Big Data in Financial Services
Big Data is undoubtedly one of the latest innovations making a significant impact on technology and business. Large-scale Data Analytics found potential applications in many areas, including healthcare, pharmaceuticals, finance, supply chain, etc. Although Big Data is very relevant in many subject areas, it has a significant impact on the business field of finance.
Business firms need to hire professionals with finance backgrounds, especially accountancy, to make innovations in this area. The professionals also should possess skills in computers and information technology (IT) to cater to modern business dynamics. Those finance professionals and accountants who have the relevant domain knowledge and relevant IT skills can take full advantage of that, integrating Big Data into decision-making processes.
Billions of financial transactions occur every day globally. The finance field cannot ignore the relevance of big data. Thus, data streams and analytics are essential tools for the financial services sector. The new data-driven services can leverage increased revenues with reduced cost and improved efficiencies. It can also help improve security and customer service. Globally, Big Data Analytics is big business. It is currently predicted that its market will exceed two hundred billion US dollars, making it one of the biggest drivers for the growth of the banking sector.
Modern Big Data empowered FinTech firms are real challenges for the traditional banking industry. It can offer customers fast and low-cost financial services, make and receive online payments, and offer more advanced benefits such as a peer-to-peer lending facility. There is specific evidence that the traditional banking sectors are catching up rapidly, adding Big Data analytics in their business.
Why does the Finance Industry need Big Data?
Large-scale Data Analytics can analyse the financial industry's petabytes of structured and unstructured data to anticipate customer behaviours and create strategies for banks and financial institutions. The financial data sets are too large for conventional database applications to handle in real-time.
In short, by collecting and analysing vast quantities of data, financial organisations can anticipate the behaviour of their customers and develop strategies to provide better, safer, and more relevant, and better services. The value of the data lies almost entirely in the way it is gathered, analysed, and interpreted.
Opportunities of Big Data in Finance
High-volume data plays a pivotal role in strengthening various aspects of the business. People’s lives are improved by smoothening the business process by utilising large-scale Data Analytics. Integrating the Internet-of-Things or IoT with the business processes is the most modern direction of data streams for real-world use cases. Sensors linked to objects (things) generating meaningful data and information helping in decision-making are known as Internet-of-Things or IoT.
Decentralisation of the decision-making process is a salient opportunity available in vast datasets studies. The systems are competent, efficient decision-making based on the information available to them and set standards, all with the help of Big Data. The minor details of the financial data are not needed to be checked by the accountants.
Predictive analytics and data visualisation are other major utilities of Big Data in the field of finance. The decision-making process is achieved from objective facts and figures when finance practices are dependent on predictive analysis of the financial data. The data analysis processes are simplified in these techniques. The decision-making process has been simplified for accountants and financial analysts by using various data visualisation approaches.
Financial analyses need a lot of refinement of data into information before it is used, so Big Data techniques are adequate for the purpose. Since vast data is to be managed by the accounts in every facet, the big data analytic solutions are primarily based on data-driven accounts audits. Extensive datasets can control and eliminate the involved risk.
The top 5 applications of Big Data in the finance industry are:
- Customer Analytics – employed by roughly 55% of the sector: Customer-centred analytics has become a priority, a radical departure from the past when the financial sector was mainly product-centred. The main focus of data insights, systems, and operations is now directed towards the customer base. Knowing the changing markets and customer preferences allows banks and other financial-services companies to quickly develop new customer-centred products and services, seize new market opportunities, and retain customer loyalty.
- Risk and Financial Management – adopted by about 23% of the sector: A large segment of the finance sector utilises big data to enhance risk and financial management by optimising return on equity, fighting fraud, reducing operational risk, and meeting regulatory and compliance requirements.
- Development of New Business Models – used by almost 15% of the sector: Fraud prevention is a critical security responsibility. Fraudsters employ experts and massive resources to develop new tools/ways to commit cybercrime. Big Data Analytics can incorporate artificial intelligence and machine learning as software weapons in combating these cyber threats, identifying cyberattacks and fraudulent financial transactions.
- Operational Optimisation –** leveraged by about 4% of the sector**: The operational cost of the financial sector depends on the large unsupervised data needed to be collected and processed on a regular basis. Massive data can serve for overall operational optimisation.
- Employee Collaboration – engaged by approximately 2% of the sector: The employees form a large part of the stakeholders of the financial sector. High-volume data can improve the participation of the employees by enriching employee collaboration.
Some examples of the use of Big Data in the financial sector:
- Offering customers credit cards with customised interest rates and incentives which is based on their spending history.
- Suggesting consumer products to the customers that offer value and that match their spending patterns.
- Analysis of customers ATM usage and interactions with call centres to increase customer engagement.
- Use of speech analytics to determine main reasons for repeat calls to call centres from customers.
- Predicting when a customer might close their account and taking steps to dissuade them from doing so.
Challenges of Big Data in Finance
In the past few years, a large amount of data is being created at a swift pace. The data that has been generated/synthesised over the past few decades has not been generated/synthesised for thousands of years of evolution of human civilisation.
Data utilisation is necessary and essential to make critical decisions. Out of all of the data created and collected, some data are analysed and utilised. This data can be passed to the company’s tables and increase its revenues, but there is a set of obstacles correlated with the utility of big data in finance.
Investments in data analysis can result from financial companies' struggle and fight for getting positive returns. There could be a couple of grounds that became the basis for negative returns, as mentioned below.
- Accountants often face some difficulties while inspecting and analysing the correct finance data. Since garbage data can produce garbage outcomes only, thus collecting, observing, and processing debris data has zero utility. Data analytics have evolved from descriptive to diagnostic, predictive, and now gradually to perspective analysis, which has usages in identifying ideal solutions. These analytics produce superior outcomes for data processed and collected from a reliable source. The financial analyst must make judgments on the data types being used. Understanding the correct type of data may overcome this problem.
- A faulty data strategy may be one of the major issues. If the company expects to have high returns from investments in big data, a sound and rational data strategy and big data implementation plan are needed. The data strategies are required to be analysed regularly. A clear and comprehensive data identification is necessary to minimise the wastage of resources.
- A company's journey from data analytics to the optimisation and improvement of business operations is long. The approach of the pilot project is one way to improvise, gain insight, and resultantly create quick wins. Taking one smart and small step makes the overall position of a company solid and stable. It is a great challenge to determine the direction of movement that the organisation follows in terms of data analysis implementation.
- The combination and integration of data are obstacles for financial companies. The creation of informed analysis for people in the field of finance has not been achieved. Without considering the format and location, the integration of various data sets needs to be made clear. Data analytics is a merger of both IT and business operations. Accountants do not usually understand that, which is a threat to data analytics. Usually, this problem takes place when there is an insufficient understanding of knowledge between finance and IT. Some serious issues arise because there is a dissimilarity between the languages of IT and finance, which results in problems in the interpretation of the analysis. This often occurs because the departments are decentralised. A centralised system where the finance teams work with the departments of operations, the supply chain, and sales and marketing is required for better cooperation and a combination of data analytics.
Data Protection Challenges of Big Data
There are still some regulatory challenges that have to be tackled to enable the technologies proficient in order to provide competitive and effective solutions.
Data Privacy Protection
Since May 2018, data privacy protection has become a prime requirement in every business about and from the EU countries. Data privacy protection matters have become mandatory, and every company must comply with the General Data Protection Regulation (GDPR). Data privacy issues have not yet been fully implemented in every big data solution. Financial businesses, such as banking and insurance, collect, store, and use a large amount of personal data. Regulatory requirements of GDPR dictate that personal data must be collected, stored, processed for specified and lawful purposes only. The impact of GDPR for the banking and insurance sectors is significant because individual clients have the right to ask financial services organisations to remove or refrain from using/processing their personal data in particular situations. The financial services sector may incur an additional cost since they are supposed to deal with the individual request of the clients. The removal of some data may lead to the large dataset being grossly modified/shuffled.
Requirements of Confidentiality
All third-party information that is processed in Big Data Analytics is supposed to be confidential in nature. Thus, the financial services sector, including the banks and insurance companies, will have to comply with a mandatory requirement of maintaining data confidentiality so that no data breach occurs.
It is fascinating to notice financial companies have initiated considering big data analysis as a developmental project. Some more prominent companies have already begun to adopt big data analytic techniques. However, the literature review shows that big data is not used in finance, mainly for smaller companies.
Research studies showed that the Big Data method is only advantageous when used with the right set of minds. How the company will enhance its performance is primarily dependent on how the company and its analysts use this critical opportunity. With the usage of automated methods and the right reforms being taken at the right time, along with the integration of big data analysis, the company's revenue can be increased significantly.
Big Data has revolutionised the finance sector in various applications, including real-time stock market analysis, fraud prevention, and machine learning-powered risk analyses. Services can improve customer satisfaction, increase revenues, analyse financial performance, and improve, control, and develop products. Despite several advantages, there remain severe concerns about big data in the field of finance.
The use of Big Data and techniques to analyse it create substantial competitive advantages for the companies that can do that. There is a set of major challenges in the world of finance that are generated by data streams. All financial products concerning services are dependent on data and, in turn, also produce data. There are still many areas that need particular research developments in big data.
Ongoing data regulatory compliance is the top priority of the financial sector. Large-scale Data Analytics in finance must be customisable to incorporate the requirements of data privacy and confidential protection criteria of GDPR in EU countries.
Further research studies in financial data management systems must be undertaken to determine how the large data sets can be collected/handled and analysed to devise more efficient financial data solutions.
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