Other parts of this series:
In the previous blog in this series, I looked at how advanced analytics and machine learning are transforming anti-money laundering (AML) transaction monitoring, increasing efficiency and lowering the number of false positive alerts.
Adverse media screening is another area in which innovation can change the way in which institutions obtain value from financial crime prevention initiatives. Issues across the adverse media screening process – from identification to decision-making – can lead to under-screening or over-screening, which are high risk and/or inefficient. Current screening processes often fail to resolve and contextualize adverse media “hits,” resulting in a barrage of alerts which then have to be discounted across multiple areas of the organization. This can potentially lead to a failure to connect the dots between clients and emerging risks.
A clearly defined, technologically sophisticated media screening policy can help better mitigate risk, lead to more informed decisions about clients and reduce the estimated over £1 billion spent annually by international banks on know your customer (KYC) and AML compliance.
Challenges abound in adverse media screening. Many banks lack a defined screening strategy, leading to variations in identifying who should be searched and how often. Different teams often conduct screening, leading to fragmentation and the escalation of matches across banking divisions. From a process standpoint, there may be a lack of coordination across lines of business as to who should be screened; a lack of process definition for what adverse media screening is, what sources are most useful, and how to identify matches; and inconsistent escalations of potential matches.
There are system problems, as well, with information silos created as different lines of business screen customers — often using different screening vendors or manual screening — which can lead to duplicative screening of customers.
To create more intelligent solutions for adverse media screening, Accenture is working with Ripjar Limited, a company providing real-time, context-rich monitoring and alerting capabilities. Ripjar capabilities help banks merge and interrogate large data sets for threats, running structured and unstructured data from multiple sources through the Ripjar Torch platform in real time.
Banks using the Accenture/Ripjar solution see not only a reduction in false positives but reduced costs from lower headcount and less duplication across AML, KYC and fraud functions. These institutions also enhance their ability to connect client information to potentially risky activity, both past and present, shifting from reactive to proactive risk management.
In the final blog in this series, I will look at integrated surveillance and how banks can benefit from a surveillance model which is driven by cultural change, rather than regulations.
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