Financial Industry

Solutions for Financial Industry

Financial risk prevention and control

Financial Industry Image

Industry Background
Industry Challenges
Strengths of Rapids Data Solutions
Value Realization

As the financial markets are moving more quickly than ever before, technological innovation represented by big data, cloud computing, block chain, artificial intelligence, etc. is undoubtedly becoming the new momentum of the information revolution and provides a new driving force for financial industry transformation.

Data is reshaping the world of financial technology. Availability of flexible and scalable methods of data collection and usage will determine the core competitiveness of enterprises. Controlling data means insights into the market, resulting in fast and accurate strategies. Therefore, the IT department needs to convert itself from a “cost center” to a “profitability center”, and enterprise data will become its core asset.

Corporate strategy will shift from “business-driven” to “data-driven”, and data-based decision making will be the future of business development. Traditional financial firms made use of simple summary and consolidation of data and information without in-depth analysis of customers, operations and competition, which leads to inaccuracy and increased risk. In the era of big data, financial companies need to take steps to make the most of data. Through data mining, they can perform intelligent analysis and predict the market demand so as to formulate more effective strategies which are better aligned with business goals.

Rapids Data's big data platform with its all-memory and massive parallel database technology, conducts real-time processing and analysis of the exponential growth of data in the financial industry. It can efficiently and timely process and analyze massive and multi-dimensional unstructured information to allow users to analyze market risks, evaluate intricate financial instruments, discover new investment opportunities and ultimately reduce cost and drive new revenue streams.

  • High Value and Low Cost Rapids Data X86 PC Server cluster-based big data real-time analysis and processing platform can provide timely, high-concurrency and massive data computing, refining process of business development and reducing operating and maintenance costs.
  • Fast Release and Continuous Iteration The data layer establishes a lightweight model and imports detailed data for new demands without data pre-summary or computing. The demand initiated by each click spells out SQL in real time and send out to the calculation layer to calculate the results using the flexible OLAP mechanism, which is easy to adapt to business changes.
  • Self-service                                                     As part of multidimensional analysis, users can easily customize reports and dashboards. The front-end handles user interaction and analysis capabilities of filtering, mining, scaling, associating, transforming, dynamic computing, linking, etc., thus providing exploratory analysis capability for end users to find and resolve problems.
  • High Utilization                 Both the online and offline analysis platforms have distributed architectures. Rapids Data's distributed streaming database supports hot swapping for computing and storage nodes, which can extend from one node to dozens or even hundreds of nodes.
  • High Concurrency                                                         The online analysis platform supports high concurrency. The Data Mart as a computing layer supports distributed computing, which uses the MapReduce architecture to improve computing efficiency. The BI front-end can be directly connected with RapidsDB that uses in-memory computing technology to support a high degree of concurrency in the OLTP system. It also has the capbility to operate on multiple storage and computing nodes in parallel, making it suitable for real-time massive data analysis.
  • 01 Collect and manipulate data at the right speed, at the right time, to gain the right insights, reducing human involvement at the time of data
    •        collection, thus improving data accuracy.
  • 02 Monitor market developments in real time, utilizing internal change monitoring and key word search methodology to provide real-time updates.
  • 03 Support real-time payment, transaction, balance and other online financial service inquiries.
  • 04 Detect potential fraud in real time. Provide portfolio risk assessment to evaluate the return on investment.
  • 05 Utilize extracted data to improve business and management flexibility and provide sentiment analysis to understand customers opinions and
    •        behaviors to improve customer experience.