One of the biggest problems facing banking systems globally is still money laundering.
The complexity of the techniques used by criminals to conceal illegal funds keeps getting better, making it harder for banks to identify and stop these operations. It is predicted that $5.05 trillion will be laundered in only one year, and institutions are finding it difficult to keep up with the high expense and intricate nature of compliance. Oracle Financial Services unveiled a cloud service driven by artificial intelligence to assist banks in reducing the risk of money laundering. The tool easily interfaces with current financial systems, improving their capacity to more accurately and effectively identify suspicious activity.
• The primary characteristics provided by these services include:
Evaluate and reduce risks from high-risk typologies in a proactive manner: Additionally, a Compliance Agent can pre-emptively evaluate and reduce hazards. associated with highly hazardous typologies, such as human trafficking. Banks should remain proactive in their preparation against unknown prospective assaults that are peculiar to those typologies by strengthening the TMS for probable high-risk typology practices. Maintaining a positive reputation with authorities and consumers is crucial for banks.
• Making judgments for risk modelling more quickly and with evidence:
The tool gives regulators and model risk teams access to evidence that facilitates effective transaction surveillance and management updates to counteract changing money laundering strategies. Its artificial intelligence (AI)-driven analytics support evidence-based compliance decisions by providing observations for more effective control selection and robust tracking of transactions.
• Evaluating the risk assessment of new banking products:
The service reduces time to market for innovative goods while preserving adherence by objectively evaluating the AML risk profile of innovative goods and evaluating procedures that minimize risks efficiently.
According to Wynne’s statement in the release, “AI and machine learning have tremendous potential to deliver higher efficiencies in the transactional modelling process and enhance the success rate of anti-money laundering and other monetary crime detection programs.”