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 customers. 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 the several applications of big data in different industries. Hence today I would like to elaborate more on Big data analytics applications in the Finance industry.
Hence prior to knowing about the big data analytics apps in the Finance industry, let us initially know,
What is Big Data Analytics?
Big data Analytics is the process of examing large and varies data sets. This Analytics reveals the information of hidden patterns, market trends, unknown correlations, customized preferences, and so on. Through this data, Analysts, data scientists, data modelers analyze the growing volumes of structured transaction data. All these analyses help marketers to make the best decisions for the best growth of the organization.
Know more on Big data Analytics from live expert at Big data Online Course
Since we people have got a brief idea regarding what is big data analytics, let us move how it impacts the Financial sector
Impact of Big data Analytics in Finance:
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 these rich data sets. Hence the financial institutions are struggling more in the process of data collection and processing for more than a decade. Moreover, due to the huge and increased usage of the customer requirements, financial institutions do not let this data 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 organizations 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 Analytics in Finance:
Big data in Finance refers to the petabytes of data. This data can be used to anticipate customer behavior. And to create 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 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, etc. The 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 various informed decisions. These decisions include improved customer services, fraud prevention, better customer targeting, risk exposure assessment, and so on.
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How Big Data analytics Revolutionized Finance?
Since the Financial system is a huge system organizations have undergone a lot of conversions that are required for behavioral as well as technological change. In the past few years, big data analytics in Finance has led the technological innovation. This enables convenient, personalized, and secure solutions in the industry. Hence Big data analytics has managed to transform not only the individual process but also the entire financial sector. And it has revolutionized the various areas of the finance industry in the following manner
a) Real-time stock market insights:
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.
b) Fraud Detection and prevention:
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 a valuable credit card is stolen, banks can instantly freeze the card and the transactions. Moreover, it also notifies the customer regarding customer threats.
c)Accurate risk analysis:
Today big financial institutions' decisions like decisions and loans rely on unbiased machine learning. Moreover, the calculated decisions based on predictive analytics taken into account for every aspect. This includes economy, customer segmentation, and business capital to identify potential risks like bad investment (or) payers.
Since we got a brief idea regarding how big data analytics radically changed the Finance sector, let us move further with
Application of Big data in finance:
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 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 financial institutions in the following way:
a) Increased revenue and customer satisfaction:
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 behaviors. Hence by gaining insights into the behaviors of the client, customers can shorten the payment delay. And generate more cases while improving customer satisfaction.
b)speeding up the manual process:
Data Integration solutions have the ability to scale up the business requirements change. Besides, this solution has access to day-to-day transactions. This transaction includes automation of the manual process, storage of staff working hours, and so on.
c) streamlined workflow and reliable system processing:
Data volumes in baking are in the way of modernizing the core banking data. And the application system through uniform integration apps. Moreover, with big data analytics companies can apply app 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
d) Analyze financial and performance growth:
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
Big Data Challenges in the finance sector
Generally, Big data is rapidly produced from various structured and unstructured data sources. Here, most traditional data systems are less capable of handling the data volume, velocity, and type of data. Also, management becomes dependent on building relevant and suitable processes, along with powerful techs, able to get information extracts.
Hence, to deal with these challenges, the latest technology is readily available but the companies need to understand big data management. However, there are some specific challenges of Big Data analytics related to finance that are relatively complex than other industry complexities.
Let us understand the different challenges held by this framework.
Meeting Regulatory requirements
The financial industry faces rigorous regulatory needs like the FRTB or basic review of the trading book. This controls the access to sensitive or critical data and demands faster reporting. While the latest Big data technology makes the financial institutions scale-up their risk management cost much effectively. Here, it helps with improved and innovative metrics and reporting that transforms data into analytics for delivering insights needed.
Data quality
Maintaining data quality is a very important task for the finance industry which is crucial. Many financial entities use their data that is stored to do something differently. Due to data is gathered from different sources, it sometimes doesn’t agree and causes barriers in decision making. Moreover, the data management solutions like Big Data ensure that the data is reliable secure, accurate, and can use it. The real-time analytics here with this technology provides access, accuracy, and speed to help entities drive quality insights. Further, this makes them capable of launching new products and many other service offerings.
Data security
Securing crucial and sensitive data and information is one of the major tasks that a business entity needs to do. With the rise in hacking and continuous threats, data access control becomes necessary for reducing the risk related to the financial industry. However, the Big data management tools make sure that the data is secure and protected from any outside intervention. It also helps in detecting any suspicious activity with immediate effect.
Silos in Data
Generally, the financial data comes from a variety of sources like emails, employee applications, documents, etc. Here, it needs data integration tools for joining and reconciling Big data to simplify data access and its storage.
Thus, Big Data and the cloud network work together to handle and resolve such data processing challenges, Due to the adoption of cloud solution platforms by financial institutions, it makes much difference. Also, it indicates that Big Data is not only useful in IT use cases but also in various business apps. This makes it a great technology useful for the financial industry.
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How to start with Big Data in Finance?
Today many big financial firms have adopted this technology for their operations and still going on. They have proven that the data insights collected using this tech solution are real and accurate. To drive business in a smooth way, some core factors involve such as customer experience, optimized ops, or business improvement. To get the full data-driven change, one should combine the Big data solutions with cloud-based solutions. The start with this, there are a few important steps involved-:
Define a strategy
Any industry whether it’s financial or any other should define a stable strategy with a business goal. This comprehensive plan will spread across the enterprise departments along with network partners. Here, the business entities must verify that the data is growing or not instead of temporary fixations.
Choose the right platform
There are different needs for different business entities. By choosing a cloud data solution can help the organization to gather much data with scalability and flexibility. Also, it helps in real-time processing and accessing such data. Further, the financial sector needs to adopt a highly secured and strong platform that secures data. Making data available to the key players by data tracking and giving valuable information can tackle data strategy well.
Begin with a suitable challenge
However, Big Data has so many capabilities so that it can deal with a good amount of challenges. So, the financial institution adopting it should choose one business challenge at a time. Making expansion from one solution to another makes the application much realistic. Also, it meets the basic resolutions with the expansion over time.
Conclusion
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-world examples at Big data online training. This learning will help you upgrade your existing skills with industry experience for a bright future.