Dynamic graph fraud detection aims to distinguish fraudulent entities that deviate significantly from most benign entities within an ever-changing graph network. However, when dealing with different financial fraud scenarios, existing methods face challenges, resulting in difficulty in effectively ensuring ...
Financial fraud negatively impacts organizational administrative processes, particularly affecting owners and/or investors seeking to maximize their profits. Addressing this issue, this study presents a literature review on financial fraud detection through machine learning techniques. The PRISMA and Kitchenham ...
In addition to financial fraud, it’s important to monitor cybersecurity threats. Here are some possible scenarios: Online scamsinvolve deceptive schemes to defraud individuals. Phishingattempts trick users into divulging personal information through fraudulent emails or websites. ...
Financial fraud negatively impacts organizational administrative processes, particularly affecting owners and/or investors seeking to maximize their profits. Addressing this issue, this study presents a literature review on financial fraud detection through machine learning techniques. The PRISMA and Kitchenham ...
Expose complex fraud networks with industry-leading AI and machine learning capabilities Take a multi-dimensional approach to identifying complex threats, such as virtual currencies, human trafficking, trade-based money laundering and drug trafficking. Combine industry-proven, out-of-the-box scenarios wit...
In financial scenarios, attackers always aim for interference with defense models to seek exorbitant profits. Hence, how to detect and defend against harmful perturbations and design robust models, especially for GNN, are becoming major implementation goals. Interpretability needs to be improved A key ...
Byrefiningandreplacingoutdatedtransactionmonitoringscenarios, thevolumeofalertswascutby 90%,withaclearrationaletowardsauditorsand regulators. Reduce fraud by 90% Global Fintech Smarterfrauddetectionand preventionmethodsreducedfraudrates by 90%, allwithoutneedingmorestafforincreasingoperationalcosts. ...
is gaining traction as a decision-support tool in complex financial scenarios (Tao et al.,2021). Furthermore, the integration of blockchain and AI is paving the way for decentralized, transparent, and secure solutions, particularly in areas such as smart contracts and digital identity (Kshetri,...
These tests should be comprehensive and include a range of scenarios to ensure that your systems are prepared for any potential risks. By regularly testing your crime prevention and AML compliance systems, you can be confident that your organization is doing everything possible to prevent financial ...
Financial crime can take many forms: money laundering, terrorism funding, corruption, tax evasion, insurance fraud, etc. In all these scenarios, perpetrators need to hide their tracks to avoid jail time or fines. That means creating layers of obfuscation between their identity and their wrongdoing...