REMI is designed to empower forensic accountants with AI-driven insights that go far beyond traditional rule-based detections. Leveraging advanced pattern recognition, behavioral analysis, and anomaly detection, REMI identifies not only typical financial crimes like fraud and theft but also complex schemes such as anti-money laundering (AML), tax evasion, asset misappropriation, and financial statement fraud.

REMI’s intelligent algorithms analyze transactional data at scale, identifying unusual patterns and red flags across accounts, locations, individuals, and entities. Each detection is linked to a comprehensive timeline that provides forensic accountants with a clear narrative of the chain of events—from initial suspicious activity through to the final transaction. REMI also highlights connections among individuals, shell companies, offshore accounts, and high-risk jurisdictions, making it a powerful tool for tracing funds and uncovering illicit financial networks.

By eliminating the months of manual analysis often required to piece together evidence, REMI enables forensic accountants to uncover sophisticated money laundering techniques and other financial crimes in a matter of minutes. REMI’s AI-powered detections adapt to emerging threats and evolving tactics, providing forensic investigators with real-time insights that allow for swift and informed decision-making. With REMI, forensic accountants gain a full and precise understanding of each case, delivering the speed, accuracy, and depth of analysis required to stay ahead of financial crime.

AI-Driven AML and Fraud Detection: Automating Forensic Accounting for Faster, Smarter Investigations

REMI's 50 forensic accounting detections use AI-driven pattern recognition to identify financial anomalies like fraud, money laundering, and asset misappropriation. By automating red flag detection across accounts, entities, and locations, REMI empowers forensic accountants to rapidly investigate complex schemes, revealing hidden connections and reducing manual analysis time significantly.