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Articles by Bart Baesens
Open data, private learners: a de-identified student activity and performance dataset for learning analytics - Scientific Data
Abstract The data-driven study and optimization of learning processes has become possible through collecting the digital traces left by learners as they interact with digital study materials and learning tools — a field known as Learning Analytics (LA). The increased availability of such data has fueled the growth of the LA field, enabling the development of new frameworks and models, and supporting the transfer of findings across domains, and collaboration on joint educational initiatives.
LM anthropomorphization: balancing ethics and business value
Advanced search Journal of Business Analytics Latest Articles Submit an article Journal homepage Full Article Figures & data References Citations Metrics Reprints & Permissions Read this article /doi/full/10.1080/2573234X.2025.2551951?needAccess=true ABSTRACT Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse applications, yet their success and human-like interactions have fueled increasing concerns about anthropomorphization.
Empowering Students to Succeed in the AI Era
Bart Baesens is a professor of Data Science and AI at KU Leuven and a lecturer at the University of Southampton. He co-authored more than 250 scientific papers and 10 books. Bart received the OR Society’s Goodeve medal for best JORS paper in 2016 and the EURO 2014 and EURO 2017 award. Bart is listed in the top 2% of Stanford University’s new Database of Top Scientists in the World. He was also named one of the World’s top educators in Data Science by CDO magazine in 2021, 2023 and 2024.
Sovereign credit risk modeling using machine learning: a novel approach to sovereign credit risk incorporating private sector and sustainability risks - Journal of Credit Risk
The use of machine learning algorithms, specifically XGBoost in this paper, and the subsequent application of model interpretability techniques of SHAP and LIME significantly improved the predictive and explanatory power of the credit risk models developed in the paper. Sovereign credit risk is a function of not just the macroeconomic environment of a country, but also the state of its private sector and its exposure to sustainability risk factors.
Sovereign credit risk modeling using machine learning: a novel approach to sovereign credit risk incorporating private sector and sustainability risks - Journal of Credit Risk
Sovereign debt has traditionally been regarded a safe asset class. However, the picture has shifted in the last decade, with a significant increase in the number of sovereign defaults. The drivers of this increase are complex and as yet unexplored, but it is clear that factors other than traditional macroeconomic events are significantly underpinning these economies.
Free SAS E-Books
Computer Vision with SAS®: Special Collection Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. In recent years, computer vision has begun to rival and even surpass human visual abilities in many areas. SAS offers many different solutions to train computers to “see” by identifying and classifying objects, and several groundbreaking papers have been written to demonstrate these techniques.
Data Warehouses, Data Marts, Operational Data Stores, and Data Lakes: What's in a Name?
Many organizations nowadays are struggling with finding the appropriate data stores for their data. Let’s zoom in on some key data structures to facilitate corporate decision making by means of business intelligence. More specifically, let’s look at data warehouses, data marts, operational data stores, data lakes, and their differences and similarities.
Building the ideal data quality team starts with these roles
Data management entails the proper management of data as well as the corresponding data definitions or metadata. It aims at ensuring that (meta-) data is of good quality and thus a key resource for effective and efficient managerial decision making. Data quality (DQ) is often defined as ‘fitness for use,’ which implies the relative nature of the concept.
OLAP queries in SQL: A Refresher
By Wilfried Lemahieu, Seppe vanden Broucke, Bart Baesens The below article is based on our recent book: Principles of Database Management - The Practical Guide to Storing, Managing and Analyzing Big and Small Data (See www.pdbmbook.com). In this article, we zoom in on how OLAP queries can be implemented in SQL. To facilitate the execution of OLAP queries and data aggregation, SQL-99 introduced three extensions to the GROUP BY statement: the CUBE, ROLLUP and GROUPING SETS operator.
SQL, NoSQL or NewSQL: How organizations can make the right pick
Relational database systems pay a lot of attention to data consistency and compliance with a formal database schema. New data or modifications to existing data are not accepted unless they satisfy constraints represented in this schema in terms of data types, referential integrity etc. The way in which RDBMSs coordinate their transactions guarantees that the entire database is consistent at all times, the well-known ACID properties: atomicity, consistency, isolation and durability.
How database management practices impact business continuity
(Editor's note: This column is based upon the forthcoming book "Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data," www.pdbmbook.com. See also the corresponding YouTube channel with free video lectures : https://www.youtube.com/watch?v=o36Z_OqC2ac&list=PLdQddgMBv5zHcEN9RrhADq3CBColhY2hl ).
A Database Perspective on Data Security
In this column, we would like to elaborate on the concept of data security. Although security is often related to privacy, they are not synonyms. Data security can be defined as the set of policies and techniques to ensure the confidentiality, availability and integrity of data at all times. On the other hand, data privacy refers to the fact that the parties accessing and using the data do so only in ways that comply with the agreed upon purposes of data use in their role.
To SQL or not To SQL: that’s the question!
To SQL or not To SQL: that’s the question! This article is based upon our upcoming book Principles of Database Management: The Practical Guide to Storing, Managing and Analyzing Big and Small Data, www.pdbmbook.com See also our corresponding YouTube channel with free video lectures : https://www.youtube.com/watch?v=o36Z_OqC2ac&list=PLdQddgMBv5zHc... Relational database systems (RDBMSs) pay a lot of attention to data consistency and compliance with a formal database schema.
A Database Perspective on Data Security
Although security is often related to privacy, they are not synonymous. Data security can be defined as the set of policies and techniques to ensure the confidentiality, availability, and integrity of data at all times. Data privacy refers to the fact that the parties accessing and using the data do so only in ways that comply with the agreed-upon purposes of data use in their roles. These purposes can be expressed as part of a company’s policy, but are also subject to legislation.
Improving Data Quality Using Data Governance
Many organizations nowadays are struggling with the quality of their data. Data quality (DQ) problems can arise in various ways. Here are common causes of bad data quality: These causes of DQ problems have always existed to a certain extent, since the beginning of the digital era. However, initially, most data processing applications and database systems existed in relative isolation, as so-called silos.
Web Scraping for Data Science with Python
In this article, we take a quick look at how web scraping can be useful in the context of data science projects. Web "scraping" (also called "web harvesting", "web data extraction" or even "web data mining"), can be defined as "the construction of an agent to download, parse, and organize data from the web in an automated manner".
New Book: Credit risk analytics, The R Companion
Credit risk analytics in R will enable you to build credit risk models from start to finish, with access to real credit data on accompanying website, you will master a wide range of applications. Credit risk analytics in R will enable you to build credit risk models from start to finish.
An Economic Perspective on Fraud Analytics: Calculating ROI of Fraud Detection Systems
By Bart Baesens, Véronique Van Vlasselaer and Wouter Verbeke The importance and need for effective fraud detection and prevention systems is highlighted by some recent numbers which give an indication of the estimated size and the financial impact of fraud: A typical organization loses 5% of its revenues to fraud each year (www.acfe.com) The total cost of insurance fraud (non-health insurance) in the U.S.A. is estimated to be more than $40 billion per year (www.fbi.gov) Fraud is costing the...
Is Your Company Ready for HR Analytics?
Although many companies have been investing heavily in big data and analytics, there have been few success stories in applying analytics to human resources. But that may be about to change. advertisement This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Big data and analytics are omnipresent in today’s business environment.
Fraud Analytics Using Social Networks
The rise of big data with new sources of data for analytics represents new opportunity to put data to work in organizations for a wide range of uses. A developing use case for leveraging data analytics on large datasets is fraud discovery. GOTCHA! is a new fraud detection technique developed by researchers in the U.S. and Belgium that uses social network-based analysis. To illustrate the technique, let’s look at real-life application of network analysis for tax avoidance detection.
Comparing Commercial Versus Open Source Software for Analytics
In order to set up an analytics environment, organizations need to make decisions about the hardware and software technologies to be adopted. Hardware-wise, big data requires specialized infrastructures to store, integrate, clean, and manage the data. Software-wise, many vendors, such as SAS, IBM, Microsoft, Oracle, and Matlab, are currently providing commercial solutions for big data and analytics.
Big Data for Credit Scoring: Opportunities and Challenges
Throughout the past few decades, banks have gathered plenty of information describing the default behavior of their customers. Examples of data collected include historical information about a customer’s date of birth, gender, income, and employment status.
Beginning Java Programming - O'Reilly Media
This kit includes everything you need, from analysis and visualization to management. Beginning Java Programming, The Object-Oriented Approach is your introduction to Java. Providing hands on experience, this comprehensive beginner's guide gives you practical examples and real-world problems that will test your skills and provide a deep understanding of tasks, including getting Java to talk to your data, creating a graphical user interface, and much more.
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