VoltDB in Financial Services

paper-icon-miniBack Office Real-Time Risk Analysis in High Demand
In this Wall Street & Technology article, Marc Firenze – CTO of BNY Mellon’s Eagle Investment Systems – discusses why he chose VoltDB to power Eagle’s solution for real-time, back–office risk analysis.
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paper-icon-miniJingit Shares Knowledge
Jingit, an integrated payment and cash-based transactional advertising platform, praises VoltDB for powering its high-volume transaction processing.
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voltcast-icon-miniEagle Investment Systems: Powering Competitive Advantage
In this brief audio clip, VoltDB’s VP of Engineering, John Piekos, talks with Marc Firenze, CTO of Eagle Investment Systems – and uncovers how VoltDB gives Eagle a significant competitive advantage in delivering real-time analytics and decisioning capabilities to some of the most successful financial organizations in the world.
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videos-icon-miniWebcast: Real-Time Analytics for Capital Markets
To make the best decisions faster, financial trading lenders use VoltDB for pre-trade compliance, real-time risk analysis and other real-time transactions.
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The global economy moves fast. Keeping track and making sense of banking and trading transactions requires in-the-moment insight on reams of data. VoltDB’s in-memory relational database gives financial institutions and businesses the real-time data they need to understand and react to the staggeringly high number of transactions that make the financial world go round.


Real-time risk and position monitoring

Market and counterparty risk consistently threaten financial trading firms that use unsophisticated technology. Not to mention, those outdated systems offer only delayed and fragmented information and fail to show a real-time, consolidated view of an entire trading operation. Real-time aggregation and valuation provides current and readily available data, giving trading firms in-the-moment insight to act on the threat of risk and also see a complete picture of trading operations.


Credit fraud detection

Banks and financial service companies face tremendous pressure to detect and stop credit fraud. To have the most accurate and least intrusive fraud detection system possible, banks and financial institutions have started using data analytics technology. They generate large volumes of data at a high velocity to have a clear, real-time picture of user information, buying habits and other consumer patterns to stop credit fraud before it happens.


Transactional applications

Financial institutions increasingly rely on software solutions to perform difficult investment accounting, data management and performance measurement tasks. But many of those applications can’t handle the rapidity of data ingestion, real-time analytics and decisioning without the support of an in-memory, high-velocity database. Only real-time data visibility will deliver the full potential of those critical applications and allow companies to stay ahead of competitors.


Real-time liquidity

Surprisingly, many banks continue to keep separate streams of data on payments and transactions, rather than linking the information for a holistic view of liquidity. But as financial institutions start to make the most of Big Data, they will begin constructing systems that merge the data to better understand and respond to potential monetary shortfalls. With real-time information, a more centralized and automated payment and transaction system will give an accurate and full view of liquidity.


Micro payments

Micro payments hold the promise of redefining ecommerce. For instance, people can listen to streaming music on smartphones and pay for each second they listen to a song. Or they can pay a small fee for each article read from a news source. This promise will be met only if the monetization platforms behind these services ingest, analyze and act on data in real-time to gauge the exact amount of use and charge accordingly.