Use of Predictive Analytic in Financial Services Assignment 2

Header: USE OF PREDICTIVE ANALYTIC IN FINANCIAL SERVICES 1
Use of Predictive Analytic in Financial Services
Assignment 2
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Header: USE OF PREDICTIVE ANALYTIC IN FINANCIAL SERVICES 2
Abstract—This essay is a superb illustration of
the practical application of random movement
model and how it affects the stock price behavior.
There has been a burning desire to test the random
walk model since its performance is more
contextual. Predictive analytics, therefore, enables
the traders and investors who wish to venture in the
financial stock by predicting the stock prices after
gaining the information about the immediate past
and immediate future information relating to the
financial services, thanks to predictive analytics in
financial services.
Keywords-
Introduction
redictive analytics is an art of obtaining
information on various aspects and using such
data to predict the possible outcome of the event
[5]. Predictive analytics in financial services,
therefore, helps the players in the financial markets
to forecast the potential changes in stock prices in
the future owing to the past and present trends in the
financial markets.
P
Predictive analytics has proved to be at the
core of the works by the financial services among
the retail banking operations for decades now. The
capacity to effectively leverage the insights of a
personalized consumer testimony and profitability
has remained elusive despite the proliferation of
large volumes of data. Even though there are
impeccable rising technologies within the cloud-
based architectures and advancement in analytic
tools, banking industries still have a lot of steps to
put in place. They do so to ensure that the
increasing discerning customer base expectations
are attended to, to increase the company’s
profitability. In other words, the financial industry
should cease from the old tradition of using data to
build a tremendous internal report of the past
operations. This will ensure the use of data to build
a stronger customer relationship that sees to the best
ways of future customer satisfaction.
Beyond reasonable doubt, it is clear that
financial services must channel their operations
away from the reliable approach that has been the
old banking technique. In predictive analytics,
financial services will only device the models that
exclusively focus on customer needs. Application of
advanced predictive analytics in financial services
has reinvigorated between the customers and the
commercial companies. However, by increasing
customer satisfaction, boosting the company’s
profitability and building strong trust even when
there is competition resulting from the new and
upcoming technologies in financial services.
Predictive analytics in financial services, therefore,
eliminates the skeptical behaviors among the
customers even when a new customer-centric digital
device is launched in the banking markets [3].
This research paper brings forth a
comprehensive literature review concerning the
application of predictive analytics in financial
services as published in the most recent journals and
academic research articles. It also discusses the
methodology used in gathering information,
conclusion, limitations and implications of the
research. Gaps for a future study relating to the
research topic is also mentioned in the article.
Figure 1: Drivers of predictive Analysis in finance