The implementation of data analytics in the governmental and not-for-profit sector
Creators: name: Arisandi, Desi
Publisher: name: No Publisher Supplied
Year: 2016

description: Applying data analytics techniques in the governmental and not-for-profit sector can transform facts and figures into strategic insights that deliver intelligence and support decision making. The objective of this research is to undertake data analytic techniques that will provide empirical evidence and improve transparency and accountability. The analytical methods used in this study include text mining, artificial neural networks, and the predictive modeling.    In order to improve the quality of state and local government financial reporting, the Governmental Accounting Standards Board (GASB) publishes standards and guidelines. One of the GASB’s 2014 research agendas was to collect broad user opinions about the governmental standards and their implementation. In support of this objective, one section of this study measures public sentiment by using textual analysis.    Text mining provides an alternative approach to the more conventional public data collection methods, such as surveys or questionnaires. This method measures user sentiment from public websites and ascertains opinions regarding specific GASB standards and exposure drafts. Such research can serve to improve the development of government financial standards and provide better insights regarding their implementation. Text mining capabilities can be used for analyses of internet based media such as online news or social media therefore the applicability of this method is quite extensive. This method is also able to evaluate sentiment from different types of documents such as financial reports or comment letters.    With the growth in the amount of municipal bond investments, the implementation of an analytical model that can provide a better understanding of financial performance evaluation is paramount. One source of investor information is obtained from credit rating agencies. These agencies provide their assessments of local governments’ creditworthiness through the issuance of credit ratings. In presenting their assessments in the form of credit ratings, these agencies have never clearly revealed either the variables or weights assigned on each of the variables on their models.    One of the chapters in this study explores the composition of credit ratings by incorporating budgetary, financial, and demographic information into an Artificial Neural Networks model. The study is expected to identify the impact that the different factors have on municipal credit ratings. The main contribution provided by this chapter is the development of a model that will explain to users the composition of the variables or factors that influence the municipal bond credit ratings.    The purpose of an entity audit is to provide an opinion as to whether management’s financial statements are prepared in accordance with applicable accounting standards. This objective is achieved by independent auditors undertaking appropriate audit procedures. However, audit findings and conclusions can be insufficient or inadequate due to limited audit information or management efforts to avoid a qualified audit opinion. One strategy management can use to avoid a qualified audit opinion is to switch auditors. This is known as opinion shopping. One chapter in this study provides empirical evidence of the association between switching auditors and the issuance of a qualified audit opinion for a Non-Federal Entity (NFE).    Overall this study will contribute in three areas to the government and not-for-profit accounting literature. Text mining analysis will be able to provide insights into public opinion on the implementation of standards. Next, predictive modeling will detect the irregularities of opinion shopping. Finally, the Artificial Neural Networks will provide an inferential analysis that can provide information to users of municipal bond credit ratings to improve future decision making.    For future studies additional data sources can be included, for example: incorporating non-financial variable to indicate the occurrence of opinion shopping, adding more states data to the neural networks prediction model, and including the GASB’s comment letters for text mining analysis.