Data Preparation Techniques

Abdelrahim Al Aqqad

Chapter Chapter 6 in Fraud Analytics in Action, 2026, pp 113-140 from Springer

Abstract: Abstract Data preparation is a foundational step in fraud analytics, transforming raw, unstructured data into reliable, analysis-ready datasets. This chapter covers the essential techniques required to clean and prepare fraud data, including handling missing values, detecting and treating outliers, data integration, sampling, and discretization. Missing value management is examined through methods such as deletion, mean/mode imputation, regression imputation, and specialized approaches including LOCF, NOCB, and KNN imputation. Outlier detection is explored using statistical methods such as Z-score and interquartile range (IQR), as well as machine learning approaches including isolation forests, one-class SVM, and elliptic envelope, with treatment techniques covering truncation, capping, and winsorizing. The chapter further discusses data integration through record linkage and entity resolution, data sampling strategies such as simple random, stratified, and cluster sampling, and data discretization methods including uniform-width binning, equal frequency binning, and k-means clustering. Practical examples with real-world fraud datasets illustrate each technique, equipping data analysts with the skills to improve data quality and reliability as a prerequisite to building accurate and effective fraud detection models.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-16023-2_6

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DOI: 10.1007/978-3-032-16023-2_6

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