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The Role of Technology in Anti-Money Laundering Compliance
Anti-Money Laundering (AML) Compliance departments are heavily based on personnel. Today, technology doesn’t play a significant role in AML, but this department is about to transform shortly as they are planning to invest heavily into new technology that will reduce staff count. The areas that require most change are Investigations, Know your customer (KYC), and Machine Learning.
Regulatory pressures and increased fines are pressurizing financial institutions to take precautionary measures. As a result, AML departments have expanded in low-cost cities to address a growing number of alerts. Along with adding capacity, pulling in data for a single case can take up to 50 percent of an investigators time. Companies are now investing in technology because of their ignorance in the past related to data access and storage that were created when financial institutions merged without proper IT investments. Currently, financial institutions rely on in-house experts and outside vendors regarding storage and access to data.
The utilization of standard templates is becoming common in the AML compliance industry which solves the most common cases with minimal modification. By 2023, compliance departments will incorporate the latest technology to automate the most common cases, and humans will only be required to investigate the most complex cases and to review machine solved instances.
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Financial institutions will use automatic tools to smoothen the KYC process. The client can take a picture of their government identity document on their mobile devices for the financial institution to automatically authenticate the document. Along with the document, the financial institution can verify the selfie taken by the client on their mobile device. When a financial institution receives a picture of the client from their mobile device, the institution can extract information like the client’s location, the type of operating system the device uses, and the client’s location. By feeding this data into a machine learning model, fraud can be prevented.
Machine learning can enable financial institutions in identifying suspicious activity efficiently. The computer learns as new data is fed which allows it to identify suspicious activity that has not been correctly programmed to recognize. This is also very helpful in detecting anomalies that a conventional monitoring system would have difficulty in determining. Shortly, AML compliance departments will be cutting-edge. The majority of investigations will be automated, KYC will be seamless, and machine learning will be used more effectively in identifying suspicious activity.