Regression in Fraud Detection

Abdelrahim Al Aqqad

Chapter Chapter 13 in Fraud Analytics in Action, 2026, pp 315-334 from Springer

Abstract: Abstract This chapter introduces supervised learning as a cornerstone of predictive analytics for fraud detection, focusing on the distinction between regression and classification approaches based on the nature of the target variable. Regression methods estimate continuous outcomes such as the probability of fraud or the expected magnitude of financial loss, while classification methods assign categorical labels such as fraudulent versus legitimate transactions. The chapter examines the practical challenges of defining the target variable in fraud detection contexts, including the complications introduced by direct and indirect costs, reputational damage, and the time value of money. Key strategies are presented for managing common difficulties such as class imbalance, evolving fraud patterns, ethical concerns, and the need for model interpretability. A significant portion of the chapter is devoted to XGBoost (extreme gradient boosting), an ensemble learning technique that combines multiple weak learners through iterative boosting to produce highly accurate and adaptive predictions. The chapter culminates in a hands-on Python lab applying XGBoost to predict university admissions outcomes, demonstrating the algorithm's regression capabilities and reinforcing the broader principles of supervised modeling in fraud analytics.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-16023-2_13

Ordering information: This item can be ordered from
http://www.springer.com/9783032160232

DOI: 10.1007/978-3-032-16023-2_13

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().