Breakdown ETL Bottlenecks
In the high-stakes world of financial services, massive data volumes and real-time demands require solutions that deliver both speed and scalability.
Voltron Data provides an accelerated SQL engine, Theseus, that eliminates ETL bottlenecks by leveraging your under utilized GPU infrastructure to process your most challenging workloads.
Theseus Accelerates FSI Pipelines
Blazing Fast Joins at Massive Scale
Power through multidimensional queries with heavy joins, large sorts, and massive aggregations—without complex transformations, indexing, or denormalization. Voltron Data handles the toughest ETL seamlessly, delivering unparalleled performance at a massive scale.
Real-time Insights from Raw Data
Transform raw telemetry, log, and transactional data directly into actionable insights. With Voltron Data, you can analyze data directly from the source without indexing it first, powering predictive maintenance, cybersecurity, and financial intelligence in real-time.
ML and Ai-Ready Pipelines
Run data analytics and AI/ ML workflows on the same GPU infrastructure, effortlessly. Voltron Data bridges raw data to advanced LLM models, enabling seamless pipeline connections with unparalleled speed and efficiency.
Financial Services Have a Big Data Problem
Skyrocketing System Costs? Time to Take Control.
As financial data volumes explode, the pressure for faster, smarter analytics grows. Rising infrastructure and energy costs are breaking the cloud model at scale. Some are even shifting back to on-premises solutions to cut costs without sacrificing speed. Stay ahead — spend smarter.
Scaling Data Shouldn't Mean Scaling Problems.
Bigger data, more sources, skyrocketing costs — it's a recipe for disaster on outdated systems. Massive Spark and Presto clusters are draining budgets and dragging down performance. It's time to rethink analytics at scale — streamline, optimize, and augment with accelerated computing.
Under Pressure? Don't Compromise on Data.
When query times lag, teams make tradeoffs by downsampling data to meet SLAs. But partial insights won't cut it in today's complex financial landscape. Full data scans within tight SLAs are the key to detecting fraud, assessing risk, and making smarter decisions — stop compromising.
AI-Powered Threats Demand AI-Powered Defense.
With AI in everyone's hands, attacks are faster, cheaper, and more frequent than ever. Fighting back means feeding your defense systems with high-quality data — because AI is only as strong as the data driving it. Stay sharp, stay secure.
Financial Services Use Cases
Solve the biggest data problems with Theseus. Here are some typical use cases we solve with large-scale data processing applications across FSI.
A fast query engine can analyze vast amounts of financial data with immediate recognition of fraudulent activities. Analysts can quickly drill into transaction logs and other data to investigate the scope and nature of the fraud, making informed decisions faster. After fraud is detected and mitigated, fast querying facilitates thorough post-incident analysis, improving future defenses.
Analyze your transaction data where it sits without centralization and perform rapid transformation. Query data without worrying about indexes and joins at blazing-fast speeds, enabling monitoring and analysis of financial transactions.
Better portfolio management requires better models built on a wider set of data. We provide accelerated tuning of models with a wider array of data, enhancing the accuracy of asset allocation, risk assessment, and performance optimization.
Fast query engines facilitate the analysis of historical financial data to identify trends and patterns that can predict future risks. This provides financial analysts with rapid access to relevant data, aiding in informed decision-making and strategy development by quickly running simulations and what-if scenarios against different market conditions and risk factors.
Leverage GPU acceleration to process and analyze massive volumes of market data. This enables financial analysts to identify emerging trends, perform sentiment analysis, and forecast market movements with higher accuracy, supporting informed investment decisions.
Utilize high-performance query engines to analyze extensive datasets from various sources, including credit bureau reports, transaction histories, and alternative data. This accelerates the credit scoring process and enhances risk assessment models. Incorporate advanced techniques such as Monte Carlo modeling and other types of credit risk modeling to better predict potential defaults and financial stability. These improvements enable the ability to offer tailored credit products and more accurately assess credit risk, ensuring a more robust and reliable financial evaluation process.
GPU-accelerated analytics can rapidly process and correlate vast amounts of financial transaction data to detect suspicious activities and patterns indicative of money laundering. This enhances the effectiveness of AML programs and ensures compliance with regulatory requirements through real-time monitoring and reporting.
Rapidly analyze large volumes of financial data to monitor and manage liquidity risk. GPU-accelerated processing helps financial institutions maintain adequate liquidity levels, perform stress testing, and forecast cash flow requirements, ensuring they can meet their financial obligations even under adverse conditions. This also includes compliance with Comprehensive Capital Analysis and Review (CCAR) and Global Capital Adequacy Reporting (GCAR) requirements, providing robust tools to assess and demonstrate capital sufficiency.