Luca Napoli

6 results

From Relational Databases to AI: An Insurance Data Modernization Journey

Imagine you’re a data architect, a developer, or a data engineer at an insurance company. Management has asked you and your team to build a new AI claim adjustment system, a customer-facing LLM-powered chatbot, and an application to streamline the underwriting process. However, doing so is far from straightforward due to the challenges you face on a daily basis. The bulk of your time is spent navigating your company’s outdated legacy systems, which were built in the 1970s and 1980s. Some of these legacy platforms were written in COBOL and CICS, and today very few people on your team know how to develop and maintain those technologies. Moreover, the data models you work with are another source of frustration. Every interaction with them is a reminder of the intricate structures that have evolved over time, making data manipulation and analysis a nightmare. In sum, legacy systems are preventing your team—and your company—from innovating and keeping up with both your industry and customer demands. Whether you’re trying to modernize your legacy systems to improve operational efficiency, or to boost developer productivity, or if you want to build AI-powered apps that integrate with large language models (LLMs), MongoDB has a solution for that. In this post, we’ll walk you through a journey that starts with a relational data model refactored into MongoDB collections, vectorization and querying of unstructured data and, finally, retrieval augmented generation (RAG) : asking large language models (LLMs) questions about data in natural language. Identifying, modernizing, and storing the data Our journey starts with an assessment of the data sources we want to work with. As shown below, we can bucket the data into three different categories: Structured legacy data: Tables of claims, coverages, billings, and more. Is your data locked in rigid relations schemas? This tutorial is a step-by-step guide on how to migrate a real-life insurance relational model with the help of MongoDB Relational Migrator , refactoring 21 tables to only five MongoDB collections. Structured data (JSON): You might have files of policies, insurance products, or forms in JSON format. Check out our docs to learn how to insert those into a MongoDB collection. Unstructured data (PDFs, Audios, Images, etc.): If you need to create and store a numerical representation (vector embedding) of, for instance, claim-related photos of accidents or PDFs of policy guidelines, you can have a look at this blog that will walk you through the process of generating embeddings of pictures of car crashes and persisting them alongside existing fields in a MongoDB collection. Figure 1: Storing different types of data into MongoDB Regardless of the original format or source, our data has finally landed into MongoDB Atlas into what we call a Converged AI Data Store, which is a platform that centrally integrates and organizes enterprise data, including vectors, that enable the development of ML- and AI-powered applications. Accessing, experimenting and interacting with the data It’s time to put the data to work. The Converged AI Data Store unlocks a plethora of use cases and efficiency gains, both for the business and for developers. The next step of the journey is about the different ways we can interact with our data: Database and Full Text Search: Learn how to run database queries, start from the basics and move up to advanced features such as facets, fuzzy search, autocomplete, highlighting, and more with Atlas Search . Vector Search: We can finally leverage unstructured data. The Image Search blog we mentioned earlier also explains how to create a Vector Search index and run vector queries against embeddings of photos. RAG: Combining Vector Search and the power of LLMs, it is possible to interact in natural language with our data (see Figure 2 below), asking complex questions and getting detailed answers. Follow this tutorial to become a RAG expert. Figure 2: Retrieval augmented generation (RAG) diagram where we dynamically combine our custom data with the LLM to generate reliable and relevant outputs Having explored all the different ways we can ask questions of the data, we made it to the end of our journey. You are now ready to modernize your company’s systems and finally be able to keep up with the business’ demands. What will you build next? If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases Build a ML-Powered Underwriting Engine in 20 Minutes with MongoDB and Databricks

March 14, 2024

Every Operational Data Layer (ODL) Can Benefit From Search

In today's digital landscape, organizations frequently encounter the daunting challenge of managing complex data architectures. Multiple systems, diverse technologies, and a variety of programming languages become entwined, making smooth operations a significant struggle. A frequent example of this issue is seen in some major banks still relying on a banking system built in the 1970s, continuing to run on a mainframe with minimal updates. The consequence is a complex architecture as seen in Figure 1, where data is scattered across various systems, creating inefficiencies and hindering seamless operations. Offloading the data from one or more monolithic systems is a well-proven approach to increase agility and deliver new innovative services to external and internal customers. In this blog we will speak about how search can make Operational Data Layers (ODL) – an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications – an even more powerful tool. Figure 1: Complex Data Architecture Operational Data Store (ODS) as a solution To tackle the complexities of their existing data architecture, organizations have turned to Operational Data Stores (ODS). An ODS serves as a secondary data store, holding data replicated of primary transactional systems as seen in Figure 2. Organizations can feed their ODS with change data capture technologies. Figure 2: Conceptual model of an Operational Data Layer The evolutionary path of adoption Implementing an ODS requires a thoughtful approach that aligns with the organization's digital transformation journey. Typically, the adoption path consists of several stages as seen in Figure 3. Initially, organizations focus on extracting data from one system into their Operational Data Store, allowing them to operate on a more unified dataset. Gradually, they can retire legacy systems and eliminate the need for intermediate data streams. The key benefit of this incremental approach is that it delivers value (e.g. offloading mainframe operations) to the business at every step by eliminating the need for a complete overhaul and minimizing disruption. Figure 3: Evolution of a basic ODS into a system of records Areas of application ODS are used to support the business in three different ways: Data Access Layers allow organizations to free their data from the limitations imposed by data silos and technological variations. Organizations consolidate data from different sources that often use different data storage technologies and paradigms, creating a unified view that simplifies data access and analysis. This pattern is mainly used to enable modern APIs, speed up development of new customer services, and improve responsiveness and resiliency. Operational Data Layer (ODL): The ODL is an internal-focused layer that aids in complex processing workflows. It serves as a hub for orchestrating and managing data across various stages of processing. The ODL empowers organizations to enrich and improve data iteratively, resulting in more powerful and accurate insights. It provides a holistic view of data and process information, an improved customer experience, and reduced operational costs. Developer ODL: Building a developer-focused ODL can provide significant advantages during the development cycle. By making data readily available to developers, organizations can accelerate the development process and gain a comprehensive understanding of their data structures. This, in turn, helps in identifying and addressing issues early on, leading to improved data models and better system performance. In a nutshell, this pattern helps reduce developer training time, streamlines development and speeds up testing and test automation. The power of search in ODS So how can every ODL benefit from search capabilities and how can MongoDB Atlas Search help? Atlas Search plays a crucial role in maximizing the value of an ODS. When we have questions or are searching for an answer, our natural interaction with information is primarily through search. We excel at interpreting imprecise queries and extracting relevant information from vast datasets. By incorporating search capabilities with Atlas Search into an ODS, organizations can empower their users to explore, analyze, and gain valuable insights from their data. Consider the example of a banking organization with a complex web of interconnected systems. Searching for specific transactions or identifying patterns becomes a daunting task, especially when dealing with numeric identifiers across multiple systems. Traditionally, this involved manual effort and navigating through numerous systems. However, with a search-enabled ODS, users can quickly query the relevant data and retrieve candidate matches. This greatly streamlines the process, saves time, and enhances efficiency. Practical examples: Leveraging ODS and Atlas Search Let's explore a few practical examples that demonstrate the power of ODS and the Atlas Search functionality. Operational Data Layer for Payments Processing: A financial institution implemented an ODS-based operational layer for processing payments. By aggregating data from multiple sources and leveraging search capabilities, they achieved faster and more accurate payment processing. This enabled them to investigate issues, ensure consistency, and deliver a superior customer experience. Customer 360 View: Another organization leveraged an ODS to create a comprehensive view of their customers, empowering relationship managers and bank tellers with a holistic understanding. With search functionality, they could quickly locate customer information across various systems, saving time and improving customer service. Post-trade Trading Platform: A global broker operating across 25 different exchanges utilized an ODS to power their post-trade trading platform. By leveraging search capabilities, they simplified the retrieval of data from various systems, leading to efficient and reliable trading operations. Conclusion In the dynamic world of data management, Operational Data Stores (ODS) have emerged as a crucial component for organizations seeking to streamline their data architectures. By adopting an incremental approach and leveraging search functionality such as Atlas Search , organizations can enhance data accessibility, improve operational efficiency, and drive valuable insights. The power of search within ODS lies in its ability to simplify data retrieval, accelerate development cycles, and enable users to interact with data in a more intuitive and efficient manner. By embracing these practices, organizations can unlock the true potential of their data, paving the way for a more productive and data-driven future. For more information on Atlas Search, check out the following resources: Watch this MongoDB.local talk which expands on this blog: Every ODS Needs Search: A Practical Primer Based on Client Experiences Discover MongoDB’s search functionalities Learn how Helvetia accelerates cloud-native modernization by 90% with MongoDB Atlas and MongoDB Atlas Search

November 1, 2023

AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases

We’re just under a year since OpenAI released ChatGPT, unleashing a wave of hype, investment, and media frenzy around the potential of generative AI to transform how we do business and interact with the world. But while the majority of the investment dollars and media attention zeroed in on the disruptive capabilities of large language models (LLMs), there’s a crucial component underpinning this breakthrough technology that hasn’t received the attention it deserves; the humble vector database. Vector databases, a type of database that stores numeric representations (or vectors) of your data, allow advanced machine learning algorithms to make sense of unstructured data like images, sound, or unstructured text, and return relevant results. (You can read more about vector search databases and vector search on our Developer Hub .) For industries dealing with vast amounts of data, such as insurance, the potential impact of vector databases and vector search is immense. In this blog, we will focus on how vectors can speed up and increase the accuracy of claim adjustment. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. The claims process… vectorized! The process of claim adjustment is time-consuming and error-prone. As one insurance client recently told us, “If an adjuster touches it, we lose money.” For each claim, adjusters need to go through past claims from the client and related guidelines, which are usually scattered across multiple systems and formats, making it difficult to find relevant information and time-consuming to produce an accurate estimate of what needs to be paid. For this blog, let’s use the example of a car accident claim. In our example, a car just crashed into another vehicle. The driver gets out and starts taking pictures of the damage, uploading them to their car insurance app, where an adjuster receives the photos. Typically, the adjuster would painstakingly comb through past claims and parse guidelines to work up an estimate of the damage and process the claim. But with a vector database, the adjuster can simply ask an AI to “show me images similar to this crash,” and a Vector Search -powered system can return photos of car accidents with similar damage profiles from the claims history database. The adjuster is now able to quickly compare the car accident photos with the most relevant ones in the insurer's claim history. What’s more, with MongoDB it is possible to store vectors as arrays alongside existing fields in a document. In our car crash scenario, this means that our fictional adjuster can not only retrieve the most similar pictures but also have access to complementary information stored in the same database: claim notes, loss amount, car model, car manufacturing year, etc. The adjuster now has a comprehensive view of past accidents and how they were handled by the insurance company, in seconds. For this use case, we have focused on image search, but most data formats can be vectorized, including text and sound. This means that an adjuster could query using claim notes and find similar notes in the claim history or related paragraphs in the guidelines. Vector Search is an extremely powerful tool as it unlocks access to unstructured data that was previously hard to work with such as PDFs, images, or audio files. How does this work in practice? Let’s go through each step of the process: A search index is configured on an existing collection in MongoDB Atlas An image set is sent to an embedding model that generates the image vectors The vectors are then stored in Atlas, alongside the current metadata found in the collection Figure 1: A dataset of photos of past accidents is vectorised and stored in Atlas We run our query against the existing database and Vector Search returns the most similar images Figure 2: An image similarity query is performed, and the 5 top similar images are returned. Example user interface: A claim-adjuster dashboard leveraging Vector Search Figure 3: UI of the claim adjuster application We can go a step further and use our vectors to provide an LLM with the context necessary to generate more reliable and accurate outputs, also known as Retrieval Augmented Output (RAG). These outputs can include: Natural language processing for tasks such as chatbots and question-answering — think of a claim adjuster that interacts with a conversational interface and asks questions such as: “Give me the average of the loss amount for accidents related to one of the photos of claim XYZ” or “Summarize the content of the guidelines related to this accident” Computer vision and audio processing for image classification and object detection to speech recognition and translation Content generation, including creating text-based documentation, reports, and computer code, or converting text to an image or video Figure 4 brings together the workflow enabling RAG for the LLM. Figure 4: Dynamically combining your custom data with the LLM to generate reliable and relevant outputs If you’re interested in seeing how to do this in practice and start prototyping, check out our GitHub repository and dive right in! Go hands-on! Vector databases and vector search will transform how insurers do business. In this blog we have explored how vectors can be leveraged to speed up the work of claim adjusters, which directly translates to an improved customer experience and, crucially, cost savings through faster claims processing and enhanced accuracy. Elsewhere, vector search could be used for: Enhanced customer service. Imagine being able to instantly pull up comprehensive policyholder profiles, their claims history, and any related information with a simple search. Vector search makes this possible, facilitating better interactions and more informed decisions. Personalized Recommendations. As AI-driven personalization becomes the gold standard, vector search aids in accurately matching policyholders with tailor-made insurance products and services that meet their unique needs. Scaled AI Efforts. Scale AI implementations across the organization. From improving customer service chatbots to detecting fraudulent activities, vector-based models can handle tasks more efficiently than traditional methods. Atlas Vector Search goes one step further. By unifying the operational database and vector store in a single platform, MongoDB Atlas turbocharges the process of building semantic search and AI-powered applications, empowering insurers to quickly build applications that take advantage of the value of your vast troves of data. Find out why leading insurers trust MongoDB .

October 4, 2023

Pagos Digitales - Foco en America Latina

Impulsado por las nuevas tecnologías y las tendencias globales, el mercado de pagos digitales está floreciendo en todo el mundo. Con una valoración de más de $ 68 mil millones en 2021 y expectativas de crecimiento de doble dígitos durante la próxima década, los mercados emergentes están liderando el camino en términos de expansión relativa. Un panorama que una vez fue dominado por grandes bancos y compañías de tarjetas de crédito ahora está siendo atacado por disruptores interesados en capturar una cuota de mercado. Según un estudio de McKinsey , hay cuatro factores principales en el núcleo de esta transformación: Adopción de pagos cashless inducidos por la pandemia E-commerce Impulso del gobierno a los pagos digitales Fintech Cabe destacar como la pandemia ha sido una gran catalizadora en el aumento de la inclusión financiera al fomentar medios de pago alternativos y nuevas formas de pedir préstamos y ahorrar. Estos nuevos servicios digitales son, de hecho, más fáciles de acceder y consumir. En América Latina y el Caribe (LAC), la Covid provocó un aumento dramático en los pagos sin efectivo, el 40% de los adultos realizó una compra en línea, el 14% de los cuales lo hizo por primera vez en su vida. El e-commerce ha visto un crecimiento estelar, con una penetración que probablemente superará el 70% de la población en 2022, los actores nacionales y globales, incluidos Mercado Libre y Falabella, están impulsando la innovación de pagos digitales para proporcionar una experiencia de cliente cada vez más fluida en sus plataformas. Los bancos centrales están promoviendo nuevas infraestructuras para pagos en tiempo real, con el objetivo de proporcionar una tecnología más económica y rápida para la transferencia de dinero tanto para ciudadanos como para empresas. PIX es probablemente el mayor caso de éxito. Una plataforma de pagos instantáneos desarrollada por el Banco Central do Brasil (Banco Central de Brasil), comenzó a operar en noviembre de 2020 y, en 18 meses, más del 75% de los brasileños adultos lo había utilizado al menos una vez. La red procesa alrededor de $250 mil millones en pagos anualizados, aproximadamente el 20 % del gasto total de los clientes. Los usuarios (incluidos los trabajadores autónomos) pueden enviar y recibir pagos en tiempo real a través de una interfaz sencilla, 24 horas al día, 7 días a la semana y de forma gratuita. Las empresas tienen que pagar una pequeña tasa. En Estados Unidos, la Federal Reserve ha anunciado que lanzará FedNow a mediados de 2023, una red de pagos con características similares a PIX. Estas iniciativas tienen como objetivo resolver problemas como los acuerdos lentos y la baja interoperabilidad entre las partes. Los bancos establecidos aún poseen la mayor parte del mercado de pagos digitales, sin embargo, las fintech han estado amenazando este dominio, aprovechando su agilidad para actuar rápidamente y satisfacer las necesidades de los clientes de formas más innovadoras y creativas. Sin el lastre de los sistemas legacy, o los modelos comerciales atados a las viejas redes de pago, las fintechs no han dudado en probar y adoptar nuevas tecnologías y sistemas de pago. Su estrategia enfocada a móvil y digital les está ayudando a capturar y retener al segmento más joven del mercado, que exige experiencias integradas en tiempo real con las que pueden interactuar tan sólo pulsando un botón. Un ejemplo es Paggo, una fintech guatemalteca que ayuda a las empresas a agilizar los pagos permitiéndoles compartir un simple código QR que los clientes pueden escanear para transferir dinero. El panorama de los pagos no solo se ve afectado por fuerzas externas, los cambios que provienen de la industria también están remodelando la experiencia del cliente y habilitando nuevos servicios. La norma ISO 20022 es un estándar flexible para el intercambio de datos que está siendo adoptado por la mayoría de las instituciones de la industria financiera para estandarizar la forma en que se comunican entre sí, optimizando así la interoperabilidad. Gracias a la adopción de ISO 20022, es más sencillo para los bancos leer y procesar mensajes, lo que se traduce en procesos internos más fluidos y una automatización más sencilla. Para los usuarios finales, esto significa pagos más rápidos y potencialmente más baratos, así como aplicaciones financieras más ricas e integradas. 3DS2 está siendo adoptado por el ecosistema de pagos con tarjeta de crédito y débito. Se trata, esencialmente, de una solución de autenticación de pagos que sirve para transacciones de compras en línea. De manera similar a ISO 20022, el usuario final ni siquiera conocerá la tecnología subyacente, sino que sólo percibirá un pago más fluido y sin fricciones. 3DS2 evita que el usuario sea redirigido a su aplicación bancaria para confirmar la compra de un artículo en línea, ahora todo sucede en el sitio web o la aplicación del vendedor. Todo esto se hace al mismo tiempo que se mejora la detección y prevención de fraude; esta nueva solución dificulta el uso de la tarjeta de crédito o débito sin autorización. El beneficio de la adopción de 3DS2 es doble: por un lado, el usuario tiene mayor confianza, por otro, los comerciantes están más contentos debido a una menor tasa de abandono de clientes; de hecho, el miedo al fraude en el proceso de pago suele ser una de las principales razones para abandonar una compra en línea. Esta solución es especialmente ventajosa para la región de LAC, donde, a pesar de la amplia adopción del comercio electrónico, las personas aún se muestran reacias a realizar transacciones online. Uno de los factores que contribuyen a esta incongruencia es el miedo al fraude. Cybersource informó que en 2019, una quinta parte de las transacciones de comercio electrónico se marcaron como potencialmente fraudulentas y el 20 % se bloquearon, es decir, más de 6 veces el promedio mundial. Es evidente que la adopción de 3DS2 por parte de las plataformas fomentará la confianza de los compradores online. Vale la pena mencionar también el papel que juegan la blockchain y las criptomonedas. Redes como Ethereum o Lightning son una alternativa descentralizada a las redes de pago más tradicionales. En los últimos años, más y más personas han comenzado a utilizar esta tecnología debido a sus características únicas: tarifas bajas, tiempo de procesamiento rápido y alcance global. América Latina ha visto una explosión en la adopción debido a varios factores, siendo muy prominentes las remesas y los pagos en stablecoins. Los proveedores de servicios de remesas tradicionales son, de hecho, más lentos y más caros que las redes de blockchain. Especialmente en Argentina, un número cada vez mayor de trabajadores autónomos exigen que se les pague en USDC o USDT, dos stablecoins vinculadas al valor del dólar, para así poder protegerse de la inflación. Está claro que el panorama de los pagos está evolucionando rápidamente, por un lado, los clientes esperan productos y servicios que se integren a la perfección con todos los aspectos de sus vidas digitales. Cada vez que una aplicación se percibe como lenta, mal diseñada o simplemente le faltan algunas funciones, el usuario puede cambiar fácilmente a la alternativa de un competidor. Por otro lado, la cantidad de actores que compiten por su participación en el mercado de pagos digitales está en auge, lo que reduce los márgenes de los productos tradicionales. La única forma de navegar con éxito en este entorno complejo es invertir en innovación y en la creación de nuevos modelos de negocio. No existe un planteamiento único para enfrentarse a tales desafíos, pero no hay duda de que toda empresa con éxito necesita aprovechar el poder de los datos y la tecnología para proporcionar a sus clientes la experiencia personalizada y en tiempo real que exigen. En MongoDB creemos que una base sólida para lograrlo está representada por una developer data platform altamente flexible y escalable, que permite a las empresas innovar más rápido y monetizar mejor sus datos de pago. ¡Visite la web de Servicios Financieros de MongoDB para obtener más información!

March 15, 2023

Digital Payments - Latin America Focus

Pushed by new technologies and global trends, the digital payments market is flourishing all around the world. With a valuation at over USD 68 billion in 2021 and expectations to grow to double digits over the next decade, emerging markets are leading the way in terms of relative expansion. A landscape once dominated by incumbents - big banks and credit card companies - is now being attacked by disruptors that are interested in capturing a market share. According to a McKinsey study , there are four major factors at the core of this transformation: Pandemic-induced cashless payments adoption E-commerce Government push for digital payments Fintechs Interestingly, the pandemic has been a big catalyst in the rise of financial inclusion by encouraging alternative means of payment and new ways of borrowing and saving. These new digital services are in fact easier to access and to consume. In Latin America and the Caribbean (LAC), Covid spurred a dramatic increase in cashless payments, 40% of adults made an online purchase, 14% of which did it for the first time in their life. E-commerce has experienced a stellar growth, with a penetration that will likely exceed 70% of the population in 2022, domestic and global players including Mercado Libre and Falabella are pushing digital payment innovation to provide an ever smoother customer experience on their platforms. Central banks are promoting new infrastructure for near real-time payments, with the goal of providing a cheaper and faster technology for money transfer both for citizens and businesses. PIX is probably the biggest success story. An instant payment platform developed by Banco Central do Brasil (Brazil Central Bank), it began operating in November 2020, and within 18 months, over 75% of adult Brazilians had used it at least once. The network processes around $250 Billion in annualized payments, about 20% of total customer spend. Users (including self employed workers) can send and receive real-time payments through a simple interface, 24/7 and free of charge. Businesses have to pay a small fee. In the United States, the Federal Reserve has announced it will be launching FedNow in mid 2023, a payment network with characteristics similar to PIX. These initiatives aim to solve issues such as slow settlements and low interoperability between parties Incumbent banks still own the lion’s share of the digital payment market, however, fintechs have been threatening this dominance by leveraging their agility to execute fast and cater to customer needs in innovative and creative ways. Without the burden of legacy systems to weigh them down, or business models tied to old payment rails, fintechs have been enthusiastic testers and adopters of new technologies and payment networks. Their mobile and digital first approach is helping them capture and retain the younger segment of the market, which expect integrated real-time experiences they can consume at the touch of a button. An example is Paggo, a Guatemalan fintech that helps businesses streamline payments by enabling them to share a simple QR code that customers can scan to transfer money. The payment landscape is not only affected by external forces, changes coming from within the industry are also reshaping the customer experience and enabling new services: ISO 20022 is a flexible standard for data interchange that is being adopted by most financial industry institutions to standardize the way they communicate between each other, thus streamlining interoperability. Thanks to the adoption of ISO 20022, it’s more straightforward for banks to read and process messages, this translates into smoother internal processes and easier automatization. For end users this means faster and potentially cheaper payments, as well as richer and more integrated financial apps. 3DS2 is being embraced by the credit and debit card payments ecosystem. It essentially is a payment authentication solution that serves online shopping transactions. Similarly to ISO 20022, the end user won’t even be aware of the underlying technology, but will only experience a smoother and frictionless checkout. 3DS2 avoids the user being redirected to their banking app for confirmation when buying an item online, now it’s all happening on the website or app of the seller. This is all done while also enhancing fraud detection and prevention; this new solution makes it harder to use one’s credit or debit card without authorization. 3DS2 adoption benefit is twofold: on the one hand the user has increased confidence, on the other hand merchants are happier because of a lower customer abandonment rate, in fact fear of fraud at checkout is usually one of the main reasons for ditching an online purchase. This solution is especially beneficial for the LAC region, where, despite wide adoption of e-commerce, people are still reluctant to transact online. One of the factors contributing to this oddity is fear of fraud, Cybersource reported that in 2019, a fifth of e-commerce transactions were flagged as potentially fraudulent and 20% were blocked, that’s over 6 times the global average. It is evident how online shoppers’ trust will be encouraged by the platforms’ adoption of 3DS2. It is worth also mentioning the role played by blockchain and cryptocurrencies. Networks such as Ethereum or Lightning are effectively a decentralized alternative to the more traditional payment rails. Over the last few years more and more people have started to use this technology because of its unique features: low fees, fast processing time and global reach. Latin America has seen an explosion in adoption due to several factors, remittances and stablecoin payments being highly prominent. Traditional remittance service providers are in fact slower and more expensive than blockchain networks. Especially in Argentina, an increasing number of autonomous workers are demanding to be paid in USDC or USDT, two stablecoins pegged to the value of the dollar, thus being able to stave off inflation. It is clear that the payment landscape is rapidly evolving, on the one end customers expect products and services that integrate seamlessly with every aspect of their digital lives. Whenever an app is perceived as slow, poorly designed or simply missing some features, the user can easily switch to a competitor’s alternative. On the other hand, the number of players contending for their share in the digital payments market is expanding, driving down margins of traditional products. The only way to successfully navigate this complex environment is investing in innovation and in creating new business models. There’s no unique approach to face such challenges, but there’s no doubt that every successful business needs to harness the power of data and technology to provide its customers with the personalized and real-time experience they demand. We at MongoDB believe that a solid foundation to achieve that is represented by a highly flexible and scalable developer data platform, allowing companies to innovate faster and better monetize their payment data. Visit our Financial Services web page to learn more!

March 14, 2023

Build a ML-Powered Underwriting Engine in 20 Minutes with MongoDB and Databricks

The insurance industry is undergoing a significant shift from traditional to near-real-time data-driven models, driven by both strong consumer demand, and the urgent need for companies to process large amounts of data efficiently. Data from sources such as connected vehicles and wearables are utilized to calculate precise and personalized premium prices, while also creating new opportunities for innovative products and services. As insurance companies strive to provide personalized and real-time products, the move towards sophisticated and real-time data-driven underwriting models is inevitable. To process all of this information efficiently, software delivery teams will need to become experts at building and maintaining data processing pipelines. This blog will focus on how you can revolutionize the underwriting process within your organization, by demonstrating how easy it is to create a usage-based insurance model using MongoDB and Databricks. This blog is a companion to the solution demo in our Github repository . In the GitHub repo, you will find detailed step-by-step instructions on how to build the data upload and transformation pipeline leveraging MongoDB Atlas platform features, as well as how to generate, send, and process events to and from Databricks. Let’s get started. Part 1: the Use Case Data Model Part 2: the Data Pipeline Part 3: Automated Decision Support with Databricks Part 1: The use case data model Figure 1: Entity relationship diagram - Usage-based insurance example Imagine being able to offer your customers personalized usage-based premiums that take into account their driving habits and behavior. To do this, you'll need to gather data from connected vehicles, send it to a Machine Learning platform for analysis, and then use the results to create a personalized premium for your customers. You’ll also want to visualize the data to identify trends and gain insights. This unique, tailored approach will give your customers greater control over their insurance costs while helping you to provide more accurate and fair pricing. A basic example data model to support this use case would include customers, the trips they take, the policies they purchase, and the vehicles insured by those policies. This example builds out three MongoDB collections, as well two Materialized Views . The full Hackloade data model which defines all the MongoDB objects within this example can be found here . Part 2: The data pipeline Figure 2: The data pipeline - Usage-based insurance The data processing pipeline component of this example consists of sample data, a daily materialized view, and a monthly materialized view. A sample dataset of IoT vehicle telemetry data represents the motor vehicle trips taken by customers. It’s loaded into the collection named ‘customerTripRaw’ (1) . The dataset can be found here and can be loaded via MongoImport , or other methods. To create a materialized view, a scheduled Trigger executes a function that runs an Aggregation Pipeline. This then generates a daily summary of the raw IoT data, and lands that in a Materialized View collection named ‘customerTripDaily’ (2) . Similarly for a monthly materialized view, a scheduled Trigger executes a function that runs an Aggregation Pipeline that, on a monthly basis, summarizes the information in the ‘customerTripDaily’ collection, and lands that in a Materialized View collection named ‘customerTripMonthly’(3). For more info on these, and other MongoDB Platform Features: MongoDB Materialized Views Building Materialized View on TimeSeries Data MongoDB Scheduled Triggers Cron Expressions Part 3: Automated decisions with Databricks Figure 3: The data pipeline with Databricks - Usage-based insurance The decision-processing component of this example consists of a scheduled trigger and an Atlas Chart. The scheduled trigger collects the necessary data and posts the payload to a Databricks ML Flow API endpoint (the model was previously trained using the MongoDB Spark Connector on Databricks). It then waits for the model to respond with a calculated premium based on the miles driven by a given customer in a month. Then the scheduled trigger updates the ‘customerPolicy’ collection, to append a new monthly premium calculation as a new subdocument within the ‘monthlyPremium’ array. You can then visualize your newly calculated usage-based premiums with an Atlas Chart! In addition to the MongoDB Platform Features listed above, this section utilizes the following: MongoDB Atlas App Services MongoDB Functions MongoDB Charts Go hands on Automated digital underwriting is the future of insurance. In this blog, we introduced how you can build a sample usage-based insurance data model with MongoDB and Databricks. If you want to see how quickly you can build a usage-based insurance model, check out our GitHub repository and dive right in! Learn more about MongoDB and Insurance .

March 6, 2023