Modernizing Core Banking: A Shift Toward Composable Systems

Modernizing core banking systems with MongoDB can bring many benefits such as faster innovation, flexible deployment, and instant scalability. According to McKinsey & Company, it is critical for banks to modernize their core banking platforms with a “flexible back end” in order to stay competitive and adapt to new business models. With the emergence of better data infrastructure based on JSON and the ongoing evolution of software design, the next generation of composable core banking processes can be built on MongoDB's developer data platform, offering greater flexibility and adaptability than traditional systems.

The current market: Potential core banking solutions

Financial disruptors such as fintechs and challenger banks are growing their businesses and attracting customers by building on process-centric core banking systems, while traditional banks struggle with inflexible, legacy systems. As seen in Figure 1 below, two potential solutions are the core banking “platform” and “suite”. The platform solution involves using a single vendor and several closely integrated modules. It also includes a single, large database and a single roadmap. On the other hand, the suite solutions refers to using multiple vendors, multiple loosely integrated modules, multiple databases and roadmaps. However, both of these systems are inflexible and result in vendor lock-in, preventing the adoption of best-of-breed functionalities from other vendors.

Graphic displaying the 3 pillars of core banking solutions: Platform, Suite, and Composable Ecosystem. Under platform, the following specifications are provided: Single vendor; tightly coupled; single, static database of giant proportion and complexity; single roadmap; entire platform sold as one. Under suite, the following specifications are provided: multiple products, often via acquisition; tightly coupled; diverse databases with coordinated schema; multiple, tightly coupled roadmaps; entire suite as
Figure 1: Core banking solutions: platform, suite and composable ecosystem.

A new approach, known as a composable ecosystem as seen on the far right of Figure 1, is being adopted by some financial institutions. This approach consists of distinct independent services and functions, with the ability to incorporate "best of breed" functionality without major integration challenges, multiple loosely coupled roadmaps, and individual component deployment without vendor lock-in. This allows for specialization and the development of advanced individual components that can be combined to deliver the best products and services and is better at adopting new technologies and approaches.

Composable ecosystems with MongoDB's developer data platform

MongoDB’s developer data platform is the best choice for financial institutions to build a composable core banking ecosystem. Such an ecosystem is made up of four key building blocks as seen below in Figure 2: JSON, BIAN, MACH, and data domains. JSON is a widely-used data format in the financial industry, and MongoDB's BSON extension allows for the storage of additional data types. BIAN is a standard that defines a component business blueprint for banking, and MongoDB's technology supports BIAN and embodies MACH principles. MACH is a set of design principles for component-based architectures, and data domains enable the mapping of business capabilities to applications and data. By using MongoDB's developer data platform, financial institutions can implement flexible and scalable core banking systems that can adapt to the ever-changing market demands.

This image presents pillars of the data platform as pieces to a puzzle. The four pieces are as follows. One: BIAN, Business Banking Processes and Structures. Two: JSON, data model. Three: MACH, Architecture patterns (microservices, API-first, cloud-native, headless). Four: Data Domains: Business functions.
Figure 2: MongoDB, the developer data platform for your core banking system.

MongoDB in action: Core banking use cases

Companies such as Temenos and Current have utilized MongoDB's capabilities to deliver innovative services and improve performance. As Tony Coleman, CTO of Temenos, said, "Implementing a good data model is a great start. Implementing a great database technology that uses the data model correctly, is vital. MongoDB is a really great fit for banking." MongoDB and Temenos have worked on a number of new, component-based services to enhance the Temenos product family. Financial institutions can embed Temenos components to deliver new functionality in their existing on-premises environments or through a full banking-as-a-service experience with Temenos T365, powered by MongoDB on various cloud platforms. Temenos has a cloud-first, microservices-based infrastructure built with MongoDB, which gives customers flexibility while improving performance.

Current is a digital bank that was founded with the aim of providing its customers with a modern, convenient, and user-friendly banking experience. To achieve this, the company needed to build a robust, scalable, and flexible technology platform. Current decided to build its core technology ecosystem in-house, using MongoDB as the underlying database technology. "MongoDB gave us the flexibility to be agile with our data design and iterate quickly," said Trevor Marshall, CTO of Current. In addition, MongoDB's strong security features make it a secure choice for handling sensitive financial data. Overall, MongoDB's capabilities make it a powerful choice for driving innovation and simplifying landscapes in the financial sector.

Conclusion

In conclusion, the financial industry is in need of modernizing their core banking systems to stay competitive in the face of rising disruptors and new business models. A composable ecosystem, utilizing a developer data platform like MongoDB, offers greater flexibility and adaptability than traditional legacy systems.

If you’d like to learn more about how MongoDB can optimize your core banking functionalities, take a look at our white paper: Componentized Core Banking: The next generation of composable banking processes built upon MongoDB.

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5 Ways to Learn MongoDB

MongoDB offers a variety of ways for users to gain product knowledge, get certified, and advance their careers. In this guide, we'll provide an overview of the top five ways to get MongoDB training, resources, and certifications. #1: MongoDB University The best place to go to get MongoDB-certified and improve your technical skills is MongoDB University . At our last MongoDB.local London event, we announced the launch of a brand new, enhanced university experience, with new courses and features, and a seamless path to MongoDB certification to help you take your skills and career to the next level. MongoDB University offers courses, learning paths, and certifications in a variety of content types and programming languages. Some of the key features that MongoDB University offers are: Hands-on labs and quizzes Bite-sized video lectures Badges for certifications earned Study guides and materials Getting certified from MongoDB University is a great way to start your developer journey. Our education offerings also include benefits for students and educators . #2: MongoDB Developer Center For continued self-paced learning, the MongoDB Developer Center is the place to go. The Developer Center houses the latest MongoDB tutorials, videos, community forums , and code examples in your preferred languages and tools. The MongoDB Developer Center is a global community of more than seven million developers. Within the Developer Center, you can code in different languages, get access to integrate technologies you already use, and start building with MongoDB products, including: MongoDB, the original NoSQL database MongoDB Atlas , the cloud document database as a service and the easiest way to deploy, operate, and scale MongoDB MongoDB Atlas App Services , the easy way to get new apps into the hands of your users faster #3: Instructor-led training As an IT leader, you can help your team succeed with MongoDB instructor-led training taught live by expert teachers and consultants. With MongoDB’s instructor-led training offering, you can access courses aimed at various roles. Our Developer and Operations learning paths cover fundamental skills needed to build and manage critical MongoDB deployments. Beyond that, our specialty courses help learners master their skills and explore advanced MongoDB features and products. You can also modify how you want to learn. MongoDB offers public remote courses, which are perfect for individuals or teams who want to send a few learners at a time. If your goal is to upskill your entire team with MongoDB, our courses can be delivered privately, both onsite or remotely. Instructor-led training also provides the opportunity for Q&A, providing answers to your specific questions. #4: Resources Beyond formal training programs, MongoDB is committed to providing thought leadership resources for those looking to dive deeper and learn more about MongoDB and database technologies in general. Our website offers an active blog with ongoing thought leadership and how-to articles, along with additional coding documentation , guides, and drivers. You can also check out the MongoDB Podcast for information about new and emerging technology, MongoDB products, and best practices. #5: Events You can also engage with MongoDB experts at our many events, including MongoDB World, our annual conference for developers and other IT leaders. After MongoDB World, we take our show on the road with MongoDB .local events across the globe. These events give you the opportunity to learn in a hands-on fashion and meet other MongoDB users. MongoDB also hosts MongoDB days in various global regions, focusing on developer workshops and leveling up skills. Beyond that, you can keep up with our webinars and other learning opportunities through our Events page. Build your own MongoDB story Of course, many people like to learn by doing. To get started using MongoDB Atlas in minutes, register for free .

January 20, 2023
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AI-Powered Call Centers: A New Era of Customer Service

Customer satisfaction is critical for insurance companies. Studies have shown that companies with superior customer experiences consistently outperform their peers. In fact, McKinsey found that life and property/casualty insurers with superior customer experiences saw a significant 20% and 65% increase in Total Shareholder Return , respectively, over five years. A satisfied customer is a loyal customer. They are 80% more likely to renew their policies, directly contributing to sustainable growth. However, one major challenge faced by many insurance companies is the inefficiency of their call centers. Agents often struggle to quickly locate and deliver accurate information to customers, leading to frustration and dissatisfaction. This article explores how Dataworkz and MongoDB can transform call center operations. By converting call recordings into searchable vectors (numerical representations of data points in a multi-dimensional space), businesses can quickly access relevant information and improve customer service. We'll dig into how the integration of Amazon Transcribe, Cohere, and MongoDB Atlas Vector Search—as well as Dataworkz's RAG-as-a-service platform— is achieving this transformation. From call recordings to vectors: A data-driven approach Customer service interactions are goldmines of valuable insights. By analyzing call recordings, we can identify successful resolution strategies and uncover frequently asked questions. In turn, by making this information—which is often buried in audio files— accessible to agents, they can give customers faster and more accurate assistance. However, the vast volume and unstructured nature of these audio files make it challenging to extract actionable information efficiently. To address this challenge, we propose a pipeline that leverages AI and analytics to transform raw audio recordings into vectors as shown in Figure 1: Storage of raw audio files: Past call recordings are stored in their original audio format Processing of the audio files with AI and analytics services (such as Amazon Transcribe Call Analytics ): speech-to-text conversion, summarization of content, and vectorization Storage of vectors and metadata: The generated vectors and associated metadata (e.g., call timestamps, agent information) are stored in an operational data store Figure 1: Customer service call insight extraction and vectorization flow Once the data is stored in vector format within the operational data store, it becomes accessible for real-time applications. This data can be consumed directly through vector search or integrated into a retrieval-augmented generation (RAG) architecture, a technique that combines the capabilities of large language models (LLMs) with external knowledge sources to generate more accurate and informative outputs. Introducing Dataworkz: Simplifying RAG implementation Building RAG pipelines can be cumbersome and time-consuming for developers who must learn yet another stack of technologies. Especially in this initial phase, where companies want to experiment and move fast, it is essential to leverage tools that allow us to abstract complexity and don’t require deep knowledge of each component in order to experiment with and realize the benefits of RAG quickly. Dataworkz offers a powerful and composable RAG-as-a-service platform that streamlines the process of building RAG applications for enterprises. To operationalize RAG effectively, organizations need to master five key capabilities: ETL for LLMs: Dataworkz connects with diverse data sources and formats, transforming the data to make it ready for consumption by generative AI applications. Indexing: The platform breaks down data into smaller chunks and creates embeddings that capture semantics, storing them in a vector database. Retrieval: Dataworkz ensures the retrieval of accurate information in response to user queries, a critical part of the RAG process. Synthesis: The retrieved information is then used to build the context for a foundational model, generating responses grounded in reality. Monitoring: With many moving parts in the RAG system, Dataworkz provides robust monitoring capabilities essential for production use cases. Dataworkz's intuitive point-and-click interface (as seen in Video 1) simplifies RAG implementation, allowing enterprises to quickly operationalize AI applications. The platform offers flexibility and choice in data connectors, embedding models, vector stores, and language models. Additionally, tools like A/B testing ensure the quality and reliability of generated responses. This combination of ease of use, optionality, and quality assurance is a key tenet of Dataworkz's "RAG as a Service" offering. Diving deeper: System architecture and functionalities Now that we’ve looked at the components of the pre-processing pipeline, let’s explore the proposed real-time system architecture in detail. It comprises the following modules and functions (see Figure 2): Amazon Transcribe , which receives the audio coming from the customer’s phone and converts it into text. Cohere ’s embedding model, served through Amazon Bedrock , vectorizes the text coming from Transcribe. MongoDB Atlas Vector Search receives the query vector and returns a document that contains the most semantically similar FAQ in the database. Figure 2: System architecture and modules Here are a couple of FAQs we used for the demo: Q: “Can you explain the different types of coverage available for my home insurance?” A: “Home insurance typically includes coverage for the structure of your home, your personal belongings, liability protection, and additional living expenses in case you need to temporarily relocate. I can provide more detailed information on each type if you'd like.” Q: “What is the process for adding a new driver to my auto insurance policy?" A: “To add a new driver to your auto insurance policy, I'll need some details about the driver, such as their name, date of birth, and driver's license number. We can add them to your policy over the phone, or you can do it through our online portal.” Note that the question is reported just for reference, and it’s not used for retrieval. The actual question is provided by the user through the voice interface and then matched in real-time with the answers in the database using Vector Search. This information is finally presented to the customer service operator in text form (see Fig. 3). The proposed architecture is simple but very powerful, easy to implement, and effective. Moreover, it can serve as a foundation for more advanced use cases that require complex interactions, such as agentic workflows , and iterative and multi-step processes that combine LLMs and hybrid search to complete sophisticated tasks. Figure 3: App interface, displaying what has been asked by the customer (left) and how the information is presented to the customer service operator (right) This solution not only impacts human operator workflows but can also underpin chatbots and voicebots, enabling them to provide more relevant and contextual customer responses. Building a better future for customer service By seamlessly integrating analytical and operational data streams, insurance companies can significantly enhance both operational efficiency and customer satisfaction. Our system empowers businesses to optimize staffing, accelerate inquiry resolution, and deliver superior customer service through data-driven, real-time insights. To embark on your own customer service transformation, explore our GitHub repository and take advantage of the Dataworkz free tier .

November 27, 2024