Finance in Data Science. Intro, monetary benifits, use cases, examples, Future of technology in finance and conclusion.

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Title: Managing Information Technology
Contents
Introduction    3
Monetary Benefits    3
Cases    4
Future of Technology in Finance    5
Conclusion    5
Works Cited    6
Introduction
The world of finance has always been dominated by data analysis. It might be claimed that professional investors were already using data in their day-to-day operations prior to the development of data science, pattern recognition and artificial intelligence technologies. The introduction of the FICO rating in 1958 provides the most conclusive proof of this point. The world of finance always was heavily reliant on statistics.
It might be claimed that financial professionals were already using data in regular day-to-day operations prior to the development of data science, computer vision and machine intelligence technologies. There is an evolution of the 1958 FICO scientific assessment the most compelling evidence for this. The current study will evaluate the monetary benefits of finance in data science as well as with the case in United States.
Monetary Benefits
A Client Lifetime Value Study may also be performed using data from a customer database (CLV). Customer with high lifetime value (CLV) must be discovered and handled appropriately in order to maximize the value of their attributes in terms of both monetary and growth potential. Existing banking system organizations stand to gain significantly from the use of data science in their operations.
Hence, as an outcome of these advantages, businesses will be able to employ advanced analytics in greater numbers. They will create default loss estimates and forecasts at the client or investment level (Engin and Treleaven). Market circumstances are being studied and forecasted. Investment banks may create a range of estimation methods...