The library, integrated into the Salv Monitoring solution, aims to promote a collaborative approach among financial institutions, allowing them to address emerging criminal tactics while adhering to regulatory requirements. The initiative aims to bridge the gap in AML compliance, where broad, risk-based guidelines often leave institutions without concrete frameworks for monitoring financial crime.
The Collaborative Scenario Library offers financial institutions access to an anonymised repository of tested monitoring scenarios and rule frameworks. By leveraging this shared resource, AML teams can more effectively address the challenges posed by evolving financial crime patterns.
In the official press release, representatives from Salv emphasised the library's focus on actionable compliance. They explained that the tool provides institutions with shared insights that improve their ability to meet regulatory requirements while improving operational effectiveness. Moreover, the library’s design enables institutions to adopt scenarios proven to yield high true-positive rates for detecting suspicious activities, as organisations can modify these scenarios to fit their specific needs to be able to identify financial crimes early.
The library prioritises scenarios that produce effective alerts, supporting a collective approach to combatting crime. As criminal tactics often target multiple institutions, Salv believes that encouraging collaboration among financial entities can strengthen both individual and industry-wide defences.
Salv provides a SaaS platform designed to help financial institutions combat money laundering, fraud, and other financial crimes. Through its solutions, including tools for intelligence sharing, the company enables banks, fintechs, and payment service providers to recover stolen funds and enhance crime detection.
In December 2023, Salv raised EUR 3.9 million in new funding to fight increasing APP fraud. The investment facilitated Salv's expansion in the UK, a market experiencing a sharp increase in Authorised Push Payment (APP) fraud, where criminals use social engineering attacks involving impersonation to trick consumers or businesses into transferring money to their accounts.
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