CTF Life Leverages MongoDB Atlas to Deliver Customer-Centric Service

MongoDB
August 28, 2024

Hong Kong-based Chow Tai Fook Life Insurance Company Limited (CTF Life) is proud of its rich, nearly 40-year history of providing a wide range of insurance and financial planning services. The company provides life, health, accident, savings, and investment insurance to its customers, helping them and their loved ones navigate life’s journey with personalized planning solutions, lifelong protection, and diverse lifestyle experiences.

A wholly-owned subsidiary of NWS Holdings Limited and a member of Chow Tai Fook Group, CTF Life consistently strengthens its collaboration with the diverse conglomerate of the Cheng family (Chow Tai Fook Group) and draws on the Group’s robust financial strength, strategic investments across the globe, and advanced customer-focused digital technology with the aspiration of becoming a leading insurance company in the Greater Bay Area. To achieve this goal, CTF Life modernized its on-premises infrastructure to provide the speed and flexibility required to offer customers personalized experiences.

To turn their vision into reality, CTF Life decided to adopt MongoDB Atlas. By modernizing their systems and processes with the world’s most versatile developer data platform, CTF Life knew they’d be able to meet customer expectations, offering improved customer service, faster response times, and more convenient access to their products and services.

Data-driven customer service

The insurance industry is undergoing a significant shift, from traditional data management to near-real-time data-driven insights, driven by strong consumer demand and the urgent need for companies to process large amounts of data efficiently. As insurance companies strive to provide personalized and real-time products, the move towards sophisticated and real-time data-driven customer service is inevitable.

CTF Life is on its digital transformation journey to modernize its relational database management system (RDBMS) infrastructure to empower its agents, known as Life Planners, to provide enhanced customer experiences. The company faced obstacles to legacy systems and siloed data. Life Planners were spending a lot of time looking up customer information from various systems and organizing this into useful customer insights.

Not having a holistic view of customer data also made it challenging to recommend personalised products and services within CTF Life, the Group, and beyond. Reliance on legacy RDBMS systems presented a major challenge in CTF Life’s pursuit of leveraging real-time customer information to enhance customer experiences and operational efficiency.

For their modernization efforts, CTF Life was looking for the following required capabilities:

  • A modernized application with agile development

  • No downtime for changing schema, new modules, or feature updates

  • A single way of centralizing and organizing data from a number of sources (backend, CRM, etc.) into a standardized format ready for a front-end mobile application

  • A future-proof data platform with extensible capability for analytics across CTF Life, their diverse conglomerate collaboration, and their strategic partners to support the company’s digital solutions

Embracing the operational data layer for enhanced experiences

CTF Life knew they had to build a solution for the Life Planners to harness the wealth of useful information available to them, making it easier to engage and connect with customers. The first project identified was their clienteling system, which is designed to establish long-term relationships with customers based on data about their preferences, behaviors, and needs.

To overcome their legacy systems and siloed data, CTF Life built their clienteling system on MongoDB Atlas. Atlas serves as the digital data store for Life Planners, creating a single view of the customer (SVOC) with a flexible document model that enables CTF Life to handle large volumes of customer data in real-time efficiently.

By integrating their operational data into one platform with MongoDB Atlas on Microsoft Azure, CTF Life’s revamped clienteling system provides their Life Planners with a comprehensive view of customer profiles, which allows them to share targeted content with customers. Additionally, CTF Life is using Atlas Search to build relevance-based search capabilities directly into the application, making it faster and easier to search for customer data across the company’s system landscape.

These benefits helped improve customer service with faster access to data with an SVOC so Life Planners can provide more accurate and timely information to their customers. Atlas Search is now the foundation of the clienteling system, which powers data analytics and machine learning capabilities to support various use cases.

For example, the clienteling app's smart reminder feature recognizes key moments in a customer's life, like the impending arrival of a newborn child. Based on these types of insights, the app can help Life Planners make personalized recommendations to the customer about relevant services and products that may be of interest to them as new parents.

Because of its work with MongoDB, CTF Life can now analyze customer profiles and use smart reminders to engage customers at the right time in the right context. This has made following up with customers and leads faster and easier. And, contacting prospects, scheduling appointments, setting reminders, sharing relevant content, running campaigns and promotions, recommending products and services, and tracking lead progress can all be performed in one system.

Moreover, access to real-time data enables Life Planners to streamline their work and reduce manual processes. And data-driven insights empower Life Planners to make informed decisions quickly. They can analyze customer information, identify trends, and tailor their recommendations to meet individual needs more effectively. With MongoDB Atlas Search, Life Planners can use advanced search capabilities to identify opportunities to serve customers better.

Continuing to create value beyond insurance

CTF Life strives to provide its customers with value beyond insurance. Through a range of collaborations with Chow Tai Fook Group, and strategic partnerships with technology partners like MongoDB, CTF Life has created a customer-centric approach and continues to advance its digital transformation strategy to enhance a well-rounded experience for customers that goes beyond insurance with a sincere and deep understanding of their diverse needs in every chapter of their life journey.

In the future, CTF Life will continue to build upon its strategic partnership with MongoDB and expand the use of its digital data store on MongoDB Atlas by creating new client servicing modules on the mobile app their Life Planners use. CTF Life will also be expanding its search capabilities with Atlas Vector Search to accelerate their journey to building advanced search and generative AI applications for more automated servicing.

Partnering with MongoDB helped us prioritize technology that accelerates our digital transformation. The integration between generative AI and MongoDB as a medium for information search can be leveraged to further support front-line Life Planners as well as mid/back-office operations.

Derek Ip, Chief Digital and Technology Officer of CTF Life

Learn how to tap into real-time data with MongoDB Atlas.

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Elevate Your Java Applications with MongoDB and Spring AI

MongoDB is excited to announce an integration with Spring AI, enhancing MongoDB Atlas Vector Search for Java developers. This collaboration brings Vector Search to Java applications, making it easier to build intelligent, high-performance AI applications. Why Spring AI? Spring AI is an AI library designed specifically for Java, applying the familiar principles of the Spring ecosystem to AI development. It enables developers to build, train, and deploy AI models efficiently within their Java applications. Spring AI addresses the gap left by other AI frameworks and integrations that focus on other programming languages, such as Python, providing a streamlined solution for Java developers. Spring has been a cornerstone for Java developers for decades, offering a consistent and reliable framework for building robust applications. The introduction of Spring AI continues this legacy, providing a straightforward path for Java developers to incorporate AI into their projects. With the MongoDB-Spring integration, developers can leverage their existing Spring knowledge to build next-generation AI applications without the friction associated with learning a new framework. Key features of Spring AI include: Familiarity: Leverage the design principles of the Spring ecosystem. Spring AI allows Java developers to use the same familiar tools and patterns they already know from other Spring projects, reducing the learning curve and allowing them to focus on building innovative AI applications. This means you can integrate AI capabilities—including Atlas Vector Search—without having to learn a new language or framework, making the transition smoother and more intuitive. Portability: Applications built with Spring AI can run anywhere the Spring framework runs. This ensures that AI applications are highly portable and can be deployed across various environments without modification, guaranteeing flexibility and consistency in deployment strategies. Modular design: Use Plain Old Java Objects (POJOs) as building blocks. Spring AI’s modular design promotes clean code architecture and maintainability. By using POJOs, developers can create modular, reusable components that simplify the development and maintenance of AI applications. This modularity also facilitates easier testing and debugging, leading to more robust applications that efficiently integrate with Atlas Vector Search. Efficiency: Streamline development with tools and features designed for AI applications in Java. Spring AI provides a range of tools that enhance development efficiency, including pre-built templates, configuration management, and integrated testing tools. These features reduce the time and effort required to develop AI applications, allowing developers to bring their ideas to market faster. These features streamline AI development by enhancing the integration and performance of Atlas Vector Search within Java applications, making it easier to build and scale AI-driven features. Enhancing AI development with Spring AI and Atlas Vector Search MongoDB Atlas Vector Search enhances AI application development by providing advanced search capabilities. The new Spring AI integration enables developers to manage and search vector data within AI models, enabling features like recommendation systems, natural language processing, and predictive analytics. Atlas Vector Search allows you to store, index, and search high-dimensional vectors, which are crucial for AI and machine learning models. This capability supports a range of AI features: Recommendation systems: Provide personalized recommendations based on user behavior and preferences. Natural language processing: Enhance text analysis and understanding for chatbots, sentiment analysis, and more. Predictive analytics: Improve forecasting and decision-making with advanced data models. What the integration means for Java developers Prior to MongoDB-Spring integration, Java developers did not have an easy way to integrate Spring into their AI applications using MongoDB Atlas Vector Search, which led to longer development times and suboptimal application performance. With this integration, the Java development landscape is transformed, allowing developers to build and deploy AI applications with greater efficiency. The integration simplifies the entire process, enabling developers to concentrate on creating innovative solutions rather than dealing with integration hurdles. This approach not only reduces development time but also accelerates time-to-market. Additionally, MongoDB offers robust support through comprehensive tutorials and a wealth of community-driven content. Whether you’re just beginning or looking to optimize existing applications, you’ll find the resources and guidance you need at every stage of your development journey. Get started! The MongoDB and Spring AI integration is designed to simplify the development of intelligent Java applications. By combining MongoDB's robust data platform with Spring AI's capabilities, you can create high-performance applications more efficiently. To start using MongoDB with Spring AI, explore our documentation , tutorial , and check out our GitHub repository to build the next generation of AI-driven applications today. Head over to our quick-start guide to get started with Atlas Vector Search today.

August 26, 2024
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MongoDB and Partners: Building the AI Future, Together

If you’re like me, over the past year you’ve closely watched AI’s developments—and the world’s reactions to them. From infectious excitement about AI’s capabilities, to impatience with its cost and return on investment, every day has been filled with AI twists and turns. It’s been quite the roller coaster. During the ride, from time to time I’ve wondered where AI falls on the Gartner hype cycle, which gives "a view of how a technology or application will evolve over time." Have we hit the "peak of inflated expectations" only to fall into the "trough of disillusionment?" Or is the hype cycle an imperfect guide, as The Economist argues? The reality is that it takes time for any new technology—even transformative ones like AI—to take hold. And every advance, no matter how big, has had its detractors. A famous example is that of Picasso (!), who in 1968 said, “Computers are useless. They can only give you answers.” (!!) For our part, MongoDB is convinced that AI is a once-in-a-generation technology that will enhance every future application—a belief that has been reinforced by the incredible work our partners have shared at MongoDB’s 2024 events. Speeding AI development MongoDB is committed to helping organizations of all sizes succeed with AI, and one way we’re doing that is by collaborating with the MongoDB partner ecosystem to create powerful, user-friendly AI development tools and solutions. For example, Fireworks.ai —which is a member of the MongoDB AI Applications Program ecosystem —created an inference solution that hosts gen AI models and supports containerized deployments. This tool makes it easier for developers to build and deploy powerful applications with a range of easy-to-use tools and customization options. They can choose to use state-of-the-art, open-source language, image, and multimodal foundation models off the shelf, or they can customize and fine-tune models to their needs. Jointly, Fireworks.ai and MongoDB provide a solution for developers who want to leverage highly curated and optimized open-source models and combine these with their organization’s own proprietary data—and to do so with unparalleled speed and security. “MongoDB is one of the most sophisticated database providers, and it’s very easy to use,” said Benny Chen , cofounder of Fireworks.ai. "We want developers to be able to use these tools, and we want to work with providers who enable and empower developers." Nomic , another MAAP ecosystem member, also enables developers with best-in-class solutions across the entire unstructured data workflow. Their Embed offering, available through the Nomic API , allows users to vectorize large-scale datasets for use in text, image, and multimodal retrieval applications, including retrieval-augmented generation (RAG), using only their web browser. The Nomic-MongoDB solution is a highly efficient, open-weight model that developers can use to visualize the unstructured datasets they store in MongoDB Atlas . These insights help users quickly discover trends and articulate data-driven value propositions. Nomic also supported the recently announced vector quantization in MongoDB Atlas Vector Search , which reduces vector sizes while preserving performance. Last—but hardly least!—there’s our new reference architecture with MAAP partners AWS and Anthropic. Announced at MongoDB.local London , the reference architecture supports building memory-enhanced AI agents, and is designed to streamline complex processes and develop smarter, more responsive applications. For more—including a link to the code on Github— check out the MongoDB Developer Center . Making AI work for anyone and everyone The companies MongoDB partners with aren’t just making gen AI easier for developers—they’re building tools for everyone. For example, Capgemini has invested $2 billion in gen AI and is training 100,000 of its employees in the technology. GenYoda, a solution that helps insurance professionals with their daily work, is a product of this investment. GenYoda leverages MongoDB Atlas Vector Search to analyze large amounts of customer data, like policy statements, premiums, claims history, and health information. Using GenYoda, insurance professionals can quickly analyze underwriters’ reports to make informed decisions, create longitudinal health summaries, and streamline customer interactions to improve contact center efficiency. GenYoda can ingest 100,000 documents in just a few hours and respond to users’ queries in two to three seconds—a metric on par with the most widely used gen AI models. And it produces results: in one example, by using Capgemini’s solution an insurer was able to increase productivity by 15%, add new reports 25% faster (thus speeding decision-making), and reduce the manual effort of searching PDFs, increasing efficiency by 10%. Building the future of AI together So, what’s next? Honestly, I’m as curious as you are. But I’m also incredibly excited. At MongoDB, we’re active participants in the AI revolution, working to embrace the possibilities that lie ahead. The future of gen AI is bright, and I can’t wait to see what we’ll build together. To learn more about how MongoDB can accelerate your AI journey, explore the MongoDB AI Applications Program .

November 4, 2024