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A Modern Anti-Money Laundering Program: What It Is and What It Does

A guide for financial institutions to meet new anti-money laundering (AML) requirements and embark on their AML program modernization journey.

A Modern Anti-Money Laundering (AML) Program Guide

 

anti-money laundering program guide

Why modernize your AML program

Financial crimes, specifically money-laundering schemes, grow unchecked in both complexity and volume. The cost of these crimes is broad and deep, encompassing and incalculable human and environmental toll. Lives and communities are destroyed by terrorism, human and drug trafficking, as well as illicit wildlife trade. It also has a steep economic cost, with money laundering activity accounting for 2 percent to 5 percent of global GDP. At the same time, financial services organizations face growing risk. Regulators levied more than $8 billion in fines in 2019, nearly double 2018 penalties, and the trend continues.

As the threat landscape changes continuously, it’s more important than ever that financial institutions elevate their defensive posture to match the increasingly perilous terrain. In fact, this is precisely the time that financial institutions should be focused on modernizing their anti-money laundering (AML) programs to bolster their position today and into the future. For Chief Compliance Officers (CCOs) looking to boost program effectiveness and efficiency to navigate these challenges, it’s the perfect time to begin the journey to AML program modernization.

The AML landscape: Rocky and risky

Banks have long grappled with extreme complexity, disparity, and redundancy of mission-critical systems across their enterprises. Their end-to-end AML environments are no exception. For example, in the transaction monitoring function and beyond, solutions do not support sufficient logic and flexibility in solutions, leading to high numbers of false positives. On the investigative front, solutions are disparate and data is isolated—limiting transparency, insight, and prompt action. These unfortunate realities contribute to higher compliance costs, suboptimal outcomes, and elevated risk across the AML compliance process.

 

Current challenges across the AML program

Impact of relying on legacy systems

The risk is real—disparate legacy AML systems and processes:

  • Lack the agility needed to adapt readily to ever-changing schemes and regulatory requirements. Most legacy environments contain rules-based systems trained on historical data that do not readily accommodate the changing landscape.
  • Are increasingly expensive to maintain—in the short and long term. Inflexible legacy systems cannot keep pace with new challenges, technologies, threats, and regulations. As a result, firms are forced to deploy new point-solution tools and technologies, which they must then integrate to their legacy systems. This drives up costs and complexity.
  • Drive higher overall program inefficiency due to an elevated need for manual processes and intervention associated with false positives.
  • Increase risk—financial, regulatory, and reputation.
  • Compromise the customer experience due to false positives.

False positive paradox

The high number of false-positive events is one of the most vexing AML program challenges. Event investigation requires significant time, effort, and budget as much of the data aggregation and analysis requires manual intervention. In the interim, transactions can be delayed or halted unnecessarily, leading to customer frustration. Firms struggle with navigating the tradeoff between risk tolerance and the false positive rate.

The false positive alert rate is staggering (surpassing 80% or 90% of the alerts) — Celent 2019. The high false positive rate is holding back the industry from achieving the true power of automation. If an institution can reduce the false positive rate, it clears the way to widely embrace automation and reap the efficiencies it can deliver.

Modernize today for brighter days ahead

Forward-thinking organizations are embracing a holistic compliance and financial crime-fighting culture that permeates the organization and is not simply siloed in the risk department. They are also embracing next-generation machine learning (ML) technologies. A modern approach to AML also asserts that compliance can no longer be viewed simply as a “check-the-box” exercise, but as a business process that delivers real value to the organization and helps to secure its future.

Financial institutions that embrace AML program modernization are well-positioned to gain benefits that extend well beyond compliance. These include:

  • Protecting customers, the business, and the financial system by boosting AML effectiveness
  • Elevating AML program efficiency, allowing financial institutions to optimize the impact of resources allocated to this critical function
  • Ensuring continued compliance over the long term, even as requirements change
  • Equipping CCOs to support business growth by boosting AML efficiency and accuracy, improving the customer experience through a better know your customer (KYC) experience, and surfacing customer insights that other departments can leverage to drive growth
  • Accelerating the organization’s enterprise data strategy, reducing data provisioning time and costs while increasing quality

Anatomy of a modern AML program

1. A consolidated back end

The first step in the journey to AML program modernization is to consolidate the back end and move from disparate systems to a unified platform for KYC/customer due diligence (CDD), monitoring, detection, investigation, and reporting. A unified platform provides several benefits. First, it improves decision accuracy because it allows AML investigators and analysts to evaluate the risk of each event 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.

2. Unified and context-appropriate data

A modern AML program requires more than a unified platform; it also needs unified and context-appropriate data. This can be achieved by having a common data foundation that can take input from any transaction system and any data source, including third-party data feeds and fragmented data. A common data foundation serves as a single source of truth that facilitates quality control and enables consistency, transparency, and auditability. To truly modernize the AML process, data must be context-appropriate. It’s not enough to simply aggregate information on a customer origination. To truly bring new levels of effectiveness to the AML process, financial institutions must also match sentiment to that data, extending well beyond fuzzy matching. With unified and context-appropriate data, financial institutions can source data once and use it for multiple use cases, instead of performing extensive ETL cycles to make data available in the right location at the right time. 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 investigation results.

3. Advanced analytics

While many financial institutions are excited about the potential of advanced analytics to dramatically improve the effectiveness and efficiency of their AML programs, 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, elevating risk to the business and its reputation. Machine-learning models can improve detection by rapidly adapting to changing patterns.

    To start, financial institutions can run models in parallel with rules-based scenarios and eventually turn off the rules when their regulators are comfortable. Machine learning models can be overlaid on top of rules-based scenarios to detect highly suspicious activity. They can also simplify tuning by replacing multiple rules and eliminating the need to tune multiple parameters in favor of a single probability score.

  • Graph analytics for better investigations
    Efficient investigation of highly organized financial crime requires technologies such as graph analytics to succinctly express intricate money movement patterns, detect multi-hop relationships, and identify hubs and spokes of activity. Graph analytics leveraging a single source of data powers investigators with an ability to search customer information from various source systems and allows the linkage of customers, accounts, external entities, transactions, and external data stored in disparate operational silos. A single source also provides a 360-degree view of a customer, foreign body, or account for a holistic view of the case, transactions, and external data of interest. Graph algorithms, such as connected components and shortest path, can generate automatic linkages. For example, such linkages could be based on customer identification numbers, name-matching, shared phone numbers, tax IDs, and more.

    Furthermore, investigators can drill down (expand/collapse) on customer information and visualize related parties using graph analytics. Graph analytics can be combined with machine learning to build better cases by correlating red flags and suspicious events from various systems into a single case. This allows investigators to work on comprehensive cases instead of single events, which significantly reduces AML program workload.
  • Entity resolution for a 360-degree view
    Graph analytics enables entity resolution, which allows institutions to gain a genuine 360-degree picture of their customers and external entities, alike, by identifying different instances of the same entity across data sources.

  • Deep learning to find patterns
    Deep learning in financial crime can be valuable because 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 effective because it is not limited to a specific point in time for risk indicators but instead evaluates holistic patterns and networks.
  • NLP 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 a 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 next steps to investigators. This can help new analysts or investigators learn to identify criminal behavior more quickly.

Operational considerations

An AML program modernization initiative naturally leads to some operational improvements across the compliance department. Training is easier when staff members work on a single system. A common data foundation means less time is spent on data provisioning. Machine learning models are easily 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 important to select the tools that make it easy for staff to do their best work. Open source data science languages, technologies, and standards minimize the need to retrain staff and increase productivity. 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 the cloud
    Running a modernized AML program in the cloud makes sense for a lot of reasons, especially now that regulators broadly accept the cloud for AML programs. It saves significant costs on maintaining data centers, provides scalability on demand, and makes ongoing upgrades easier. Management can be further streamlined by working with an AML vendor that provides both applications and cloud services.

Engineered for success: Oracle Financial Crime and Compliance Management

Oracle Financial Crime and Compliance Management allows financial institutions to efficiently detect, investigate, and report suspected money laundering and other financial crimes to comply with current and future regulations and guidelines. It provides automated, comprehensive, and consistent surveillance of all accounts, customers, correspondents, and third-parties in transactions across all business lines. Machine learning capabilities, including graphic analytics and NLP, deliver unprecedented insight and efficiency.

The Oracle value proposition:

Engineered for Success—Oracle Financial Crime and Compliance Management

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