Risk-based audits in a behavioural model.

The tools of predictive analytics are widely used in the analysis of large data sets to predict future patterns in the system. In particular, predictive analytics is used to estimate risk of engaging in certain behavior. Risk-based audits are used by revenue services to target potentially noncompliant taxpayers, but the results of predictive analytics serve predominantly only as a guide rather than a rule. “Auditor judgment” retains an important role in selecting audit targets. This article assesses the effectiveness of using predictive analytics in a model of the compliance decision that incorporates several components from behavioral economics: subjective beliefs about audit probabilities, a social custom reward from honest tax payment, and a degree of risk aversion that increases with age. Simulation analysis shows that predictive analytics are successful in raising compliance and that the resulting pattern of audits is very close to being a cutoff rule

Privacy Tradeoffs in Predictive Analytics

Online services routinely mine user data to predict user preferences, make recommendations, and place targeted ads. Recent research has demonstrated that several private user attributes (such as political affiliation, sexual orientation, and gender) can be inferred from such data. Can a privacy-conscious user benefit from personalization while simultaneously protecting her private attributes? We study this question in the context of a rating prediction service based on matrix factorization. We construct a protocol of interactions between the service and users that has remarkable optimality properties: it is privacy-preserving, in that no inference algorithm can succeed in inferring a user's private attribute with a probability better than random guessing; it has maximal accuracy, in that no other privacy-preserving protocol improves rating prediction; and, finally, it involves a minimal disclosure, as the prediction accuracy strictly decreases when the service reveals less information. We extensively evaluate our protocol using several rating datasets, demonstrating that it successfully blocks the inference of gender, age and political affiliation, while incurring less than 5% decrease in the accuracy of rating prediction.Comment: Extended version of the paper appearing in SIGMETRICS 201

Predictive Analytics In Higher Education: Five Guiding Practices for Ethical Use

Without ethical practices, student data could be used to curtail academic success rather than help ensure it. For example, without a clear plan in place, an institution could use predictive analytics to justify using fewer resources to recruit low-income students because their chances of enrolling are less sure than for more affluent prospective students. In this report, New America lays out important questions to consider as administrators formulate how to use predictive analytics ethically

Risk Assessment Using Predictive Analytics

Purpose: This research paper uses design science methodology to develop and evaluate a predictive analytics model for audit risk assessment. This research therefore contributes to improving the accuracy and efficiency of audit risk assessment through predictive analytics.   Theoretical framework: This study involved developing and evaluating a predictive analytics model for audit risk assessment, with it being tested during the audit of a publicly listed Saudi company.   Design/methodology/approach: This study adopted the design science research methodology, which is a problem-solving approach that involves the creation of innovative solutions to practical problems. This methodology is particularly relevant for developing and evaluating predictive analytics models for audit risk assessment, because it provides a structured, systematic approach to the problem-solving process. In the context of this research paper, the design science research methodology was used to develop and evaluate a predictive analytics model for audit risk assessment.   Findings: The proposed predictive analytics model for audit risk assessment was found to be an effective tool for helping auditors to make informed decisions based on data analysis. The model accurately identifies high-risk factors associated with an organization, provides valuable insights for decision-making, and highlights areas of potential risk that may require further investigation.   Research, practical & social implications: Future research could explore several areas related to predictive analytics in audit risk assessment. One important area to investigate would be the impact of using predictive analytics on audit quality. The ethical implications of using predictive analytics in audit risk assessment and the potential biases that could affect a model’s accuracy are also important areas to explore.   Originality/value: This paper helps improve our understanding of how predictive analytics can be effectively applied to audit risk assessment and how design science methodology can be used to develop and evaluate predictive analytics models. Furthermore, this study provides insights about the effectiveness of predictive analytics for improving audit risk assessment, thus contributing to the existing literature on the topic

Predictive Analytics in Information Systems Research

This research essay highlights the need to integrate predictive analytics into information systems research and shows several concrete ways in which this goal can be accomplished. Predictive analytics include empirical methods (statistical and other) that generate data predictions as well as methods for assessing predictive power. Predictive analytics not only assist in creating practically useful models, they also play an important role alongside explanatory modeling in theory building and theory testing. We describe six roles for predictive analytics: new theory generation, measurement development, comparison of competing theories, improvement of existing models, relevance assessment, and assessment of the predictability of empirical phenomena. Despite the importance of predictive analytics, we find that they are rare in the empirical IS literature. Extant IS literature relies nearly exclusively on explanatory statistical modeling, where statistical inference is used to test and evaluate the explanatory power of underlying causal models, and predictive power is assumed to follow automatically from the explanatory model. However, explanatory power does not imply predictive power and thus predictive analytics are necessary for assessing predictive power and for building empirical models that predict well. To show that predictive analytics and explanatory statistical modeling are fundamentally disparate, we show that they are different in each step of the modeling process. These differences translate into different final models, so that a pure explanatory statistical model is best tuned for testing causal hypotheses and a pure predictive model is best in terms of predictive power. We convert a well-known explanatory paper on TAM to a predictive context to illustrate these differences and show how predictive analytics can add theoretical and practical value to IS research

Predictive Analytics – Examining the Effects on Decision Making in Organizations

Predictive analytics is a type of business analytics which enables predictions to be made, about occurrence of particular events in the future, based on data of the past. The predictive analytics is widely incorporated among the most successful organizations where it supports their decision-making process. The aim of our study is to examine the effects on decision making in organizations caused by predictive analytics. We perform a qualitative study to investigate the effects by using Simon’s model to break down the decision-making process and analyse how the predictive analytics affects each stage. Additionally we test the propositions from Huber’s theory of the effects of advanced information technology on organizational design, intelligence and decision making, in the context of predictive analytics as an advanced information technology. Our contribution to IS knowledge is derived from our findings which show that the predictive analytics offers strong support in the intelligence and design phase of the decision-making process, while having no effect on the choice phase. Furthermore, through the prism of Huber’s theory, we find that the predictive analytics generates effects on the organizational intelligence and decision making, while also having effects at subunit level, organizational level and the organizational memory

Effective modelling for predictive analytics in data science

Predictive analytics includes many statistical and other empirical methods that create various data predictions as well as different methods for assessing predictive power. Predictive analytics not only helps in creating practically useful models but also plays an important role in building new theory for further study and research. Today, the use of available data to extract inferences and predictions by using predictive analytics has grown in the industry from being a small department in large companies to being an active component in most mid to large sized organizations. This paper addresses to reduce a particularly large gap of, the nearabsence of empirical or factual predictive analytics in the mainstream research going on in this field by analyzing the issues faced in predictive modelling by the empirical determination of data with its experimental facts for latency pattern.Keywords: Predictive Analytics, Big Data, Business Intelligence, Project Planning