Financial fraud poses a major challenge for the financial services industry. Not only does it come in many different forms, but it’s often difficult to detect due to the complexity of the relationships between entities and hidden patterns. And, once detected, financial institutions must notify customers of fraudulent activity in real time and take immediate action to stop it—for example, by blocking the customer’s credit card.
The financial services industry is also regulated and must report anti–money laundering (AML) activities and complete due diligence on their customers using Know Your Customer (or KYC) processes. This often requires analyzing data across products, markets, and geographies to identify relationships and patterns for AML.
Conceptually, money laundering is simple: Dirty money is passed around, blended with legitimate funds, and then turned into hard assets. In reality, it’s far more complicated, relying on a long, complex series of valid transfers between accounts created using synthetic (often stolen) identities and often using similar information, such as email and street addresses. In short, it involves a vast amount of data, which is why a unified data platform that supports advanced analytic techniques, such as graph analysis, is essential for AML programs.
The financial services industry continues to be highly monitored and regulated, and few areas have seen a greater increase in regulatory focus than anti–money laundering and counter-terrorist financing activities. Driven by vast criminal networks, financial fraud is a sophisticated and growing challenge requiring anti–money laundering solutions that provide insight across the enterprise and the entire globe.
The following architecture demonstrates how Oracle components and capabilities, including advanced analytics and machine learning, can be combined to create a data platform that covers the entire data analytics lifecycle and delivers the insights AML teams need to identify the anomalous patterns in behavior that can be indicative of fraudulent activity.
There are three main ways to inject data into an architecture to enable financial services organizations to identify potentially fraudulent activity.
Data persistence and processing is built on three (optionally four) components.
The ability to analyze, predict, and act is built on three technology approaches.
Oracle Data Platform can help your organization detect money laundering more effectively, boost the accuracy and efficiency of financial crime investigations, and streamline reporting processes to keep compliance costs down.
Learn how Oracle Data Platform for financial services can help you reduce risk and improve fraud detection and compliance in this use case.
Learn how Oracle Data Platform for financial services can help you reduce risk and improve regulatory compliance in this use case.
Learn how to manage financial services operations more efficiently using a data platform that helps improve performance with machine learning.
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