GPU-Accelerated Database for Financial Services

In an industry where milliseconds matter and where insight directly equates to money, machine learning, deep learning and faster analytics offer a distinct competitive advantage. Kinetica makes it possible for financial organizations to derive insights and make predictions from vast volumes of complex and streaming data in milliseconds. Use Kinetica for AI and truly real-time analytics demands including fraud analysis, risk management and algorithmic trading.
Used By:

Streaming Analytics Database, Ideal for Financial Workloads

10x-100x faster query on 1/10 the hardware, compared to even the most advanced in-memory analytics databases.

Lightning Fast Query

In tests, Kinetica returns results for advanced analytical queries on billions of rows of data in well under a second.
Fast OLAP Queries »

Ideal for Streaming Data

Parallel ingest and reduced reliance on indexes means data on Kinetica is available for query the moment it arrives.

Converged AI and BI

In-database processing makes it possible to run customized, GPU-accelerated, predictive modelling on the same platform as traditional analytics and data exploration.
Machine Learning in Finance »

Geospatial Visualization

Visualize patterns on geospatial and temporal data. Kinetica harnsses the GPU for query and drawing geospatial display of vast volumes of data.
Location-Based Analytics »

Webinar: GPU Acceleration in Finance

View the Recording

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

Kinetica has numerous use cases that can be applied to operations, customers, and markets.

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 »

Risk Management

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.

Regulatory Compliance

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 Decisioning

Measuring 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 Trading

Algorithmic 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 Detection

Performing 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 Analytics

Banks 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.
GPU Data Analytics eBook
FREE eBook

Introduction to GPUs for Data Analytics

Advances and Applications for Accelerated Computing

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.