Data is the biggest in the IT industry. Many people think that this data increases the storage space in the local space/ cloud. And if you think the same, then you have gone wrong. This is because we can utilize this data for various purposes. Moreover, this data is more and most useful for marketing people. These people utilize this data for remarketing their customer. This remarketing is an instance. Likewise, we people can utilize this data for a variety of purposes in various industries. And in my previous articles of the blog, I have explained to you regarding the applications of big data in different industries. Hence today I would like to elaborate more on Big data analytics application in the Finance industry.
Hence prior to knowing about the big data analytics applications in the Finance industry, let us initially know,
Big data Analytics is the process of examing large and the varies data sets. This Analytics reveals the information of hidden patterns, market trends, unknown correlations, customized preferences and so on. Through this data, Analyst, data scientist, data modellers to analyze the growing volumes of the structured transaction data. All these analyses help marketers to make the best decisions for the best growth of the organization.
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Since we people have got a brief idea regarding what is big data analytics, let us move how it impacts the Financial sector
Since the financial sector is the most data-intensive sector in the global economy, the impact of big data is hard to estimate. Usually, financial organizations maintain a huge amount of user data. This data includes the transaction w.r.t the customer account. Since the banks are silos in the organizations, they are not good at utilizing this rich data sets. Hence the financial institutions are struggling more in the process of data collection and processing more than a decade. Moreover, due to the huge and increased usage of the customer requirements, financial institutions do not let this data to be unexploited. So these financial institutions were utilizing this big data analytics to maximize the user experience and gain a competitive advantage. Even though many organization were using this, they have still lagged w.r.t the benchmark. So they were concentrating more and more on Big data Analytics.
So without wasting much time, let us see
Big data in Finance refers to the petabytes of data. This data can be used to anticipate customer behaviour and to create the strategies for the banks as well as the financial institutions.
Since the finance industry has a huge number of customers, data originates in both structured as well as the unstructured format. Here the structure data refers to the information, that can be managed within the organization to provide the key-decision making insights. On the other hand, unstructured data exists in multiple sources in large volumes to offer significant analytical opportunities.
Since for giant organizations like banks, transactions take place in terms of dollars, analysts are responsible to analyze this in a keen manner. Moreover, the value of data is dependent on how it is gathered, stored, processed as well as interpreted. This is important because the legacy systems were incapable of handling the unstructured, siloed data.Hence with the ability to analyze the diverse data sets, financial companies can make a various informed decision. These decisions include improved customer services, fraud prevention, better customer targeting, risk exposure assessment and so on.
Since the Financial system is the huge system organisations have undergone through a lot of conversions that are required for behavioural as well as the technological change. In the past few years, big data analytics in Finance has led the technological innovation that enables convenient, personalized and secure solutions in the industry. Hence Big data analytics has managed to transform not only to the individual process but also the entire financial sector. And it has revolutionized the various areas of the finance industry in the following manner
Machine learning is responsible for changing trade and investment. Rather than simply analyzing the stock prices, big data now are taken into account in various areas. This includes political and social trends that affect the stock market. Moreover, machine learning monitor trends in real-time allow an analyst to compile and evaluate the appropriate data to make smart decisions.
Today Machine learning fueled by big data is responsible for fraud detection and prevention. For instance, the security risk posed by credit cards has mitigated with analytics that interprets the buying patterns. So when the valuable credit card is stolen, banks can instantly freeze the card and the transactions. Moreover, it also notifies the customer regarding customer threats.
Today big financial institutions decisions like decisions and loans rely on unbiased machine learning. Moreover, the calculated decisions based on predictive analytics into the account for every aspect. This includes economy, customer segmentation and business capital to identify potential risk like bad investment (or) payers.
Since we got a brief idea regarding how big data analytics revolutionized the Finance sector, let us move further with
Today Financial institutes have the ability to use big data in real-time use cases. This includes generating a new revenue stream with data-driven offers, delivering recommendations to the customers, improving efficiency to drive the competitive advantages. Moreover, it is also responsible for strengthening security and deliver better results to the customers. Practically this Big data analytics is useful to the financial institutions in the following way:
Many companies today have the capability to apply the big data solution to the big data analytics platform. This big data analytics platform predicts client payment behaviours. Hence by gaining the insights to the behaviours of the client, customer can shorten the payment delay and generate more case while improving the customer satisfaction.
Data Integration solutions have the ability to scale up the business requirements change. Besides, it has access to day-to-day transactions. This transaction includes automation of the manual process, storage of staff working hours and so on..
Data volumes in baking are in the way of modernizing the core banking data and the application system through the uniform integration applications. Moreover, with big data analytics companies can apply application integration to process 2 TB of data daily. Moreover, it is also capable of implementing 1000 interfaces and uses one process for all information logistics and interfacing
Since the data is more and increasing day to day, analysis of this huge data is quite complex. And this data integration process is capable of automating the daily reporting, gaining the IT Department productivity. Moreover, it allows business users to analyze the critical insights
Hence likewise there is much application of Big data analytics in the Financial sector. And you people can get the practical knowledge of all these from live experts with real-word examples at Big data online training