Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for ...
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 ...
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 ...
2) Real-world application orientation: The benchmark should focus on real-world scenarios, including stock market analysis and trading, highlighting LLMs’ practical application capabilities. 3) Inclusion of financial domain-specific characteristics: It also needs to address the unique aspects of ...
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...
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...
Use advanced machine learning techniques to build and refine predictive models to target specific groups or segments, run numerous what-if scenarios simultaneously, and process results for each group or segment without having to sort or index data each time. Anti-financial crime optimization Performs ...
To validate the effectiveness of "TimeTrail," a study is conducted on a diverse financial dataset, surrounding various fraud scenarios. Results demonstrate the technique's capability to uncover hidden temporal correlations and patterns, performing better than conventional methods in both accuracy and ...
As a form of knowledge organization, KG has many practical downstream-oriented application scenarios in RM, such as risk identification(Wang, 2022; Yang, 2022; Xia et al., 2022), specific types of risk assessment (Yerashenia & Bolotov, 2019; Hong et al., 2021), risk conduction analysis (...