Resources | MongoDB

How AI is re-shaping financial services with Finextra, AWS, Tietoevry Banking, and MongoDB

There is no precedent for the impact that AI, once its full potential is realized, can have in the financial sector. In the world of payments, large language models, pattern recognition and machine learning are playing a substantial role when it comes to advanced fraud detection, streamlined settlement and credit decisioning. But are organizations leveraging all the benefits AI can offer? The capture and use of data, which is in need of upheaval and change, is another area where AI plays a crucial role. Financial organizations need to generate real-time insights from massive amounts of continuously growing datapoints. AI has the potential to identify areas of improvement, new revenue opportunities, and trends that might otherwise be missed, but successful AI initiatives are only possible with the right technology stack. Watch this Finextra webinar, hosted in association with MongoDB and Amazon Web Services (AWS), to watch our panel of industry experts who will discuss the opportunities and use cases for AI in the world of payments. Speakers: Gary Wright: Head of Research, Finextra Shiv Pullepu: Principal, Financial Services Industry Solutions, MongoDB Ruben Falk: Generative AI Lead, Capital Markets & Financial Services, AWS David Park - CTO, Tietoevry Banking

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Continuously Updating Vector Embeddings for Gen AI Apps

The solution uses MongoDB Atlas Stream Processing and Atlas Vector Search to continuously update vector embeddings with data received from an Apache Kafka data pipeline. Our Senior Consulting Engineer, David Sanchez walks developers through continuously updating, storing, and searching embeddings with a single interface. And it shows why the MongoDB document data model is so well suited to stream processing. The webinar details: How to set up and configure the environment. How to create vector search indices in Atlas. How to create a private and scalable embedding generator system using purpose-built LLMs. How to interactively run semantic queries.

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Continuously Updating Vector Embeddings for Gen AI Apps

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Building Powerful Gen AI Applications with Google Cloud and MongoDB

Learn how MongoDB, in collaboration with Google Cloud, can help developers build and deploy gen AI-powered apps faster and at scale. In this the webinar, you'll learn how to: Simplify data management — Discover how MongoDB Atlas on Google Cloud eliminates database complexities. Free your team to focus on what truly matters: innovation. Unlock data's power — Learn how MongoDB's flexible platform empowers you to build modern, data-driven applications with ease. Dive into the world of generative AI and see how it can transform your business. Accelerate your cloud journey — See how MongoDB Atlas seamlessly integrates with Google Cloud, leveraging the power of Vertex AI to enhance development and drive growth. Take your cloud capabilities to the next level.

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Building Powerful Gen AI Applications with Google Cloud and MongoDB

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Enhance React Applications with Generative AI LLMs

Large language models (LLMs) like OpenAI’s GPT provide powerful natural language processing capabilities. Integrating those capabilities with React empowers developers to create dynamic, intelligent user interfaces that provide personalized user experiences. Enabling those user interfaces to remember earlier conversations and incorporate that memory as context in new interactions makes AI even more intelligent and human-like — and the application even more effective. Senior Developer Advocate Jesse Hall explores and explains how to achieve this in JavaScript and React. The app uses Next.js, LangChain, and MongoDB Vector Search to create an application that extends the benefits of LLMs by creating a memory store that enables the app to recall and use previous conversations. Adding this historical insight allows the chatbot to offer responses that are more relevant and tailored to the individual user. The webinar shows how to: Setup the environment and database. Create a naive RAG chain with a MongoDB vector store. Add a chat history to the RAG chain to improve answer relevance and accuracy.

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Mastering MongoDB Atlas Sharding and Monitoring

Watch an exclusive webinar focused on MongoDB Atlas Monitoring and Sharding. This session is designed to provide developers with the insights and best practices needed to effectively monitor and scale their MongoDB Atlas deployments. Our expert speaker will guide you through the essentials of setting up and managing sharding, followed by real-life use cases and solutions to common challenges.

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Transforming Your Business Through Artificial Intelligence

Today, AI-powered intelligent applications are being used to transform business processes and thereby improve their performance, efficiency, effectiveness, and agility. Enterprises all over the world, in all industries, and of all sizes are implementing digital business transformation to varying degrees. Advances in cloud computing, real-time stream and event processing, low-latency data fabrics, and more – all accelerated by AI – are driving a continual feed of real-time data updates, contextual insights, optimized experiences, and fast results into all business processes. This TDWI Best Practices Report explores key trends, considerations, and opportunities associated with successful implementation of AI in enterprise digital business transformation initiatives, including: What challenges enterprises must address & key success factors in using AI for digital transformation The role of AI in transforming business processes and decision-making

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MongoDB & Amazon Web Services (AWS) Fraud Detection Immersion Day (APAC)

Existing fraud detection systems often grapple with a critical limitation – relying on stale data – which limits the ability to access data in real-time to prevent fraudulent activities before they happen. Join MongoDB and AWS for a deep dive on-demand virtual workshop into Fraud Detection using MongoDB Atlas and Amazon SageMaker Canvas to learn how you can proactively identify and combat fraudulent activities, safeguarding systems and protecting customers. By leveraging MongoDB Atlas as an operational data store you are able to accommodate high-volume transactional data with exceptional performance and flexibility. Coupled with Amazon SageMaker Canvas, leverage AI/ML solutions effortlessly with a no-code platform that brings the power of advanced analytics for fraud detection to your fingertips. In this on-demand virtual workshop, you will learn: How to architect, design, build, and deploy applications with MongoDB Atlas in the AWS Marketplace How to identify and prevent fraudulent activities or transactions with a modern data architecture Data analytics and machine learning techniques to identify patterns and anomalies How to monitor transactions in real-time, analyse historical data, and flag any suspicious activity with with MongoDB Atlas and Sagemaker Canvas

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Atlas Search eWorkshop - English

Watch this exclusive workshop to discover why MongoDB Atlas is the ideal platform to build modern semantic search apps, leveraging text search, vector search and large language models. During this time, you will learn how to: Leverage Atlas to build a semantic search using LLMs for pre-summarization and information extraction Learn how to get started developing your first semantic search app Atlas with a free cluster Use automatic client side encryption to store user secrets securely (we're going to access users Google Drive Docs!) Learn about the pre-summarization technique, an alternative to chunking techniques Store LLM interactions and keep track of model usage and costs using Atlas Charts Use langchain to compare costs & performance of various models, featuring open-source models, OpenAI, Anthropic, Cohere & Mistral. Implement access control for vector search Build a basic RAG pipeline to search across stored facts

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Intro to Data Modeling

In this webinar, Principal Developer Advocate Michael Lynn will examine the most important considerations when making decisions about your data model, so that you can create the model that best fits your application. You will learn: The differences between data modeling in MongoDB vs. relational databases The key considerations for data modeling with MongoDB How to further enhance the power of the document model with schema design patterns

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Transforming legacy banking systems to build intelligent applications with MongoDB and Microsoft

From shifts in the market to changes in the regulatory landscape to aging legacy systems, financial institutions are under pressure to modernize their legacy core systems and build intelligent applications. At the heart of it all lies software and data, architecting systems for real-time adaptability, scalability, and resilience. Modernizing these core banking platforms enables financial institutions to better serve customers by using data and AI to deliver impactful business outcomes. Learn how MongoDB Atlas on Microsoft Azure enables you to unify operational, analytical, and AI data services to streamline building intelligent applications. In this webinar, see how to: Change the way you think about data in the face of current trends and challenges Modernize your approach to core systems surrounding real-time payments and fraud prevention Migrate legacy systems to modern platforms with generative AI Drive product innovation and operational efficiency with flexible modern platforms like AI/ML You will hear from: Shiv Pullepu: Principal, Financial Services Industry Solutions at MongoDB Thomas Mathew: Senior Director Partner Technology for Financial Services at Microsoft

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