fraud

Fraud detection remains a significant challenge in the financial industry, requiring advanced machine learning (ML) techniques to detect fraudulent patterns while maintaining compliance with strict privacy regulations. Traditional ML models often rely on centralized data aggregation, which raises concerns about data security and regulatory constraints. Fraud cost businesses over $485.6Continue Reading

Financial fraud detection isn’t just important to banks—it’s essential. With global fraud losses surpassing $40 billion annually and sophisticated criminal networks constantly evolving their tactics, financial institutions face an increasingly complex threat landscape. Today’s fraud schemes operate across multiple accounts, institutions, and channels, creating intricate webs designed specifically to evadeContinue Reading

This post is co-written with Qing Chen and Mark Sinclair from Radial. Radial is the largest 3PL fulfillment provider, also offering integrated payment, fraud detection, and omnichannel solutions to mid-market and enterprise brands. With over 30 years of industry expertise, Radial tailors its services and solutions to align strategically withContinue Reading

As digital commerce expands, fraud detection has become critical in protecting businesses and consumers engaging in online transactions. Implementing machine learning (ML) algorithms enables real-time analysis of high-volume transactional data to rapidly identify fraudulent activity. This advanced capability helps mitigate financial risks and safeguard customer privacy within expanding digital markets.Continue Reading

The U.S. Securities and Exchange Commission (SEC) on Monday charged cryptocurrency-friendly lender Silvergate Capital (OTC:SICP) for “negligence-based fraud” on Monday. According to the SEC’s complaint, Silvergate and its former CFO Antonio Martino had misrepresented the company’s financial condition during a liquidity crisis and bank run following FTX’s collapse. The complaintContinue Reading