Financial Services Industry Solutions
As the financial markets are moving faster than ever before, technology innovation represented by big data, cloud computing, block chain, and artificial intelligence is becoming the new momentum of the information revolution and provides a new driving force for financial services industry transformation. Legacy data architectures can no longer effectively meet the new requirements of financial activities such as risk analysis and management, fraud detection, portfolio management, future market demand prediction and so forth. In the era of big data, financial services companies need to leverage the power of new technologies to accelerate data-driven insights and monetize the value of data.
|01||The financial services industry is extremely data-intensive. Financial institutions usually have enormous amounts of customer and transactional data, which traditional data systems are unable to process sufficiently.|
|02||While legacy data systems can only process structured data in batches, streaming data generated by IoT devices has added more complexity to workflow and led to a complete transformation of operating procedure in financial services.|
|03||Hedge funds, money managers and investment banks are often required to build complicated investment portfolios. The capability of integrating all historical and real-time data feeds they have is critical to ensure availability of data and increase data analysis integrity.|
|04||AI is becoming increasingly important especially in computer aided trading. Data needs to be analyzed further to find hidden patterns in real time to capture perishable insights and support time-sensitive trading decisions.|
|05||Financial institutions demand new data systems to properly identify and respond to a fraudulent event in order to prevent the risk of loss.|
|06||Traditional data infrastructures do not provide strong support for data governance, which can lead to data misuse and noncompliance of data regulations.|
|01||The distributed and in-memory computing architecture of RDP guarantees high-performance while empowering users to explore massive amount of data in a more scalable and flexible way.|
|02||The federated data integration system of RDP allows users to easily identify and combine different types of data across a wide variety of data sources without the need for ETL.|
|03||The in-memory architecture of RDP converges transactional and analytical processing. The federation system can integrate streaming data and non-streaming data. As a result, real time data analysis can be achieved to support time-sensitive decision-making.|
|04||The AI supporting capability of RDP works intelligently to streamline financial processes by providing fast and actionable insights through automation.|
|05||RDP offers an ANSI SQL standard and user-friendly self-service interface to reduce operational complexity and allow business users without subject-matter expertise to be able to access and analyze their data.|
|06||The unified system reduces data governance complexity.|
Cluster-based big data real-time analytics platform supports high-concurrency and massive data computing in real-time, refining the process of business development and reducing operating and maintenance costs.
Agile development model
The data layer incorporates a lightweight model, which does not require pre-calculation for new data analytics needs. It also provides flexible support for a hybrid mechanism of OLTP and OLAP.
Users can customize reports and dashboards with ease. The front-end interacts with users and enables them to find and resolve problems by themselves.
Rapids Data's distributed framework allows horizontal expansion based on growing business needs.
Leveraging the in-memory computing technology, RapidsDB supports a high degree of concurrency. It operates on multiple storage and computing nodes in parallel, making it ideal for real-time massive data analysis.
|01||The federated data integration system of RDP offers a timely and cost-efficient way to support data-driven decision-making on all available data streams.|
|02||Financial organizations can respond to significant events in real time, enabling them to identify investment opportunities or hidden risk factors in a live market, detect fraud attempts as soon as they occur, qualify loans or credit card applications based on a faster and accurate risk assessment, or respond to customer service calls or ad hoc queries in real time for greater customer satisfaction.|
|03||Rapids ParallelAI enables users to build machine learning models to achieve high-efficiency and low-latency data processing and analysis. The advanced analytics trains models to learn new patterns to determine best buy/sell pricing, detect new threats, forecast market expectations, reduce investment risks, identify billing and payment errors, and prevent fraudulent activities.|
|04||Users can customize dashboards to filter, mine, scale, associate, transform, compute and link data to generate reports based on their changing needs, providing exploratory analysis capabilities for end users to find and resolve problems.|
|05||Integrated with Kerberos, RDP provides fine-grained access control along with user authentication and other methods to ensure data security. Utilizing the ParrellAI technology, machine learning models can be built and trained to automatically recognize intruder patterns to reduce the threat of unauthorized access to data.|