2 résultats pour « Fraud detection »

On the Potential of Network‑Based Features for Fraud Detection

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Online transaction fraud poses significant challenges to businesses and consumers, with rule-based systems struggling to keep up. Machine learning, particularly personalized PageRank (PPR), offers promise by analyzing account relationships. Results show PPR enhances fraud detection models, providing valuable insights and stable features across datasets, improving predictive power.
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Can Words Reveal Fraud? A Lexicon Approach to Detecting Fraudulent Financial Reporting

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The study introduces a fraud lexicon and a Balanced Random Forest classifier for detecting fraudulent financial reporting. The classifier, utilizing the fraud lexicon as a feature set, demonstrates strong accuracy in predicting fraud across multiple samples from 2000 to 2017, outperforming random guessing by 40 to 48 percent. The fraud lexicon proves valuable for "bag-of-words" analysis, benefiting researchers, practitioners, auditors, regulators, and investors in enhancing fraud risk assessment procedures.
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