Financial services organizations are reinventing their back office systems due to new regulatory pressures and increased competition in a global market. Regulations such as the Dodd-Frank Act and European Market Infrastructure Regulation (EMIR) are driving requirements for greater transparency of financial transactions, in turn increasing data volumes across the business, from financial products to risk data. Financial services organizations are reacting to these demands by redesigning their core services on the leading NoSQL database, MongoDB. With MongoDB organizations can innovate at scale, and adapt to the rapid demands of the market.
Example Financial Services Solutions
Risk Analytics and Reporting. MongoDB's flexible and dynamic schema model makes it simple to aggregate data from multiple sources, such as front office systems. For example, organizations can analyze multiple risk metrics to create a single view of exposure across asset classes or counterparties. MongoDB’s dynamic query language allows granular access to any data attribute, and the native Aggregation Framework provides a powerful tool for grouping, reshaping and analyzing data at massive scale. These features make MongoDB the ideal platform for consolidating risk measures and for providing a powerful reporting platform for a number of risk management applications.
Reference Data Management. Managing and distributing reference data globally has always been a challenge. Managing and maintaining database schemas while integrating and replicating that data across geographies is costly and time consuming. MongoDB's native replication capabilities and partitioned architecture make it simple to distribute and synchronize data efficiently across the globe. MongoDB’s dynamic schema dramatically reduces database maintenance for schema migrations – data structure changes can be applied with no down time, and with no impact to existing applications.
Market Data Management. Financial institutions consume numerous high volume data feeds, including tick data, FIX messages, FpML, news, social media and log files from other enterprise systems. MongoDB's high-speed ingestion rate makes it ideal for storing, processing and analyzing diverse data sets in one massively scalable system in real-time. Firms use MongoDB to look for trading signals in their data feeds, to serve as the data store for market data, to provide the basis for back testing or automated trading, and to detect money laundering and other types of fraudulent activities.
Trade Repository. Due to changes in legislation, organizations are now required to store trade data to allow for greater transparency and regulatory reporting. Acts in the United States and Europe, for example, are driving organizations to maintain records of OTC Derivatives over long periods of time, often 7 years. The data associated with trades can grow quickly to immense volumes. MongoDB's horizontal scale-out architecture helps ensure that database capacity can be grown on commodity infrastructure as volumes increase over time. MongoDB’s dynamic, flexible schemas and fast in-place updates allow for varied trade data to be maintained in a single store and queried and updated throughout the trade lifecycle.
Other use cases for MongoDB in financial services include:
- Product Catalogs
- Trade Capture
- Portfolio Management
- Quantitative Analysis & Automated Trading
- Order Capture
- P&L Reporting
- Time Series Data
- Bi-Temporal Data Models
Watch one of our financial services presentations and learn how your organization can leverage MongoDB to support your business objectives:
- How and Why Leading Investment Organizations are Migrating to MongoDB
- Real World MongoDB: Use Cases from Financial Services
- How Financial Firms Create Single Customer Views Using MongoDB
- How Banks Use MongoDB to Manage Risk
- How Banks Manage Reference Data with MongoDB
- How Banks Use MongoDB as a Tick Database
- Position and Trade Management with MongoDB