Gao Fei, CTO of Thailand KWI Public Insurance Group: Looking at the future development direction of insurance intelligence from Sora

Gao Fei, Chief Technology Officer (CTO) of KWI Public Insurance Holding Group, Thailand

In recent years, the boundaries of AI technology have been pushed forward, and the gradual development of this technology has made significant progress over the past few years. From text to images to video, intelligent models are becoming more complex and have a wider range of applications. These technological breakthroughs not only demonstrate the rapid progress in the field of artificial intelligence, but also reveal its potential to disrupt traditional human perceptions and ways of working. Today, AI technology is capable of generating photorealistic images, capable of producing cinematic effects-like videos, demonstrating the possibilities of innovation and providing new impetus to all walks of life.

Especially in financial industries such as insurance, the application of these technologies is opening up new business models that improve operational efficiency, reduce costs, and enhance the customer service experience. From risk assessment to claims processing, the application of intelligent models makes insurance business processes more automated and accurate, greatly improving operational efficiency and decision-making quality. In addition, through in-depth analysis of customer needs and market trends, intelligent models can help insurers develop more personalized products and services to further enhance their competitiveness.

The purpose of this article is to explore the application of intelligent models in the insurance industry and their potential impact. Through in-depth analysis of technical characteristics, industry application cases and future trends, we hope to provide valuable insights and thinking for intelligent applications in the insurance industry, so as to contribute to the innovation and development of the industry.

1. Overview of the development of image and video technology

Before the rise of deep learning, the field of image processing relied heavily on a series of predefined algorithms and mathematical models to process and analyze images. Steps such as image enhancement, image restoration, color image processing, etc., are fundamental components in any typical digital image processing process. However, these methods often rely on manually extracted features and rules, which limits the processing complexity and generalization ability.

With the rise of deep learning, especially the development of convolutional neural networks (CNNs), image processing technology has undergone a revolutionary transformation. Deep learning enables models to automatically learn complex representations of data, greatly improving the performance of tasks such as image recognition, image generation, and video processing. Early deep learning models such as AlexNet and later developments, such as image super-resolution (SRCNN models) and image inpainting techniques, have shown the power of deep learning for image quality improvement and recovery of damaged images.

In particular, advances in video processing technology have been remarkable. Deep learning technologies such as generative adversarial networks (GANs) have made the generation and editing of video content more efficient and realistic. These technologies not only demonstrate the power of deep learning in theory, but also show great potential in practical applications such as automated video editing, virtual reality, and augmented reality.

At the forefront of this field, OpenAI has developed DALL· The E and GPT series models, as well as Gemini launched by other research institutes and companies such as Google's DeepMind, demonstrate the tremendous advancements that AI can make in understanding and creating visual content. This year, OpenAI further launched the Sora video recognition and generative model, which not only marks that large model research has reached a new height, but also opens up new business models and application scenarios for various industries in the future.

In summary, the introduction and application of deep learning and large model technology in the field of image and video processing has brought a qualitative leap compared with the previous methods that relied on predefined algorithms and manual feature extraction. These advancements have not only changed the way visual information is processed and understood, but have also opened up new avenues for future technological innovation and industrial applications.

The first is automatic feature learning: deep learning models can automatically learn complex feature representations from large amounts of data without human intervention, significantly improving efficiency and accuracy.

The second is enhanced image quality: through deep learning technologies, such as image super-resolution (SRCNN) and image inpainting, image quality can be greatly improved, damaged images can be restored, and even high-quality images can be reconstructed from low-resolution images.

Third, innovation in video processing: The application of deep learning in video generation and editing, such as Stability Diffusion, has made the creation of video content more natural and realistic, opening up new creative and commercial application scenarios.

Fourth, the ability to deal with complex tasks: Early image processing technologies faced challenges when processing complex or blurry images, and deep learning technology can effectively cope with these challenges and provide more accurate and reliable results through its powerful pattern recognition and learning capabilities.

These technological advancements have not only advanced image processing technology, but also provided powerful tools for automation, improved accuracy, and new services for multiple industries, including insurance, healthcare, and security surveillance. The continued evolution of deep learning models heralds the ability to solve more complex image and video processing tasks, while bringing unprecedented changes and opportunities to industries such as insurance.

2. Mainstream application scenarios of intelligent technology in the insurance industry

In today's digital age, the insurance industry is facing increasing challenges, one of which is how to conduct risk assessment and claims processing more efficiently and accurately, as well as bring smarter services to customers. Fortunately, advances in technology offer new possibilities for solving these challenges.

1. Risk assessment

An important application of image recognition technology in the insurance industry is to improve the accuracy and efficiency of risk assessment. In the traditional risk assessment process, insurers rely on data and information provided by customers, a time-consuming and error-prone process. However, the application of image recognition technology has revolutionized this process. By using advanced algorithms to analyze images submitted by customers, insurers are able to automatically identify details in the images, such as the condition of the roof of the house, the extent of damage to the vehicle, and more, which are key factors in assessing risk and pricing. This not only reduces the time and cost of manual assessments, but also significantly improves the accuracy of assessments, allowing insurers to process risk assessment requests from insurance customers faster and fairerly.

2. Claims Processing

In terms of claims processing, the application of image recognition and generation technology has greatly improved the processing speed and customer satisfaction. By allowing customers to upload photos or videos directly from the scene of the accident, the AI system can quickly identify the severity of the damage, automatically determine the reasonableness of the claim, and even estimate the amount of the claim. This automated processing not only significantly shortens the time it takes to settle claims, but also increases customer satisfaction as it reduces communication costs between customers and insurers and speeds up the claims payment process. In addition, systematic and accurate estimation also means that the risk of overestimating or underestimating compensation is reduced, ensuring the fairness of the payout.

In the field of auto insurance, property and casualty insurers widely use image recognition technology, especially in the rapid assessment of losses after accidents. Customers only need to use their mobile phones to take photos of the damaged vehicle, and the system can automatically analyze the extent of the damage and repair costs, and quickly determine the reasonable range of claim amounts. This technology greatly reduces the claims processing time and workload, while alleviating the traffic congestion caused by urban traffic accidents to a certain extent.

3. Customer Service

GPT technology is also widely used to improve customer service efficiency. By training specialized models, insurers are able to create virtual assistants that understand natural language and can automatically answer customers' frequently asked questions and provide customers with some basic services. With the introduction of image recognition and generation technology, it can not only handle simple basic services, such as informing customers how to fill in and prepare insurance and claim information by generating images, but also guide customers to submit claims quickly and efficiently. Virtual assistants can identify and analyze user-uploaded images to instantly provide an initial damage assessment, or guide customers through the next steps in the claims process, or even provide an instant claims assessment in some cases. Not only does this improve the efficiency of customer service, but it also allows insurers to make better use of human resources and focus on complex cases that require more human intervention.

3. Successful case studies

In the insurance industry, the rise of smart technology has led a wave of revolutionary applications. Through several case studies, this article will delve into how domestic and foreign insurers can use these advanced technologies to solve pain points in real business and transform scientific and technological achievements into effective insurance solutions.

1. Solving the Problem of Unstructured Document Recognition with Deep Learning: AXA CZ/SK

AXA CZ/SK applied a deep learning platform to automatically extract information from unstructured scanned documents, an advancement that highlights the ability of AI to streamline the data processing process. Compared with the cumbersome manual operations such as scanning, entering, and reviewing unstructured information in the traditional process, the application of this technology shortens the time to complete information archiving from days to seconds. This innovation not only significantly speeds up the processing of customer applications and services, but also significantly reduces operating costs and increases customer satisfaction.

2. Improve risk control capabilities through fraud prediction technology: Anadolu Sigorta

Anadolu Sigorta, an insurance company in Turkey, has demonstrated the power of AI technology in the fight against insurance fraud by introducing an advanced predictive fraud detection system. The AI-powered system enables real-time fraud detection, dramatically reducing the time-consuming process that previously relied on manual review. Not only does this strategy dramatically improve productivity, but it also delivers significant economic benefits, including increased ROI and millions of dollars in cost savings in fraud detection and prevention.

3. Innovation on the insurance product side to promote the comprehensive promotion of AI in various industries: Munich Re's aiSure

Munich Re's aiSure is a revolutionary insurance product designed for AI technology providers to provide performance guarantees, which marks the convergence of insurance and technological innovation. By providing insurance coverage for AI applications, aiSure solves the market's trust in AI technology and greatly improves end-user confidence. This innovation not only strengthens the market position of AI technology providers, but also provides strong support for the growth of the entire industry.

A typical success story is in the medical field. A startup that uses AI technology to assist in diagnosing diseases has successfully strengthened the trust of partner hospitals and patients in its AI diagnostic system through the insurance product provided by aiSure. This not only boosts the company's product sales, but also promotes the wider application of AI technology in the field of medical diagnosis.

Another example is that of an autonomous driving technology provider that has demonstrated the safety and reliability of its autonomous driving system to partners and consumers through aiSure's performance guarantee. This strengthens the willingness of partners to cooperate and the market's acceptance of autonomous vehicles, opening up a larger market space for the company.

Through these applications and success stories, aiSure not only proves its worth, but also shows how insurance products can support and facilitate technological innovation and its application across industries. With the further development and maturity of AI technology, aiSure and similar insurance products will play an increasingly important role in driving technological innovation and application.

4. Integrate comprehensive data to manage risk: AXA's building ecosystem

AXA's building ecosystem provides a comprehensive view of risk management on the construction site by integrating multi-source data such as images, wearables, and sensors. The system's automated data collection and analysis capabilities effectively overcome the slowness and inaccuracies of the traditional manual risk assessment process, thereby improving the efficiency and accuracy of risk management.

AXA's case study of the construction ecosystem highlights how the insurance industry is using modern technology to solve traditional problems and create new business models and processes, which has far-reaching implications not only for AXA itself, but also for the construction and insurance industry as a whole.

5. Analysis of success factors

The success of these cases can be attributed to three key factors: technological innovation, where the innovative application of smart technology demonstrates the insurance industry's commitment to adopting cutting-edge solutions, and understanding customer needs, each of which reflects a deep understanding of customer needs, whether it's improving data processing speed, Whether to enhance fraud detection or provide more accurate risk assessments, and business model flexibility, these examples demonstrate insurers' ability to adapt their business models to ensure they remain competitive and respond to market demands when new technologies are adopted.

Taken together, the above case studies demonstrate how smart technology can create value in the insurance industry, solving industry problems by improving efficiency, accuracy, and customer satisfaction. By innovating, understanding customer needs and adapting business models in a timely manner, insurers are setting new standards for operational efficiency, risk management and customer service.

Fourth, intelligent technology will change the future of the insurance industry

With the continuous advancement of technology, it will not only solve business pain points in the future, but also further change the business model of the insurance industry, leading the industry to a more personalized and intelligent development direction. At the same time, the differences in the application of these technologies by insurance companies will also reshape the competitive landscape of the market.

1. Change the insurance publicity and marketing model

With the rise of the self-media era, short video platforms have become the mainstream of the Internet. The latest achievements in image and video generation technology will further lower the threshold for creation, attract more content creators to join the short video creation camp, bring more interesting materials to the platform, and increase traffic.

To keep up with this trend, insurers must master the use of these video and image generation tools in advance. By generating various styles of promotional content, and using big data to accurately match audiences, it attracts the attention of different groups and enhances market appeal. According to the characteristics of different groups, establish personalized marketing strategies, and touch the emotions of potential customers by pushing vivid and personalized content, so as to promote sales conversion.

2. Further optimize the risk identification and assessment mode

With the continuous advancement of technology, the application of video recognition technology has further improved the risk identification and assessment level of insurance companies. Compared with image recognition technology alone, video recognition technology can provide more comprehensive and detailed information. In the case of car insurance, for example, video captures the entire process of an accident, rather than just a static image of the accident after the accident. By analyzing the video data, the insurance company can automatically identify key information such as the cause of the accident, the driving status of the vehicle, and the traffic situation, so as to accurately assess the extent of the damage and the attribution of responsibility.

In addition, the application of video generation technology provides a more reliable basis for insurance liability. Similarly, taking auto insurance as an example, by restoring the accident through video generation technology, insurance companies can more accurately determine the accident liability and liability ratio, and avoid disputes caused by insufficient information or misunderstanding. This technology not only improves the fairness and accuracy of the determination of liability, but also helps to speed up the processing of claims and improve customer satisfaction.

3. Fully automate the claims process

In the field of auto insurance, there are currently insurance companies that allow customers to complete the claim process without personal injury accidents by uploading pictures and videos. In contrast, car insurance cases involving personal injuries and other more specialized types of insurance, such as health insurance, used to require manual intervention due to problems such as information gaps and inconsistent data formats between medical institutions, causing inconvenience to customers.

With the continuous advancement of image and video technology, the future development potential of this field is huge. With the help of photos of the injured and medical examination videos uploaded by customers, insurance companies can expect to quickly assess the health status of the injured through advanced image recognition technology in the future. Furthermore, by building a medical evaluation model, insurance companies can automatically calculate the claim amount, which greatly improves the speed and efficiency of the claims process, and also improves customer satisfaction.

4. Truly personalize customer service

In the past, although the concept of "customer-centric" personalized service was widely admired, in practice, insurance companies were often limited by factors such as complex service processes, limited human resources, insufficient data analysis capabilities and insufficient system support, resulting in only limited hierarchical or classified services to customers. This service model struggles to meet the growing demand for customer personalization, which affects customer experience and satisfaction.

The rapid development of technology, especially in image recognition and automatic video generation, presents insurers with an unprecedented opportunity to transcend previous limitations and deliver truly personalized service to each customer. These technologies can quickly and accurately capture the specific needs and situations of customers by analyzing the images and videos uploaded by customers, so as to provide more accurate and efficient services. For example, through image recognition technology, insurance companies can instantly identify customers at the first contact, quickly access their historical insurance and service records, and generate personalized welcome messages based on this, making every customer service appear more intimate and professional. In addition, according to the specific needs of different customers, insurance companies can generate solution introduction videos in real time, so that customers can get richer and more intuitive information when learning about insurance products and services.

Further, the application of virtual customer service technology makes communication with customers more efficient and natural. These virtual agents not only handle customer inquiries, but also analyze all images and videos uploaded by customers to provide customized service solutions for each customer. Such a service not only increases the speed and efficiency of the process, but more importantly, greatly increases customer satisfaction and loyalty.

The result is a virtual agent that each customer has a dedicated virtual agent that understands the customer's historical behavior and preferences and is able to deliver the service in a way that best meets the customer's expectations. More importantly, such a service model can effectively protect customers' privacy and personal information security while improving service efficiency and customer experience, and fundamentally avoid the risk of information leakage.

5. Reshaping the competitive landscape of the insurance industry

The impact of new technologies on the insurance industry goes far beyond the personalized service and efficiency improvements mentioned above, and more fundamentally, it is reshaping the future competitive landscape of the industry. Unlike the previous era of relying on a large number of manpower and being dominated by channels, the competition in the insurance industry in the future will increasingly rely on the power of science and technology. In this transformation, insurers that can truly master and apply core technologies will gain a competitive advantage, and may even rewrite the balance of power in the industry.

For insurers that are not currently leading the industry, the development of technology also provides an opportunity to leapfrog forward. By embracing new technologies, these companies can not only improve their competitiveness, but also have the potential to lead in certain areas and even become market leaders in specific technologies or services.

6. Implications for insurance and tech companies

In the face of new technological challenges and opportunities, insurers must recognize the importance of increasing investment in technology and strengthening the training of technical talents. It is not limited to investment in cutting-edge technologies such as image recognition and generation, but also covers the cultivation of talents who are proficient in the application of these technologies. At the same time, a continued focus on emerging technology trends will enable insurers to quickly adapt to market changes and meet the new needs of their customers.

Technology companies play a key role in driving technological change in the insurance industry. Not only do they need to learn and apply the latest AI technologies, but they also need to have a deep understanding of the business pain points of the insurance industry and directly solve problems through technology solutions to create truly valuable products and services.

With the widespread use of unstructured data, including images and videos, in the insurance business, insurers must place greater emphasis on data management and privacy protection, which involves ensuring the security and compliance of data collection, storage, and processing, which is key to maintaining customer trust and protecting the company's reputation.

Insurance companies should work with technology companies and academic institutions to jointly promote the application of image recognition and generation technology in the insurance industry. Such cross-border collaborations not only accelerate the pace of technological innovation, but also help build a healthy technology ecosystem that enables insurers to use external resources more efficiently.

Insurers need to define their technology strategies, strengthen cooperation with technology companies, and invest in and develop key technology areas. At the same time, establishing a flexible and efficient organizational structure and corporate culture to adapt to the rapidly changing technological environment and market demand is the key to winning competitive advantages in the future technology era.

About the Author:

Mr. Gao Fei, a well-known expert in the field of financial technology, currently holds the position of Chief Technology Officer (CTO) of KWI Public Insurance Holding Group in Thailand.

With more than 20 years of experience in the field of financial technology, Mr. Gao Fei has been widely recognized in the industry. He has held technical leadership positions in a number of well-known financial institutions, including Taikang Life Insurance, Kunlun Health Insurance, Anbang Insurance Group and Sino British Life Insurance, where he is responsible for technology management and innovation, e-commerce and digital transformation, demonstrating his extensive experience and outstanding talent in the field of financial institution business and management.

In addition, Mr. Gao Fei has held core positions such as Chief Technology Officer and Chief Data Scientist in cutting-edge artificial intelligence companies and marketing technology companies, respectively, demonstrating his deep understanding and efficient practical ability of artificial intelligence and big data technologies in practical application scenarios. He has successfully led a number of digital transformation projects for financial institutions, and has deep insights and unique insights into the digital challenges and opportunities facing financial institutions today. Its achievements in promoting fintech innovation and practice have made important contributions to the development of the fintech field.