arXiv:2608. 07471v1 Announce Type: cross Abstract: This study considers the task of applying artificial intelligence to recognize bank fraud.
arXiv:2607. 13469v1 Announce Type: cross Abstract: The banking sector increasingly relies on automated systems to monitor electronic transactions for signs of fraud, yet conventional rule-based approaches struggle with high false-positive rates and offer no justification for their outputs, limiting their utility for compliance teams.
arXiv:2606. 16663v1 Announce Type: new Abstract: Money laundering through insurance claims poses a threat to insurers both through fraudulent payouts and reputational and regulatory risk.
arXiv:2607. 19350v1 Announce Type: new Abstract: Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due to extreme data imbalance (0.
arXiv:2607. 01257v1 Announce Type: cross Abstract: The rapid digitalisation of financial systems has improved operational efficiency and financial inclusion while simultaneously increasing exposure to sophisticated forms of cyber-enabled fraud and electronic financial misconduct.
arXiv:2606. 08146v1 Announce Type: new Abstract: Fraud detection in payment, e-commerce, and telecommunications systems requires accuracy at the individual level, robustness under severe class imbalance, and ease of understanding for risk managers.
arXiv:2606. 17555v1 Announce Type: cross Abstract: Banks simultaneously face signature-based fraud (card-not-present attacks, account takeover, ATM cloning) and behavioural financial crime (structuring, layering, mule networks, business email compromise) -- two threat families with fundamentally different detection requirements.
arXiv:2606. 25007v1 Announce Type: new Abstract: Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams -- transactions, login sessions, risk signals -- that individually appear benign but collectively reveal fraudulent patterns.
arXiv:2608.20343v1 Announce Type: new Abstract: This study develops and evaluates a bankruptcy prediction framework that integrates consensus-based feature selection, hybrid resampling, stacking ense...
arXiv:2607. 05101v1 Announce Type: new Abstract: The application of machine learning-based predictive algorithms to Anti-Money Laundering (AML) has grown rapidly, driven by the vast volume of financial transaction data available to banks.
The paper explores a hybrid approach that combines traditional Journal Entry Tests (JETs) with machine learning techniques to enhance anomaly detection in general ledger data. It presents specialized models designed to improve the accuracy and validity of detected anomalies, thereby aiming to reduce false positives and increase audit efficiency. Experiments are conducted using synthetic data that includes both normal and anomalous journal entries.
arXiv:2605.14467v2 Announce Type: replace Abstract: We propose a new method of learning from positive and unlabeled (PU) examples in highly imbalanced datasets. Many real-world problems, such as dise...
The paper presents a deep learning credit risk early warning system that fuses heterogeneous data sources, such as transaction behaviors and social networks, using deep neural networks and attention mechanisms. By extracting multidimensional features, the system establishes an early identification mechanism for corporate and individual credit risks. Testing shows that this approach improves the accuracy and timeliness of risk warnings compared to traditional rule‑based engines.