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INSPIRIT AI FINANCIAL AID SERIES

Aggregate and reconcile customer data across internal systems to eliminate duplication and errors and enhance the consistency of AML measures between customers.Īutomatically enrich suspicious activity reports with relevant data from customer risk profiles or data from external sources.Ĭlick here to learn about our AI solutions that can help your business comply with AML regulations.

Identify beneficial owners of customer entities in the same manner, using external data faster and more efficiently.

Efficiently collect identifying data from a greater range of external sources, including sanctions lists and watch lists, in order to construct a more accurate customer risk profile.

In more detail, the CDD and KYC applications of financial crime technology will allow firms to: The quality and quantity of AI-enhanced CDD will give compliance employees a greater range of relevant AML data that can be used to inform risk assessments, suspicious activity reports and subsequent investigations.

Automated AI systems allow the CDD and Know Your Customer (KYC) processes to take place faster and with greater depth and scope.

Customer Insights from Financial Crime Technology: Those deviations from the norm would represent a new set of data inputs that could, in turn, be analyzed by an AI algorithm to determine whether a suspicious activity report (SAR) is warranted. More specifically, with the benefit of ML, an AML system could become sensitive to changes in behavior, however subtle, that conventional AML checks might miss.

When customers’ transaction data is inputted into an AML program, machine learning models can analyze that behavior to make predictions and judgments about that customer in the future. The practical advantages of financial crime technology and money laundering tools to an AML program are as follows: Changes in Behavior: By utilizing previously analyzed CDD and AML transaction monitoring data, machine learning tools can gauge emerging customer behavior and make a more accurate determination as to the level of money laundering risk that behavior represents. The application of machine learning within an AI infrastructure means that AML programs can, in theory, become even more efficient. These algorithms analyze huge amounts of customer data, including customer due diligence (CDD), sanctions screening and transaction monitoring inputs, to perform a variety of automated tasks in order to remediate suspicious activity.

INSPIRIT AI FINANCIAL AID SERIES

What is Artificial Intelligence and Machine LearningĪI, deployed as part of a firm’s AML compliance program, represents a series of algorithms that control the digital measures put in place to detect money laundering (and other criminal activities).

Since modern AML requires firms to deal with vast amounts of complex customer data, many are turning to financial crime technology, specifically artificial intelligence (AI) and machine learning (ML) systems, to help them detect money laundering activities and satisfy their evolving compliance obligations.ĪI holds such promise as an AML tool because it not only performs tasks faster than a human AML compliance officer but, via machine learning, has the capability to adapt to new threats and new money laundering methodologies, ensuring that firms are able to re-position quickly in different regulatory environments and stay one step ahead of the criminals. The fight against money laundering is a huge challenge for financial institutions around the world, and as criminal methodologies grow more sophisticated, so must the AML measures put in place to stop them. AI and Machine Learning in Financial Crime Technology