition algorithm, the FACEKYD algorithm can not only differentiate the driving risk from different customers, but also keep the risk scores from the same customer stable by using the innovative rank algorithm "tetrad ranking". Another star product of the company currently under development is DRVR (Driving Risk Video Recognition). DRVR technology combines FACEKYD and DMS technology, through the identification and analysis of driving behavior, and combining the FACEKYD auxiliary driving risk prediction, to manage the risk in the whole process of driving and active warning, provide a variety of interim risk management solutions, rather than a single post-event compensation According to the Ping An Property Insurance technology center, the technology, with the bolster of database encompassing people, cars and roads, can identify hazardous driving behaviors including drowsy driving, smoking, looking at phones, dangerous lane-changing and speeding.

Big data and artificial intelligence are at the heart of a platform announced last week by ZhongAn Online Casualty and Property Insurance, China's first internet-only insurer. "Mobile internet and digital technologies are transforming the car industry. So the car insurance industry must change as well," Wang Yu, head of car insurance at ZhongAn, said in an interview with the South China Morning Post. The platform will include companies along the car value chain and provide a one-stop shop for buyers and owners. "Imagine shopping for your car and you can take care of matters related to car purchase insurance, loans, after purchase services, investment management and usage-based car insurance (UBI) during one visit," said Wang.