Rethinking anti-money laundering
The traditional methods banks use to address Anti-Money Laundering (AML) have been failing for some time. Financial crimes and fraud, like Ponzi schemes, are slipping through the cracks because criminals, with enough time and patience, can seemingly find their way around any security measure.
One of the central ways that regulators and institutions are cracking down on fraud is to remove barriers between fraud prevention units inside and between organizations. The removal of communication and data siloes allows these units to recognize broader trends in laundering and fraud and prevent them as they occur. More importantly, rethinking anti-money laundering as a data transparency issue gives prevention experts the tools they need to do their job… namely, to prevent fraud before it occurs.
Money laundering and fraud as challenges of collaboration and data visibility
More often than not, money laundering and fraud flourish when criminals can exploit what consumers and fraud prevention experts can’t see or know. Consider Bernie Madoff. By moving money between his private and business banking, all while doing business with small hedge funds and foreign banks, Madoff could siphon off tens of billions of dollars and make it look like his funds were producing hefty profits. In a more common fraud scenario, a merchant might be selling premium sneakers through an online retailer below cost. While consumers might validate that the goods are authentic, they don’t know how this retailer can sell them at such a loss. The reality might be that the merchant is using stolen credit cards to buy authentic premium sneakers.
Why do financial institutions struggle with this kind of fraud? As banks undergo digital transformation, the number of ways that they interact with customers grows, and so do the ways in which fraud can occur. FSIs frequently maintain distinct teams to handle these different types of fraud, often siloed from one another, which leads to fragmentation of prevention efforts and information. As a result, different teams are hyper-focused on serving a particular customer in a particular touchpoint.
Institutions with infrastructure fragmentation frequently struggle with even the most basic of analytic tasks, finding they have surprisingly little confidence that customer information is coherent and reliable from one platform to the next. These technical and cultural differences create further barriers to pooling data and analytics to present a united defense against financial criminals.
Breaking organizational siloes with comprehensive restructuring and technological transformation
With these challenges in mind, the clear solution is breaking down barriers preventing a clear picture of fraud in financial systems. First, banks need to collapse their fraud, financial crimes, and AML organizations into a single horizontal organization, much in the same way they have with HR or Legal departments. Next, they need to make data accessible and transparent across their entire organization through cloud technology and security.
Consolidating into a single fraud prevention department means tearing down walls between customer touchpoints. By doing this, banks gain insights across retail, institutional, and asset management transactions while optimizing their operations with shared resources. This consolidation will also better position financial institutions as regulators move to strengthen fraud detection and promote internal cooperation within financial institutions.
Bringing people together will help them work together, but they also need access to data from throughout the institution. Even more critically, as fraud schemes become more complex and insidious, prevention teams in different organizations should have ways to confidentially share data to promote mitigation more broadly. Recently, the UK’s Financial Conduct Authority (FCA) asked Microsoft to participate in a hackathon to look at different banks and their transactions to determine fraud when looking across payments from different bank’s transaction systems. This exercise identified three new criminal enterprises that would have been undetectable by a single bank looking at its payment stream.
What Microsoft does is create the cloud infrastructures that empower financial institutions to share data internally and externally in a confidential manner. Microsoft leverages a new technology they have developed with Intel called confidential computing. It is a secure enclave created in the hardware chip that allows data and code to be processed in an encrypted area that not even developers and administrators can access. This allows new ecosystems of competitors to collaborate on data without breaking security and receiving insights on how to recognize and prevent money laundering and theft.
What needs to be done
To combat money laundering and fraud, organizations are taking several steps. First, they are thinking about Financial Crime units, AML, Fraud units as a single organization across multiple touchpoints. Then, they are removing data siloes and avoiding Yet Another Data Warehouse (YADW) mentality. They can leverage technologies like Azure Stream Analytics to do serverless real-time analytics of payments from existing repositories so that fraud prevention teams can access that data in real-time. Furthermore, automating your processes with technologies like Microsoft Power Platform can allow you to catch fraudulent activities as they occur. Finally, build smart dashboards that integrate with that automation, real-time analytics, and repositories with Microsoft Power BI.
