5 Ways Innovation Can Help AML Analytics Challenges

Garima Chaudhary
Oracle Financial Crime and Compliance Management Specialist, Oracle Financial Services

Among the most challenging aspects of instituting effective anti-money laundering and anti-fraud programs at financial institutions is the need to adapt quickly to changing patterns of financial crime. Earlier, the financial crime business team consists of analysts & investigators only, supported by technology. However, for the last few years, analytics experts have become an addition to these teams, especially after model risk management regulation (OCC SR11-7 in the US), which describes elements of a sound program to effectively manage model risk, namely the need for independent review and model validations. The primary responsibly of the analytics team (aka modeling team) within a financial crime program consist of pre-production scenario tuning, ongoing above-the-line (ATL) tuning, and below-the-line (BTL) tuning. Initially, users from the enterprise modeling team were loaned for financial crime analytics. However, due to the lack of subject-matter-expertise and increased focus, a dedicated analytics team was formed. Lately, as the compliance cost increased, the need for Machine Learning and advanced analytics became prominent overall, creating a much larger analytics group, a team of data scientists dedicated to financial crime.

Current Approach: Expensive & Slow in Adaption to New Threats

Given the reputational implications associated with the accuracy and effectiveness of models, issues concerning discovery, model validation, and documentation are of obvious concern to the industry. Erroneous or miss-specified models may lead to expensive look-backs or other regulatory fines. Below are some of the critical challenges which impact the productivity & effectiveness of the analytics program.

  1. Desktop Tools: In many institutions, desktop tools are leveraged, which cannot scale or integrate with other internal systems. Most importantly, desktop systems do not provide must have audit trail & data lineage capabilities.
  2. Limited Coding Languages: Typical systems provide the ability to code in specific languages only. However, based on the use cases, data volume & institutions existing infrastructure, analytics teams should be able to design models in any available coding languages such as R, Python, Scala, SQL, etc.
  3. Disparate Systems: Specially, larger institutions use multiple systems for different use cases such as authoring new scenarios, scenario tuning & machine learning. The systems, most of the time, use different coding languages. This requires additional, sometimes manual integration.
  4. Cumbersome Data Provisioning: Mostly, analytics systems are not well integrated with existing production systems; hence, acquiring production data requires cumbersome data provisioning process. Based on the institution’s policies, it can take days, sometimes weeks, for data provisioning. In many cases, by the time data is providing, it is outdated.
  5. Lack of Intuitive Visualizations: Lack of visualization options leaves the analytics team with limited ability to understand underlying data, model output & investigation outcome.
  6. Limited Discovery Capabilities: Generally, the risk of discovery is limited to scenario threshold tuning only, which does not provide the ability to discover completely unknown suspicious behaviors that institutions might be exposed to.
  7. Lack of Governance & Documentation: With model risk management regulations, institutions are required to maintain full audit trail & documentation of the modeling process, which is generally unavailable in typical modeling tools. This leads to the maintenance of two different systems, one for modeling and another one for model documentation. Overall, creating more work for the analytics team.
  8. Lack of Explainability: Typical, Machine Learning tools allow the data scientist to write Machine Learning models; however, due to lack of governance & full audit trail, such projects remain hard for human consumption.
  9. Inefficient Models Deployment Process: Once developed, the operationalization of models, especially Machine Learning models, is a considerable challenge. One of the primary reasons could be the dis-alignment of data attributes between analytics, transaction monitoring, and case management systems.
  10. Expensive: Overall, due to multiple systems that require integration, maintenance of model documentation & operationalization of models makes most of the analytics initiatives costly.

Getting Ahead of the Curve

Effective criminal pattern discovery and detection requires the application of a variety of techniques. An integrated platform that provides a single, unified workbench for Graph Analytics, data visualization, Machine Learning, scenario authoring, and testing for financial crime data is key for an efficient strategic program. Below are five aspects of the analytics program which can be game-changer in handling the challenges we discussed above.

  1. Centralized Data Lake: Institutions realize that effective discovery requires all the financial institution’s transactions, accounts, case, and other financial crimes related data be brought into an analytical data lake. Engineered as a portal into the enterprise’s financial crimes data lake, a platform which allows loading that data into the data lake automatically, significantly reduces the time and effort data scientists spend in preparing data for analysis. Additionally, institutions may have data in different types of systems (Oracle database, big data, structured/unstructured, files.), flexibility to connect these is critical. Lastly, connection strategy should be flexible to different coding languages. For example, if a user prefers to code in Python leveraging data from Hadoop, it should not require a different system. The ability to read and merge data from various sources for a use case (notebook) allows analytics team flexibility to access data quickly. This also significantly improves the efficiency of the IT team and will enable them to focus on the actual analysis of data.
  2. Polyglot Scenario Authoring: Apache Zeppelin and Jupyter notebooks are the de facto standard development tools for data scientists; Apache Spark is the most prevalent analytics engine on Big Data. A strategic platform should be able to leverage these open technologies and standards, thereby minimizing the need to re-train data scientists. Furthermore, the ability to use popular data science languages such as R, Python, SQL, and Scala increases modeler productivity. This is possible by leveraging the interpreter concept. An interpreter is a program that directly reads and executes the instructions written in a programming or scripting language without previously compiling the high-level language code into a machine language program. Analytics platform should allow configuration of various types of interpreters such as JDBC, R, Pyspark, Python, Spark-Scala, Spark-SQL, and graph query.
  3. Intuitive Visualization: The configuration of comprehensive use cases won’t be effective without proper visualization tools. Therefore, the ability to visualize the output of the configured code and flexibility to tailor to output to make it more meaningful will allow analytics teams to understand the output quickly and more effectively.

    Graph Analytics is proven to be revolutionary, which allows succinct expression of complex money movement patterns, detects multi-hop relationships, and identifies hubs and spokes of suspicious activities. Data scientists and analysts should be able to interactively explore financial crime data and gain insights into new and emerging financial crime patterns and trends. Ability to analyze historical data available in the database, and forecast the generated patterns using various interpreters is vital.
  1. Collaboration & Governance: In most cases, analytics teams use one system for the core technical activities and a second system for documentation. A single platform that includes full audit trail capabilities of notebook authoring, approval, publishing, and deployment takes away the additional documentation tasks from the users. Such a platform is more efficient compared to a model risk & governance regulation perspective.
  2. Unified Platform: Typically, case management and modeling are handled in two separate systems, which means deployment of new models and feedback loop from case management to analytics systems is generally not very efficient, and many require manual integration. Continuous discovery and modeling of new criminal behavioral patterns, coupled with the ability to deploy these models rapidly, is a critical requirement in today’s age of continually changing criminal behavior patterns. Graph Analytics and Machine Learning are essential methods for pattern discovery. They are most effective when applied over the data in a comprehensively designed enterprise-wide financial crimes data lake. Therefore, a strategic platform that is designed from the ground-up to enable data scientists to discover and model financial crime patterns rapidly will increase data scientist productivity drastically.

With digitalization paving the way for increased customer expectations from their financial services providers, there is a beeline of new products and services that are being offered by banks to cater to this demand. This also means that the volume of transactions is multifold when compared to that about ten years back. This added by the drastically changing risk profiles and criminal behavior patterns puts anti-financial crime initiatives in excessive focus. The involvement of analytics in financial crime programs has become a bare necessity. Hence it is critical to enable data scientists with the right tools that allow them to nip the bud before it is too late. Any missed financial crime always comes with the risk of huge penalties and even higher reputational risks to the banks.

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