Data Mining-Based Analytical Perspective For Financial Fraud Detection
DOI:
https://doi.org/10.61841/x6gq1989Keywords:
data mining, analytical perspective, financial fraud detection, decision trees, Naive Bayes, clustering, deep learning, feature selection, anomaly detection, fraud patterns, imbalanced data, interpretability, privacy, scalability, false positives, false negatives, machine learning, credit card fraud, fraud detection systems, data preprocessing.Abstract
Financial fraud poses a serious threat to both organisations and people, causing major financial losses and reputational harm. Traditional rule-based fraud detection systems frequently fall behind the rapidly changing nature of fraudulent operations. Data mining techniques have become effective tools for detecting and preventing financial fraud in response to this problem. This study attempts to investigate the use of data mining in identifying financial fraud from an analytical standpoint. We go over numerous data mining methods and algorithms that are frequently employed in fraud detection and emphasise their advantages and disadvantages. To improve the accuracy of fraud detection, we also investigate other data sources and feature engineering techniques. Finally, we explore future research objectives and give a case study to demonstrate the actual application of data mining approaches for financial fraud detection.
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