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This research explores how enterprise risk management (ERM) can be modernized to combat the rising financial threat of insurance fraud. By integrating artificial intelligence and machine learning into traditional frameworks like Basel II, insurers can shift from reactive investigations to proactive prevention. The author emphasizes the use of data analytics and Principal Component Analysis (PCA) to simplify complex claims data into clear, actionable risk categories. These advanced visualization techniques, such as confidence ellipses and heat maps, allow executives to identify fraudulent patterns and anomalies more efficiently. Ultimately, the paper provides a data-driven roadmap for casualty insurers to strengthen their operational resilience while maintaining regulatory compliance.

This study analyzes financial risk management in digital-only banking using quantitative methods. Phishing (35%) and ransomware (20%) cause major financial losses. Basel III compliance reduces fraud risks, while AI-driven fraud monitoring has inefficiencies. Regulatory enforcement improves fraud prevention by 1.90%, highlighting the need for stronger cybersecurity and regulatory measures.

This paper tackles corporate fraud detection using real-world Chinese stock market data. It highlights challenges like information overload and hidden fraud. The proposed KeGCNR model enhances detection with knowledge graph embeddings and robust training. Experiments show superior performance. Future research should address class imbalance and IND noise. Public datasets are provided.

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.

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.