How a Modern Anti-Money Laundering Program Can Drive Long-Term Success

John Edison
Global Head of Financial Crime and Compliance Management Products, Oracle Financial Services

By John Edison, Global Head of Financial Crime and Compliance Management Products, and Garima Chaudhary, Financial Crime and Compliance Management Specialist, Oracle Financial Services

Why Financial Institutions Should Consider Modernizing their Anti-Money laundering Programs

Increasingly sophisticated criminal schemes. Transnational criminal networks. Customer expectations for superior products, customer experience, and security. Chief Compliance Officers (CCOs) at banks and other financial institutions are under extreme pressure to get their anti-money laundering (AML) programs right. At the same time, they see a rising cost of compliance as AML regulations and fines increase across the globe.

Unfortunately, financial institutions that use disparate AML systems or rely on legacy technology are finding that these systems are neither efficient nor effective enough to address these pressures. AML teams spend much of their time gathering and preparing data, with little time left for analysis and investigation. And CCOs are finding that the accuracy of investigators’ decisions is negatively impacted by the lack of a complete view of each customer, account, and transaction. Additionally, there are evergreen problems of low detection and too many false positives. As a result, financial institutions risk regulatory fines and reputational damage.

For these financial institutions, it’s time to consider modernizing their AML programs. We will not sugar coat it, however. This is a significant undertaking that requires executive buy-in, alignment across multiple stakeholders, and a metrics-driven transformation roadmap. Financial institutions can manage this complexity by taking a phased approach that tackles their pain points in order of priority. They may first address excessive case volumes, overworked compliance staff, high false positives, low detection, or something else.

A modernized AML program provides tremendous benefits to financial institutions. By boosting effectiveness and efficiency, it helps them protect their customers, their business, and the financial system. It allows them to achieve compliance over the long term. And, it enables CCOs to support business growth by reducing the overall cost of compliance, improving customer experience, and surfacing customer insights that can be leveraged by other departments.

Here, we explain the critical components of a modern AML program, and how they can help financial institutions achieve long-term success.

A Consolidated Backend

The first step in the journey to AML program modernization is to consolidate the backend and move from disparate systems to a unified platform for KYC/CDD, monitoring, detection, investigation, and reporting.

A unified platform provides many benefits. First, it improves decision accuracy because it allows AML investigators and analysts to evaluate the risk of each alert holistically, regardless of where it comes from. Second, a standardized, unified platform helps financial institutions control costs by providing operational efficiencies, reducing the training costs required to keep staff up-to-date on multiple systems, and making daily workflows smoother. Finally, a unified platform can give CCOs a view of end-to-end compliance operations, which they can measure and manage within a single dashboard.

Unified Data

A modern AML program doesn’t just need a unified platform; it needs unified data as well. This can be achieved by having a common data foundation that can take inputs from any transaction system and any data source, including third-party data feeds and fragmented data. A typical data foundation serves as a single source of truth that enables consistency, transparency, and auditability.

With unified data, instead of performing extensive ETL cycles to make data available in the right locations at the right time, financial institutions can source data once and then use it for multiple use cases. Importantly, unified data makes it easy to leverage the detection data pipeline for discovery and modeling of new criminal patterns and for advanced analytics applications that enhance monitoring, detection, and investigations results.

With unified data, investigators will save much time on data gathering and therefore have more time for investigation and analysis. Unified data also helps investigators make better decisions by providing a better view of customer activity across channels, which enables them to see relationships and patterns that they otherwise might have missed.

Advanced Analytics

A consolidated backend and unified data don’t just create value on their own. They also create value by supporting advanced analytics capabilities. In particular, with unified data, financial institutions improve the completeness and accuracy of their data, which in turn enhances the effectiveness of any advanced analytics run on that data. And with a consolidated backend, financial institutions can gain seamless, timely access to production data and leverage advanced capabilities across multiple use cases.

While many financial institutions are excited about the potential of advanced analytics to improve the effectiveness and efficiency of their AML programs dramatically, they often wonder where to start. Fortunately, advanced analytics can be brought in gradually. Each financial institution can take the path and pace that works for them based on their needs and their data pipeline.

Advanced analytics to consider as part of an AML program modernization initiative include:

Machine Learning to Improve Detection

While traditional rules-based AML scenarios may keep financial institutions technically compliant, they are unable to adapt to the constantly changing patterns of modern financial crimes. Machine learning models can improve detection by rapidly adapting to evolving trends. To start, financial institutions can run models in parallel with rules and eventually turn off the rules when they and their regulators are comfortable.

Graph Analytics for Better Investigations

Graph analytics organizes data according to relationships, which allows financial institutions to better connect the dots among customers, accounts, transactions, external parties, and external data to reveal deep insights about customers’ behavior and connections. Importantly, graph analytics enables compelling, real-time visualizations of potential money laundering networks the AML investigators can explore to uncover otherwise hidden suspicious patterns.

Entity Resolution for a 360-Degree Customer View

Graph analytics enables entity resolution, which allows institutions to gain a genuinely 360-degree picture of their customers and external entities alike by identifying different instances of the same entity across data sources.

Correlation to Build Better Cases

Graph analytics can be combined with machine learning to build better cases by correlating red flags and suspicious alerts from various systems into a single case, which the graph visualization of which can be used as an investigation tool. This allows investigators to work on comprehensive cases instead of individual alerts, which significantly reduces the AML program workload.

Deep Learning to Find Patterns

Deep learning in financial crime can be valuable since similar past behaviors can be clusters that can predict new cases. Financial institutions can apply deep learning to graphs to find new graphs that are similar to previously identified graphs of criminal activity. Deep learning on graphs is especially useful because it is not limited to a specific point in time risk indicators but instead evaluates holistic patterns and networks.

Natural Language Processing for Automatic Case Narratives

Financial institutions can use natural language processing (NLP) to make graph-based investigations more efficient. NLP can automatically generate case narratives based on what an investigator uncovers in graph visualization. This eliminates the manual step of writing case narratives, thereby reducing investigation times dramatically and avoiding human errors.

Collective Intelligence and Collective Learning for Recommendations

Financial institutions can use artificial intelligence to learn from previous case decisions about graph networks and provide recommendations or suggest the next steps to investigators. This can help new analysts or investigators gain learn to identify criminal behavior more quickly.

Improve Operations

An AML program modernization initiative naturally leads to some operational improvements across the compliance department. Training is easier when staff works on a single system. A common data foundation means less time is spent on data provisioning. Machine learning models are quickly taken into production when advanced analytics is integrated throughout the platform. Some other operational improvements to consider include:

Leverage Open Source Technologies

Advanced analytics requires a set of data science tools, and it’s crucial to select the tools that make it easy for staff to do their best work. We suggest using popular open-source data science languages, technologies, and standards. This will minimize the need to re-train staff and increase productivity. Open source tools to consider include: Apache Zeppelin and Jupyter notebooks, Apache Spark as an analytics engine, and popular data science languages such as R, Python, SQL, and Scala.

Consider Cloud

Running a modernized AML program on cloud makes sense for many reasons, especially now that regulators broadly accept cloud for AML programs. It saves significant cost on maintaining data centers, provides scalability on demand, and makes ongoing upgrades easier. Management can be further streamlined by working with an AML vendor who provides both applications and cloud services.

Looking Ahead

It’s been a year and a half since U.S. financial regulators issued a joint statement in December 2018, encouraging financial institutions to consider, evaluate, and, where appropriate, responsibly implement innovative approaches to meet their Bank Secrecy Act/AML compliance obligations. This encouragement feels even timelier now as CCOs are called not only to address ongoing pressures from criminals, customers, and regulators but also to manage costs in a challenging economic environment. For CCOs looking to boost program effectiveness and efficiency to navigate these challenges, now may be just the right time to begin the journey to AML program modernization.

This article originally appeared in the May 2020 issue of CeFPro Magazine.

To learn more about how Oracle Financial Crime and Compliance Management can help your financial institution modernize its anti-financial crime program, feel free to message us here or here.

For more information, please visit:
Oracle Financial Crime and Compliance Management Solutions: oracle.com/aml          
Oracle Financial Services: oracle.com/financial-services

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