Data is increasing at a tremendous rate in today’s world and the immersion of Big data has gained significant momentum in driving innovative business models to create new customer services. In recent years Big Data has also made an impact with regard to contextualising transaction data in real-time across multiple finance institutions.
The three keys of Big Data – Volume, Velocity and Variety (3 V’s of Big Data) is what has helped overcome the challenges of the traditional approach of data handling, analysis and data extraction. Veracity is an additional dimension of big data to consider: Establishing and maintaining trust in data presents a challenge as its sources and varieties grow.
A major challenge for the finance sector is being able to process increasingly large volumes of data in a timely manner. Big data now helps them to gain insights into their operations, customers, and market opportunities. It is known that Big data is changing the world of finance sector and affecting how they operate. Finance is the area where big data is making its big mark, as emerging technologies such as Hadoop, Storm and NoSQL allow investors to analyse large volumes of data.
Though financial institutes are finding it a bit challenging to enable data-oriented business capabilities with Big Data, they are finding ways to integrate their processes and get the advantages of Big Data. Here are some of the many ways, Big Data is applied in the financial sector: -
Successfully harnessing big data can help banks achieve three critical objectives for banking transformation: Create a customer-focused enterprise; Optimize enterprise risk management; and Increase flexibility and streamline operations. Big Data helps to overcome many banking problems by optimizing the data, MIS (management Information System)/Regulatory Reporting, fraud detection and investigation and best for counterparty credit risk management.
Opportunities for Big Data include enabling post trade analytics to help asset managers to evaluate key metrics like transaction costs, order execution performance and portfolio returns measurement in real time. When we talk about asset management in finance, we are covering the business emotional factor to make the economy better in institutes like trade sentiment analysis, investment product quality management and risk management.
80% of our data is unstructured and not stored in the manageable and friendly confines of a database. Despite the volume of data and content available today, decision makers are often starved for true insight. Financial institutes helps insurance companies harness big data to drive business results. Insurance leaders are focusing on major imperatives to drive competitive advantage and differentiation in current market conditions, like customer-focused enterprise creation, optimizing risk management and multi-channel interaction and by increasing flexibility and streamlining operations.
Insights from market indicators, economic indicators, and sentiment analysis for stocks and events may be used to enrich the information set used by Investment and portfolio managers for investment and asset balancing decisions. Big Data capabilities also enable the enterprise to develop comprehensive check-points for KYC initiatives, fraud detection, investigation and prevention. We expect that the overall relevance of Big Data will continue to increase manifold and will play a significant role in defining the overall performance of firms in capital markets over the years.
To compete in current economy system, it is increasingly clear that financial firms must leverage their information assets to gain a comprehensive understanding of markets, customers, channels, products, regulations, competitors, suppliers, employees and more. Financial institutions will realize value by effectively managing and analysing the rapidly increasing volume, velocity and variety (3 V’s) of new and existing data, and definitely putting the right skills and tools in place to better understand their financial operations, the marketplace and the customers as a whole.