New regulations. More global competitors. Calls for transparency and accountability. To meet these challenges, financial services firms are reinventing their core systems with MongoDB. They are analyzing trade signals faster, getting a single view of their customers, and reducing risk. Here’s how.
MetLife built a single view of 100M+ customers across 70 different systems in just 90 days using MongoDB. It had been trying for 8 years to build the same application with a relational database. Customer service just got a lot better.
Other Financial Services Customers
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AHL Man Group
100x lower latency at 40x lower cost for market data management.
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Financial Services Use Cases
Risk Analytics & Reporting. Financial institutions need to consolidate and analyze multiple risk metrics to create a single view of exposure across asset classes or counterparties. A European equity derivatives institution uses MongoDB’s dynamic query language to allow granular access to any data attribute. MongoDB’s native aggregation framework gives them a powerful tool for grouping and reshaping of data at massive scale for intraday analysis.
Reference Data Management. A global financial services institution estimates 5-year savings of $40m after migrating to MongoDB. Data can be quickly distributed across geographies for local consumption using MongoDB’s native replication. Each business unit operates on more accurate data. The BU reduces the risk of regulatory penalties levied from reporting on outdated information and they eliminate expensive licensing of multiple technologies.
Single View of the Customer. Creating a single view of your customer allows better identification of upsell opportunities. You can more accurately predict churn and improve customer service. After 8 years struggling to build a single view of its customers, MetLife tried a new approach. MongoDB’s flexible schema enabled the project team to seamlessly blend data from 70 separate source systems. They delivered the application in just three months.
Market Data Management. By moving to MongoDB, AHL / Man Group was able to scale to 250M ticks per second. Compared to its previous database, AHL experienced a 25x improvement in throughput, 100x lower latency with 40x cost savings. High speed data ingestion and analytics, coupled with simple scale-out enables AHL to better identify trading signals in its market data feeds.
Trade Repository. Financial institutions are mandated to store trade data for 7 years or more. A global leader in research and investment management has been able to reduce costs by scaling out data storage on commodity hardware. MongoDB’s flexible schemas enables them to integrate diverse trades in a single database.
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