One of the key functions of an anti-money laundering (AML) solution is to get it easy for financial institutions to identify and report suspicious activities to governing bodies. This is done by offering a suspicious activity report (SAR) to the regulator that governs the services of a particular financial institution.
There are some challenges such as, how then can financial organizations reduce the cost and resources they require to maintain the quality of their reports, have their establishment free from illegal exercises like money laundering, as well as remain obedient to the measures set by regulatory bodies? The answer may be observed in these three innovations used in enterprise-level AML solutions:
Recognizing Suspicious Patterns by Machine Learning
Traditional AML programs are typically built up of many components like transaction monitoring, case management, and analytics, to list a few. The segmented process has served as a bane and a boon to both financial institutions and malicious things. While it has earlier allowed legitimate companies to refine the way they identify suspicious activities, financial criminals are also finding ways to catch up and apply this segmented system to their benefit. They do this by hiding traces of their illegal activities in several platforms such as the cloud, social media, and big data, making detection a more challenging task.
Machine learning, or a kind of artificial intelligence with the ability to determine from existing data, evolves into the picture as a step up from the traditional query-based detection system that financial organizations now use to single out suspicious activities.
Based on the money-laundering patterns that criminals currently rely on, the AI can teach itself to address expressions of complex detection practices. This means that the technology can continuously filter and update rules without needing human intervention. In turn, this can save the financial institution’s compliance team highly efficient without necessarily bringing new and specific personnel into the picture. Take note, though, that machine learning does not reduce the need for confirmation of detection configuration; the technology simply facilitates the detection process.
Automating Investigations by Robotics
Robotics refers to the method of using robots to automate work, something that can be done when managing investigations. The search can be automated from start to end, or it can also be created to seamlessly integrate the human and robotic elements of the process. Using robotics, the investigation method can be made more effective and less resource-intensive without making permits with the quality of the report. At the same time, because the compliance team is supported by a robot and is no longer bogged down by mundane jobs, its members can concentrate on enhancing their skills and enhancing their decision-making processes.
Managing Costs by Cloud Solutions
More than half of an average company’s compliance cost goes to technology, while a vital portion of the other half of the budget proceeds toward staffing. These expenses, in part, can be due to using AML solutions that don’t appear with automatic updates. Solutions like this often need on-premise deployment with the help of a technician from the solutions provider, a program that is prone to setbacks and errors.
A service-based cloud solution allows a mode of refreshing and delivering software on a subscription basis. The companies subscribed to this service can then presume the solution they are using to be the latest version. Without bothering about whether or not they’re operating with the updated version of their AML solution or if the solution is up-to-date with the newest rules set by regulatory bodies, the compliance team can concentrate on the quality of their report.
A future-proof AML solution can go a long method in streamlining the AML reporting process. With these three technologies at their fingertips, financial institutions can relieve the burden that SAR filing and compliance efforts place on their establishment.
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