GPU-Accelerated Database for Financial Services
Streaming Analytics Database, Ideal for Financial Workloads
Ideal for Streaming Data
Converged AI and BI
Machine Learning in Finance »
Webinar: GPU Acceleration in Finance
Kinetica’s Eric Mizell, VP of Global Solution Engineering, and NVIDIA’s Charlie Boyle, Sr. Director Product Marketing, are joined by featured speaker Gerald A. Hanweck, PhD, CEO and Co-founder of Hanweck, to present 'How GPUs are Accelerating Analytics for Finance.'
Transforming Finance with Truly Real-Time Analytics
With Kinetica’s user-defined function (UDF) capabilities, data exploration, model development/scoring, and model consumption can all be performed on a single compute-heavy platform. This means that you can now perform complex queries on demand without needing to move data between systems. This solves the data movement challenge and enables a much cleaner architecture for AI workloads. Machine Learning in Finance »
Real-time and intra-day risk management is a major problem facing the financial industry today, and it’s pushing conventional computing to its limits. Kinetica is being used to rapidly visualize and simulate multiple scenarios and reveal risk exposures, so that suspicious activity can be detected in seconds, not hours. Financial institutions can use Kinetica to perform risk calculations on demand using the most up-to-the-moment data with sub-second speed. This ability allows them to make better informed investment decisions and react quickly to market events, while reducing credit risk.
With Kinetica, you can run more complex market or counter party risk calculations and obtain results intra-day rather than overnight. The speed and quality of information gives you deeper insight into your exposures, enabling you to rapidly adjust positions and reduce risk. Use Kinetica to auto identify accounts at risk and execute credit line reductions/eliminations to reduce balance sheet charge-offs and loan loss reserves.
Financial institutions often take a fragmented approach to dealing with fraud and compliance. In addition, the ad-hoc query demands of regulatory compliance are frequently slow and cumbersome on the large datasets involved. With Kinetica, organizations can meet growing global compliance demands, while leveraging its horizontally scalable architecture for reporting on a massive scale. The result? Reduced compliance and regulatory costs.
Kinetica offers banks and investment firms near-real-time tracking of their risk exposures, allowing them to monitor their capital requirements at all times.
Trade DecisioningMeasuring risk, spotting customer behavioral patterns, and discovering upsell opportunities are some of the workloads that are becoming too large and too slow for traditional RDBMS. Kinetica’s ability to ingest and query data at scale offers financial institutions a myriad of ways to cut costs and improve profitability. High-frequency trading firms, traditional asset managers, and traditional lending institutions can use Kinetica to measure risk, spot customer behavioral patterns, and discover upsell opportunities.
Algorithmic TradingAlgorithmic trading uses vast historical data with complex mathematical models to maximize portfolio returns. In addition, financial firms need to be able to run analytics against massive, live, streaming data. With Kinetica, you can manage a huge amount of tick data by loading all of the data from all of your sources into memory and leveraging the power of GPUs to enable a more real-time user experience.
Fraud and Cyber Threat DetectionPerforming ad-hoc analysis on the volumes and disparate types of data necessary for fraud detection is a challenge for even the most advanced data systems. Kinetica is uniquely able to enable those who model fraud to perform queries on large streaming datasets in order to uncover anomalies and patterns of behavior that signal potential fraud. Specifically, Kinetica can be used to combine data feeds with anomaly detection, monitor multiple streams of global attack vectors, find security lapses, and mine system logs. By applying machine learning and analytics, financial institutions can crunch petabytes worth of data in order to detect advanced persistent threats in real time and react quickly to abnormal/malicious activity
Counterparty Risk AnalyticsBanks can improve their ability to make prudent business decision by employing counterparty credit risk (CCR) capabilities. Kinetica can be used to run more complex market or counter party risk calculations and obtain results intra-day rather than overnight. The speed and quality of information gives you deeper insight into your exposures, enabling you to rapidly adjust positions and reduce risk. Kinetica provides the speed, performance, reliability, and scalability needed to calculate counterparty risk with no downtime, and it can also support real-time alerting if a risk threshold is surpassed.
Discuss Finance Use Cases
Chat with one of our industry specialists for more in-depth use-cases and examples of how GPU computing and Kinetica is being used in financial services and insurance organizations today.
We can also walk you through a demo, and set you up with a trial environment for you to experience it for yourself.