Big Data in Finance
Significant financial institutions and banks are applying innovative Big Data technology to derive actionable information from a massive amount of consumer data and standardize financial data from different sources. Big Data has gradually become necessary for these institutions. The goal is to increase efficiency, provide quality options, and become more customer-focused. Simultaneously, reducing the tangent of bribery and risks in the financial realm.
Fraud Management
A colossal amount of financial data is generated from the internet every day. Finance professionals can leverage Big Data’s resources to help organizations anticipate or preempt risks—and protect performance. For instance, social media may effectively signal changes in market opinion and significant social and political threats. Accountants and financial experts can help their companies properly detect and manage risks by using various data sets in their estimates.
Customer Analytics
Banks produce goods that are expected to do well in the future based on how well a product does, doesn’t sell, or to whom it sells. They also use external data to produce offerings that are beneficial to their clients and profitable for the bank, such as retail activity during a crisis or what mortgage products sell well while the housing market is stagnant.
Financial services organizations are taking a market-driven approach to big data in terms of data strategy: first, business needs are defined, and then-current internal resources and capabilities are aligned to meet the business demand before investing in data sources and infrastructures. However, not all financial institutions are following suit.
Big data analytics in financial models
It is possible to link historical results to future strategies and hypotheses on crucial management decisions by considering the value chains of market theory from the entry and existence of financial knowledge in a model. This would enable key business decision-makers to know all vital financial issues that could be affected by their decision.
Similarly, using a financial model, mortgage pricing’s impacts and elasticity can be robustly evaluated in terms of the net interest margin, cash, and risk returns on the balance sheet. Mortgage securitization and stress-checking mortgage defaults are also options.
Real-time analytics
The term “algorithm investing” is currently trending in the financial industry. After all, artificial learning has advanced to the point that machines can now make choices that are far superior to those made by humans. Machine learning, on the other hand, can complete trades even quicker and at frequencies that humans will never reach. The company archetype can incorporate the correct rates and decrease the number of mistakes that could be caused by intrinsic behavioral forces that usually affect humans.
Sources
IBM Institute for Business Value. (2013). Analytics: The real-world use of big data in financial services. IBM. https://www.ibm.com/downloads/cas/E4BWZ1PY
Using new models and big data to better understand financial risk. (2016, April 11). MIT News | Massachusetts Institute of Technology. https://news.mit.edu/2016/using-new-models-big-data-to-better-understand-financial-risk-0411