Fraud Analytics and Its Importance

Abdelrahim AL Aqqad ()

Chapter Chapter 3 in Fraud Analytics in Action, 2026, pp 37-45 from Springer

Abstract: Abstract This chapter introduces fraud analytics and explains its growing importance in modern organizations. It traces the development of fraud analytics from manual review methods to advanced data-driven techniques that use statistical analysis, data mining, and machine learning to detect suspicious patterns and anomalies. The chapter highlights how fraud analytics helps organizations prevent losses, protect reputation, support compliance, and strengthen customer trust. It also discusses why organizations should invest in fraud analytics and outlines the main features and challenges of effective fraud detection models, including interpretability, economic value, imbalanced data, false positives, and the use of performance measures such as precision, recall, F1 score, and the confusion matrix. Overall, the chapter provides a practical foundation for understanding the role of fraud analytics in improving fraud prevention and detection. Keywords Fraud analytics; Fraud detection; Fraud prevention; Machine learning; Data mining; Anomaly detection; Imbalanced data; Precision and recall; Confusion matrix; Regulatory compliance; Fraud risk management.

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_3

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

DOI: 10.1007/978-3-032-16023-2_3

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 ().