INTRODUCTION company, and the stakeholders of the whole company.


Nowadays, company’s CEO’s and
other heads need decide fast in a right way, without any errors. Also they need
to be careful in monetary terms, since a single minor fault in financial aspect
and some frauds in monetary aspect which would spoil the name and future of the
company, and the stakeholders of the whole company. Hence, analytics when
implemented in finance, benefits a lot and makes them time saving. Some of the
articles are discussed below where analytics are used in various places like
auditing, bankruptcy and others.

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Cao et al (2015), came
up with how big data analytics is useful in audits, its characteristics and
issues in implementing analytics for fiscal needs like auditing. They have
mentioned that analytics can be used in auditing fields to manage the risks of
bankruptcy, in order to prevent the risks of material incorrect statement. Analytics
is used in predicting the averages in Dow Jones Industrial Average, in which
the shifts were predicted three days ahead. Walmart used customers demographics
and managed inventories like selling breakfast at unfavourable climatic
conditions to their customers, which increased the sales to seven fold times.
Ayata’s Prescriptive Analytics uses analytics to drill oil by combining all
sources of data like images, texts, structured and unstructured, retrieved
information like image from well logs, drilling operations sound, text, videos
of fluid flows from hydraulics and quantifiable data (numbers) from production
reports and are progressing well and good. The paper also discussed the biggest
challenges of financial analytics, where financial analytics may generate false
positives and hence it should identify its anomalies correctly and implement


Fitz,(2015), pointed out few ideas which can help financial
managers to add value and ways to implement. It requires the approval of top
management sponsorship to get a strong support, the need of information should
be understood and need to know what questions need to be answered, full available
range of advanced analytic capabilities should be understood, comprehensive gap
analysis should be performed to understand where our data reside, barriers and
other privacy laws should be discussed, to define your transformation strategy
and to buy-in the stakeholder where the author highlighted to start small and enhance
on building upon baby-step success, keep building capacity by trial and error
and not to do the entire work in one day, keep checking if the data that got
converted into information adds value to the company, Constantly revaluation of
the performed works should be done, and accepting all these modifications as
change management.


Drew, (2016) suggested that the local government faced the
problems of financial sustainability where reformation of financial data was in
need. Hence factor analysis were applied to financial ratio’s, Financial flexibility
like operating ratio and Own Source Operating Revenue ratio, Cash expense,
Unrestricted Current, Debt Service Cover and Interest Cover ratios and Asset
Renewal and Capital Works like Infrastructure Backlog, Asset Maintenance, Asset
Renewal and Capital Expenditure ratios, were summarised as a single financial
sustainability assessment ranging through ‘very weak’, ‘weak’, ‘moderate’,
‘sound’, ‘strong’ to ‘very strong’. All these ratio’s were done with regression
analysis and found the correlation between the ratio’s with the latent factors
like population size, number of employing businesses and population density
would be statistically significant regressors for econometric models employing
the dominant factor loadings as regressands. It was found that three  major latent factors driving the observed
financial  sustainability ratios which
prompted the reform process in the first place. Another important finding was
that the three factors – scale and
density, legacy and management competency acts independently.


Deborah magliozzi (2017) done an empirical research study that analyses
the economic and financial aspects of national telecom operators in Europe by
constructing appropriate profitability map per market share, Financial autonomy map,
Capital expenditure cover map, Current map, Liquidity mar for Telecom Italia (Italy), British Telecom
(U.K.), France Telecom (France), Deutsche Telekom (Germany), KPN (Holland),
Telefonica de Espana (Spain) and Portugal Telecom (Portugal). Financial
analysis of the operators were done using regression and maps were formulated
where the standard regression modelling was done to identify the uniformity and
standard deviation of all the operators in each and every financial aspects
like current liabilities, assets etc.,


Rafal, (2016), analysed changes
between a chosen world stock market and the constructed synthetic index. He
proved the dependence between the synthetic stock market index and other stock
markets is increasing when there is rapid decrease in value of stock market
indexes. Contagion in financial markets were verified through positive
verification. They proved the contagious nature of stock markets to the world
economy by proving that the decline of stock market in US is delayed by about
one-three months. They also indicated that contagious nature of can be well
measured by the taxonomic
index and a conditional concordance.


Beck, torre (2007)
pointed out that price and income level were the economic determinants of the
demand for payments and savings services. Economic development along with the
associated rise in per capita income increases the need for more and more
sophisticated versions of these services. However, demand is not only driven by
economic but also by socio-cultural factors. Further, we have to isolate pure
demand factors from demand reductions that are due to the expectation of supply
constraints. In the following, we will distinguish between two demand curves—a
potential demand curve, driven purely by economic factors, and an actual demand
curve, that might be below the potential one due to non-economic factors. We
can write the potential (individual or aggregate) demand as D? = f{income, price},
with demand increasing in the first argument and decreasing in the second. The
actual demand can be lower than potential demand for a given price and income
level, due to self-exclusion arising from such non-economic reasons as financial
illiteracy and ethnic or religious factors.


Kirca, et, al., (2011) used sampling method of four stage procedure for
his meta-analysis of 120 independent samples where, around in 111 studies, the
predictions of internalization theory was done from the context of the multi-nationality
performance relationship, which provided an efficient organizational enabling
firms to generate higher returns in international markets by transferring their
firm-specific assets. Independent variables: R&D intensity and advertising
intensity , dependent variable: multi-nationality. Organizational variables:
firm international experience, age, and size. From the meta-analytic evidence,
they came to know that multinationality had intrinsic values at the limit
beyond the firm’s intangible assets.


Flood (2009) discussed about meta-data, where financial innovation,

risk, and strategic
policy evolution creates a very unstable data integration environment for risk
management analytics. They also discussed about the costs involved in managing
meta-data, such as specification and mapping costs, did scaling of the costs in
a computer software, and finally They proposed design with two main features,
where the first one centralizes metadata in a numeraire specification, to
linearize the mapping costs and second one it introduces an ontological
structure and an ontology editor to expose the metadata to financial analysts
for on-the-fly editing.


Telli., et al., (2008)
studies the two sectoral adjustments (real and financial) of the Turkish
economy. Because, turkey was facing with failed reforms and deterioriated
performance of macro-economy for the past 10 years. Hence, under the
conditionalities of the ‘twin targets’ such as primary surplus to gross
national product (GNP) ratio and on inflation rate, the major three issues were
rolled out: the role of the expanded foreign capital inflows in resolving the
macroeconomic issue of the disinflation motives of the central bank and
imperatives of debt sustainability and fiscal credibility of the ministry of
finance, and reduction of the central bank’s interest rates, and reducing
payroll taxes in labor market reforms. Expansion
after 2011 was observed to be concomitant with a external disequilibrium and
fragility and the output growth contrasts with persistent unemployment.



Kim., et al., (2013)
came up with the social media analytics with the application in financial
sector. In the case study, the software used was BlitzMetrics, which is a
social media software, which captures information from social media sites for
storing them in database. The software 
includes a viewer , an analytic layer, a database layer, and a data collection
layer. Lots of information are there in the database, and the financial
statements of fortune 900 companies were found out. From then, analysis of
engagement in finance sector of social media and financial dashboards were
found out. Big data integration helps to overcome the challenges of managing
where higher rates of change and innovation in business model are usual.
Effective strategy would be able to recognize and include an investigation of
the requirements to ingest, index, and integrate structured and unstructured,
and static data from a variety of sources.


Donnell., (2015) came
up with a study to make the auditors and financial executives need to know
about financial analytics for improvement in revenue & accounts receivable, segregation of duties purchases and
accounts payable, supply chain, supply cycles. In revenue & accounts
receivable, it can Identify discrepancies in price and quantity between
invoices, sales orders, stock-outs and customer orders outpacing shipped
products. In segregation of duties, it highlights
areas of higher risk by identifying incompatible duty assignments and specific transactions.
In purchases and account payable, it would analyse purchases to identify
significant or unusual items. In supply chain it would Identifies risks
resulting from the concentration of suppliers in a particular region. It determines
vendor cycles to understand average time between the purchase order and goods
receipt to help determine the impact on inventory costing, potential valuation
concerns and the performance of the business that could signal the need for
impairment considerations. Hence analytics when implemented in financial
sectors and all the higher level financial authorities should know what all
benefits they reap in financial analytics.


Dubofsky (2009)., surveyed and came up with the changing roles of
financial planner. An
online survey was sent to 38,810 members of the Financial Planning Association
and CFP Board mailing list participant, to determine the coaching and life
planning activities of financial planners, 74 percent planners estimates that the
amount of time they are spending on these issues have increased over the last
five years. Huge number of respondents believe that the process of coaching
makes them better planners and helped the clients, but not sure that if these
activities would increase business wellness. The article concludes that the financial
planners are yet to listen to their clients sayings, and accurately infer what
clients want to say but are afraid to articulate, and be able to respond
appropriately and expand on the specific strategies for coaching skills
implicitly and explicitly required by the clients.


Kaplan (1983), eliminated the differences between the industrial firms
and financial institutions by model building approach where regression analysis
were used for reviewing the audits of the financial statements.


The relation ship between dependent and independent variable is not
stable from period to period. The research concluded that it was a difficult
task for building a model which could predict accurately the accounts change of
assets and liabilities.

For modelling of income and expense account, traditional regression
analysis is not possible, due to the fluctuations in the interest rates, which
is a major bond between the variable. Equations are easy to implement but the
interest rates should be easy to apply.


Dessislava et al.,
(2000) came up with the several industrial challenges and potential
oppurtunities for the portfolio-managers. Nowadays portfolio managers have
access to new data analytics resources, and techniques for their equity
portfolio’s. and possess some softwares with smart beta strategies, giving ways
for managers for the better ways of identifying investment oppurtunities. The
research also mentioned that building an analytical structure is a challenging
one, and could be done only by step by step approach.   We should be questioning about the quality
of the data that we have and the limitations and merits of the analytical
models that we have. Some finaicial market analytics trends were also discussed
where automatic execution of trades on electronic platforms, execution of risk
analytics, were possibly done by algorithmic trading.



 Preet,(2000) developed a framework for
visualising the export strategies of firms starting from blooming economies
till their performance in foreign markets. Hypotheses that were derived from
this study were tested on the firms from Brazil, Chile, and Mexico. The
cost-based strategies enhanced the export performance of developed country
markets and differentiation strategies enhance performance in other developing
countries. Marketing mix variables for the specific needs of developed country
markets also enhances export performance.