Leverage structured and unstructured data to capitalize on the automation and scale of algorithmic trading. Optimize models to learn from market sentiment, trends, and historical analysis.
Analyze trends in news and social media using disparate sources and develop predictive models to assess trader performance, identify client behaviors, and produce customized offerings.
Reduce market and credit risk with real-time analysis and mitigation, manage compliance and reputation at the macro level, and proactively expose operations risk to prevent trade lifecycle losses.
Break down data silos and handle different data types seamlessly, store trade-related data in native formats, and build sentiment-based surveillance to identify patterns.
Enhance analysis using increasingly disparate data and emerging trends. Perform multi-risk analysis and scenario modeling with increased automation and self-learning.
Capture and store financial and trade-related data together, respond to compliance and regulatory requests with confidence, and analyze pre-trade communications to gain insights and detect errors.
With a layered security approach, including client-side field-level encryption, MongoDB Atlas ensures compliance with global data security standards.
Easily create, share, and embed visualizations of your data from MongoDB Atlas to create quick, easy, real-time business insights.
Run your data where you want, and enjoy unparalleled reliability, security, and flexibility with a multi-cloud or on-premises environment from MongoDB.
Deploying systems globally with different regulations is complex. Cluster-to-cluster sync allows banks to deploy hybrid systems to meet data residency and other mandates.
Trading systems must scale as business and regulatory requirements evolve. Automatically scale up or down with demand, optimizing resource usage and costs.
Build smarter applications, increase the speed of business insights, improve trading profits, and reduce errors with application-driven analytics.