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.
The Fourth Industrial Revolution is upon us, even with the Third is still in progress. Big Data, Machine Learning and Artificial Intelligence are three of the driving forces behind it. While the term'Industrial Revolution' has always applied mainly to manufacturing, it now also involves service industries such as banking and insurance, who are investing heavily in Big Data to help them model credit risk, fraud, marketing success and other key data. Meanwhile manufacturing, retail, telco, pharma and many other sectors constantly need people skilled in building, analysing, monitoring and maintaining data models to gain strategic intelligence that helps them inform and adapt their key business processes. A leader in the world of Data Analytics is the SAS Institute, whose flagship product is SAS (Statistical Analysis System).
On November 15th, my credit risk analytics course will be available as e-Learning. Send me an email at Bart.Baesens@gmail.com Bart Baesens holds a master's degree in Business Engineering (option: Management Informatics) and a PhD in Applied Economic Sciences from KU Leuven University (Belgium). He is currently an associate professor at KU Leuven, and a guest lecturer at the University of Southampton (United Kingdom). He has done extensive research on data mining and its applications.
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.