Today's data challenges mean large amounts of diverse data are readily available, but is often siloed, fragmented and difficult to fuse between internal and external sources.
Screening beyond just a small number of high-risk clients requires ever increasing resources to be allocated to compliance efforts. Manual ways of discovering AML violations are already obsolete.
Millions of news articles flood investigators every day with potential threats, requiring ever more vigilance by discovering sanctions violations from any client profile as soon as they occur.
Torch screens millions of clients in real time for KYC/AML/ABC threats across unstructured news media matching against sanctioned entities, client and transactional records.
Going beyond simple name matching, Torch resolves identities using advanced, deep learning algorithms. Reducing false positives, matching contextual identity features across multiple domains.
Configurable risk thresholds allow automated alerts to be triggered as soon the threat is detected, protecting your institution every hour of every day.
A single, fused view of all potential threats allows the detection of complex fraud, money laundering and other financial crimes across large, disparate data sources using advanced, automated analytics.
Integrating with any structured or unstructured data source, Torch gives investigators the ability to perform enhanced due diligence and follow the trail wherever it may lead.
The public need for threat discovery is balanced with the evolving expectations of privacy, security and auditing. Ripjar Torch seamlessly works with enterprise security models to provide assurance and management of information and access control.