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Staying Ahead of Financial Crime

Every year, an estimated $2.4 trillion worth of proceeds from illegal activities are laundered through financial markets and the banking systems. However, many financial institutions today are playing a catch-up game as perpetrators of financial fraud and crime adapt to the latest technology innovations to launder money.

From the cyberheist on Bank of Bangladesh, to dirty money in sports, online vehicle fraud, and exploiting new digital currency (hello, Facebook’s Libra), criminal actors are constantly looking for weaknesses in the international financial system to exploit.

The good news is that while technology has been an enabler of crime, it can also become one of the most powerful tools to fight it. The bad news? Many existing anti-money laundering (AML) processes in financial institutions are not ready to tackle financial crime.

The Anti-Money Laundering Challenge Today

The amount of illegal activity that has been detected is a drop in the financial crime ocean. According to the United Nations Office on Drugs and Crime (PDF), less than one percent of criminal funds flowing through the international financial system is actually frozen or confiscated.

One key reason for the current predicament is the lack of an enterprise-wide approach to tackling financing crime. AML software of yore tended to be black-box rules-based behavior detection systems that did not have capabilities to extend beyond their respective departments or to process large volumes of transaction data generated from digital payments. Newer forms of encryption-based digital currencies further add to the challenge.

At the same time, compliance departments are facing increased regulatory and executive pressure as regulators now expect institutions to understand and demonstrate (PDF) that their money laundering and terrorist-financing combat programs are innovative and effective. Recent high-profile cases implicating large institutions has seen a rollover effect in client attrition, investor confidence, and market value that cause many boards to shudder.

New licenses for digital banks and payment operators are also opening up new routes for money laundering and a whole new data set to track from nontraditional financial services providers.

Close Encounters with the Criminal Mind

In order to effectively tackle financial crime, financial institutions need to be able to unravel the intricate web of global financial transactions and relationships.

From the trillions moved across offshore accounts (PDF) to the tiny amounts of funding (PDF) required to buy knives or rent vehicles, connections and organized crime rings can span geographies and both sides of the fence. In short, we need to think like a criminal.

With the vast web woven by organized crime, it is no longer enough to rely on bank data to spot financial crime behaviors. Financial intelligence between different public and private sector organizations will need to connect indicators from government databases, cybercriminal activity, geographical locations, social media chatter, and financial transactions to follow the dirty money.

While technology solutions around big data exist, they are only as good as the level of information that they can exploit. Effective AML technology goes beyond blocking transactions to infiltrating the criminal network, understanding who the different actors are, and the various roles they play in the larger nefarious scheme of things.

Financial information sharing (PDF) programs initiated by international agencies and regulators will only be as good as the actionable intelligence that the AML solutions can yield from the data.

Augment Existing Anti-Money Laundering Processes

The good news is that financial institutions need not rip and replace existing systems to tap into the latest innovations in AML technology.

Oracle’s leadership in anti-financial crime software in the last 15+ years and commitment to open source technology has helped bring down the costs of compliance by allowing new applications to fit in the existing tech stack in most institutions.

Oracle Financial Services Crime and Compliance Studio (PDF) moves away from the traditional rules-based behavior detection processes to risk-based analysis and fuses artificial intelligence (AI) and deep machine learning to solve the issue of data fragmentation within the institution.

Instead of trying to extrapolate a customer behavior from one or a few transactions, institutions can now look at over 300 data points for each customer to access their risk profile. This is because the platform includes an industry-first data model for graph analytics across financial crime data. Financial institution transactions, accounts, alerts, and other financial crimes-related data, such as watch lists and datasets, can now be brought into an analytical data lake that links everything together and enables discovery and analysis by frontline investigators.

Instead of a black box, the enterprise financial crimes graph model (EFCGM) serves as a window into enterprise’s financial crimes data lake. It collates disparate data sets into an enterprise-wide global graph, enabling a whole new set of financial crime use cases. The EFCGM links all of the institution’s financial crimes data—AML, fraud, alerts, sanctions lists, know your customer (KYC) data, and external datasets—and serves as the single, central source for compliance investigations.

Step Up Your Game

The latest AML technology is not enough if the end users cannot leverage actionable intelligence. Oracle Financial Services Crime and Compliance Studio makes complex data analytics and visualizations more accessible to every user level across the organization.

By providing a single, unified workbench for graph analytics, scenario authoring and testing, multiple teams can collaborate and access criminal pattern discovery and detection at their fingertips. A notebook user interface is created for human consumption with codes and results transparency.

Oracle Financial Services Crime and Compliance Studio makes complex analytics and visualizations accessible to business users. At the same time, the open platform is also friendly to technically-inclined users such as data scientists, scenario authors, and analysts who are able to use popular data science languages such as R, Python, SQL, and Scala.

This facilitates an enterprise-wide approach to AML in complex banking organizations that need to take into account multiple business lines, legal entities, and geographies for effective financial crime and compliance management.

With the Oracle Financial Services Crime and Compliance Studio, you can drive data scientists’ productivity with a unified tool for machine learning, graph analytics, and AML scenario authoring. Enable your data scientists to rapidly build powerful detection patterns and investigation dashboards that can be easily comprehended by business users to uncover hidden suspicious network patterns for effective AML and antifraud programs.

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