INTRODUCTIONNowadays, company CEO’s andother heads need to take fast decision making in a right way, without anyerrors. Also they need to be careful in monetary terms, since a single minorfault in financial aspect and fraud in money aspect would spoil the name and futurefor not only the company, but also for the stakeholders of the entire company(as in the case of satyam scandal 10 years before). Hence, analytics whenimplemented in finance benefits a lot and makes them time saving. Some of thearticles are discussed below where analytics are used in various places likeauditing, bankruptcy and others. LITERATURE REVIEWCao et al (2015), cameup with a study how big data analytics is used in audits, its characteristicsand issues in implementing analytics for financial needs like auditing.
Theyhave mentioned that analytics can be used in auditing fields to manage therisks of bankruptcy, to prevent the risks of material misstatement. Analyticswere used in predicting the averages in Dow Jones Industrial Average (DJIA),where the shifts were predicted three days ahead. Walmart used customersdemographics and managed inventories like selling a day breakfast duringunfavourable climatic conditions to their customers, which increased the salesto seven fold times. Ayata’s Prescriptive Analytics uses analytics to drill oilby combining all sources of data like images, texts, structured andunstructured, retrieved information like images from well logs, drillingoperations sound, videos of fluid flows from hydraulic fractures, text, and quantifiabledata (numbers) from production reports and are progressing well and good. Thepaper also discussed the biggest challenges of financial analytics, wherefinancial analytics may generate false positives and hence it should identifyits anomalies correctly and implement them.
Fitzet.al.,(2015), pointed out few ideas which can help financial managers to addvalue and ways to implement them. Obtain top management sponsorship to get astrong support, the need of information should be understood and should knowwhat questions need to be answered, full range of available advanced analyticcapabilities should be understood, comprehensive gap analysis should beperformed to understand where our data reside, barriers and other privacy lawsshould be discussed, to define your transformation strategy and to buy-in thestakeholder where the author highlighted to start small and further enhance bybuilding upon initial success, keep building capacity by trial and error andnot to do the entire work in one day, keep checking if the data that gotconverted into information adds value to the company, Constantly revaluation ofthe performed works should be done, and to accept all these modifications aschange management.
Drew et.al.,(2016) suggested that the local government faced the problems of financialsustainability where reformation of financial data was in need.
Hence factoranalysis were applied to financial ratio’s, Financial flexibilitylike operating ratio and Own Source Operating Revenue ratio, Cash expense,Unrestricted Current, Debt Service Cover and Interest Cover ratios and AssetRenewal and Capital Works like Infrastructure Backlog, Asset Maintenance, AssetRenewal and Capital Expenditure ratios, were summarised as a single financial sustainabilityassessment ranging through ‘very weak’, ‘weak’, ‘moderate’, ‘sound’, ‘strong’to ‘very strong’. All these ratio’s were done with regression analysis andfound the correlation between the ratio’s with the latent factors like population size, numberof employing businesses and population density would be statisticallysignificant regressors for econometric models employing the dominant factorloadings as regressands. It was found that three major latent factors driving the observedfinancial sustainability ratios whichprompted the reform process in the first place. Another important finding wasthat the three factors – scale anddensity, legacy and management competency acts independently. Deborah magliozzi(2017) done an empirical research study that analyses the economic andfinancial aspects of national telecom operators in Europe through theconstruction of appropriate financial strategic maps profitability map permarket share, Financial autonomy map, Capital expenditure cover map, Currentmap, 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 wereformulated where the standard regression equation were done to identify theuniformity and standard deviation of all the operators in each and everyfinancial aspects like current assets, liabilities, etc.,