10 articles tagged with #financial-crime. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Crypto crime reached a record $154 billion, with funds flowing to sanctioned entities surging 694% year-over-year. Sanctions evasion has become the fastest-growing category of cryptocurrency-related criminal activity.
The U.S. Justice Department has opened a compensation program for victims of OneCoin, a $4 billion cryptocurrency fraud scheme. OneCoin's co-founders Ruja Ignatova and Karl Sebastian Greenwood operated the scam from Bulgaria, with Ignatova disappearing in 2017 and Greenwood serving a 20-year prison sentence.
The FBI reported crypto-related fraud losses reached a record $11.4 billion in 2025, with seniors aged 60+ suffering disproportionate losses of $4.4 billion across 44,555 complaints. This trend underscores growing vulnerabilities in cryptocurrency adoption among older populations and highlights systemic fraud risks that may influence regulatory approaches to consumer protection in the digital asset space.
Binance faces renewed compliance challenges as multiple financial crime monitoring staff depart and Chief Compliance Officer Noah Perlman considers leaving. This comes as the exchange attempts to rebuild its compliance operations following a $4.3 billion guilty plea to US authorities.
The U.S. Treasury has released a report identifying key technology tools including AI, digital identity systems, blockchain analytics, and APIs that can be used to combat financial crime in the digital asset space. This represents a significant government initiative to leverage advanced technologies for cryptocurrency crime prevention and enforcement.
The Financial Action Task Force (FATF) has identified anti-money laundering (AML) risks associated with peer-to-peer stablecoin transfers. The organization suggests implementing freeze and deny-list controls as potential safeguards to mitigate these regulatory concerns.
Researchers developed LineMVGNN, a novel graph neural network method for anti-money laundering that uses multi-view graph learning to analyze transaction networks. The method outperformed existing approaches on real-world datasets including Ethereum phishing networks and financial payment data.
The Bank for International Settlements has issued a warning that criminals may prefer using an offline digital euro central bank digital currency (CBDC) over traditional cash for illicit activities. This highlights potential security and regulatory challenges that could arise with the implementation of offline CBDC functionality.
Pig-butchering crypto scams are emerging as the fastest-growing fraud threat, utilizing sophisticated long-term trust-building tactics to manipulate victims. These scams weaponize personal relationships and trust to defraud individuals of their cryptocurrency investments.
Researchers have released Tide, an open-source synthetic dataset generator for Anti-Money Laundering (AML) research that creates graph-based financial networks with both structural and temporal money laundering patterns. The tool addresses the lack of accessible transactional data for machine learning research due to privacy constraints, and includes two reference datasets with different illicit ratios for benchmarking detection models.
