Fingerprint Presence Fraud Detection Using Tight Clustering on Employee’s Presence and Activity Data

Abstract

Detecting fraud in fingerprint presence poses a unique challenge as we cannot rely on an existing employee’s attribute. Furthermore, analyzing using a supervised algorithm cannot handle unlabeled data [1] that generated uniquely for this case. We study the patterns of employee’s presence and activity report data and found that fraud action tends to be closely similar to other fraud action. Therefore, we propose a tight clustering method to detect fraud in fingerprint data using DBSCAN (Density-based spatial clustering of applications with noise) algorithm, as tight distance calculation removes non-fraud data because non-fraud data is generated to be unique naturally.