A huge volume of market data, historical trade data and quotes are produced by the financial industry. More than one Tbyte per day is written alone by the New York Stock Exchange. The velocity of the big data will depend upon the speed of data processing or storage. Nearly 105 transactions are done per second and to generate at this speed is really not a challenge in the financial markets. The trade data can be processed with the faster systems which will help them to manage the trade.
Financial sector:
New regulations are implemented in a constant stream which is an interesting part in the financial sector of the big data. The new data sources are brought by reporting the standards. The complex metrics are also considered in the financial system. The data scientists found that the financial sector is a very interesting place. In one form or the other the algorithmic trading is used from a long time in the financial sector. Large orders are now broken into small pieces by the algorithmic trading systems. The algorithmic systems execute automatically depending on the price, volume and time which is optimized for the market parameters.
In the financial institutions for the purpose of reporting, a large volume of data is used continuously for the processing. Various complex metrics are required for the calculatin the regulations in the financial and banking market. The profitability of the bank can be influenced by directly by setting the minimal reserves of a bank by using the metrics. The model market and the customer behaviour can be analysed by the transactional data in a sequence of time.
Approach:
The approaches of big data are adopted slowly by the some of the big financial institutions. The PWC has clarified in a market research to adopt these approaches in some of the institutions. The industrial sector also has some relevance in the approaches of big data. Some of the technical problems can be solved by using the big data algorithms and it is said by some of the managers in the financial sector. But all the business problems cannot be solved with the algorithms of big data. The technology will support the business when the data is generated in the business to obtain the results.
The data streams cannot be understood by some people as they have no idea on how to gain value. Some people say that the technical efficiency can be improved by using the approaches of big data. The business growth can be supported and growth can be improved in the big data with deep analysis.
Market Patterns:
The investment strategies are provided with input by the adaptive models of the trading patterns in the market. These strategies will help them to buy and sell the assets. To create strategies some models are used by the government tax authorities and traders.
The applicants are able to produce the credit score in real time by processing the data from the small and medium businesses. The banking is relevant to the specific applications in the financial sector for the consumers. The information from the application form and other sources will be collected by the bank when a person applies for the credit. A client will have credit proposal created by the information which is analysed by the specialists. This will include the terms of repayment and the rate of interest. The trade-offs may be included as a negotiation between the bank and the applicant with various loan parameters with the overall terms.
