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New E-learning course: Profit-driven Business Analytics

@machinelearnbot

The e-learning course on profit-driven business analytics presents a toolbox of advanced analytical approaches that support subsequent cost-optimal decision making. They are advanced in that they are tailored for use in a business setting, where it is crucial to account for the costs and benefits that are related to decision making based on the output of analytical models. We call such approaches profit-driven analytics and they extend and reinforce the abilities of traditional analytics.The profit-driven perspective towards analytics that is advanced in this course contrasts with a traditional statistical perspective, which ignores the costs and benefits related to decision making based on analytical models. In the course, we discuss both profit-driven descriptive and predictive analytics, and as well introduce uplift modeling as a stepping stone toward developing prescriptive analytical models. We also discuss a range of profit-driven evaluation measures for assessing the performance of analytical models from a business perspective.


E-learning courses on Advanced Analytics, Credit Risk Modeling, and Fraud Analytics

@machinelearnbot

The E-learning course starts by refreshing the basic concepts of the analytics process model: data preprocessing, analytics and post processing. We then discuss decision trees and ensemble methods (bagging, boosting, random forests), neural networks, support vector machines (SVMs), Bayesian networks, survival analysis, social networks, monitoring and backtesting analytical models. Throughout the course, we extensively refer to our industry and research experience. The E-learning course consists of more than 20 hours of movies, each 5 minutes on average. Quizzes are included to facilitate the understanding of the material.


An Economic Perspective on Fraud Analytics: Calculating ROI of Fraud Detection Systems

@machinelearnbot

Even though these numbers are rough estimates rather than exact measurements, they are based on evidence and do indicate the importance and impact of the phenomenon, and therefore as well the need for organizations and governments to actively fight and prevent fraud with all means they have at their disposal. These numbers indicate that it is likely worthwhile to invest in fraud detection and prevention systems, since a significant financial return on investment can be made. However, estimating the return on investment in analytical approaches to fighting fraud is not self-evident, requiring an assessment of the total cost of ownership of analytical models as well as the full impact of fraud on the organization and the total utility of fraud detection and investigation. The Total Cost of Ownership (TCO) of a fraud analytical model refers to the cost of owning and operating the analytical model over its expected lifetime, from inception to retirement. It should consider both quantitative and qualitative costs and is a key input to make strategic decisions about how to optimally invest in fraud analytics.


Is Your Company Ready for HR Analytics?

#artificialintelligence

Although many companies have been investing heavily in big data and analytics, there haven't yet been many success stories in applying analytics to human resources. But that may be about to change. Big data and analytics are omnipresent in today's business environment. What's more, new technologies such as the internet of things, the ever-expanding online social graph, and the emergence of open, public data only increase the need for deep analytical knowledge and skills. Many companies have already invested in big data and analytics to gain a better understanding of customer behavior.


Interview with Prof. Dr. Bart Baesens - Author of Multiple Business Analytics Books

@machinelearnbot

Professor Bart Baesens is a professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on analytics, customer relationship management, web analytics, fraud detection, and credit risk management. His findings have been published in well-known international journals (e.g. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research, …) and presented at international top conferences. He is also author of the books Credit Risk Management: Basic Concepts, published by Oxford University Press in 2008; and Analytics in a Big Data World published by Wiley in 2014.