More Money Spent on Less Money Laundered: The AML Big Data Story

  • Datameer, Inc.
  • February 19, 2018
More Money Spent on Less Money Laundered: The AML Big Data Story

The bankers also DON’T need to hear another glaringly obvious anecdote about an analyst who has just joined the company and has a great opportunity to work with King/Prince/Senator X from non-cooperative African/Middle Eastern jurisdiction Y – and most importantly – you know that when in doubt, ALWAYS call the company AML/Compliance hotline.

BUT…for a bank facing large fines, what happens when the scenario isn’t so clear and the answers aren’t multiple-choice? How can your technology and your data infrastructure help you rely less on personal judgment and more on facts to prevent money laundering and terrorist financing?

Fines – and budgets – are growing

Fines and monetary settlements for banks not in compliance with AML regulations are growing and surpassed $13.4 billion in 2014. As such, banks are increasing their investment in countermeasures, which include the employment of former investigators as staff members in senior compliance roles and advanced technology and data systems.

According to Ovum’s annual ICT Enterprise Insights survey, compliance continues to be a core driver of growing IT budgets. The survey showed that 55 percent of retail banking respondents expected AML-related IT budgets to grow in 2016. If banks have the IT budget – the next question is – how can they most effectively use it to prevent money laundering and comply with regulatory mandates? The answer lies in big data and analytics.

Follow the money with the data

AML presents a data analytics challenge with a wide variety of sources and types of data available for analysis. These encompass both public and private data sets that may be structured, semi-structured, or unstructured, including:

  • Publicly Available Sanctions Lists– Data sets include the OFAC (Office of Foreign Assets Control) sanctions lists of Specially Designated Nationals (SDNs), Politically Exposed Persons (PEPs), sanctions programs, and countries.
  • Client and Legal Entity Data– Banks have historically managed their own client databases within the walls of their institutions or relied on other commercially available data on individuals and entities. Recently, they have started to consolidate efforts with the creation of client and legal entity data utilities to be leveraged across multiple institutions. These greatly improve a bank’s customer identification and due diligence capabilities and provide a common identification method. The utilities were designed by and are supported and utilized by the world’s largest institutions and include the Clarient Entity Hub and
  • Financial Transaction Data– Transactional structured/semi-structured data is typically held within the exchanges or institutions in which transactions have taken place.
  • Personal Communications– Communications with counterparties can take many forms and manifest themselves in many systems.

When banks harness the value of this data, they are able to set up big data analytics to help drive the discovery of money laundering. As banks are actively adopting big data platforms, including Hadoop and supporting analytical tools, they can be applied to help solve this problem. A platform that enables the ingestion, enrichment, analysis, and visualization of these diverse, large, and constantly changing data sets can be the bank’s best asset.

I recently had the opportunity to speak with a former investigator with Homeland Security Investigations (HSI) who runs a consultancy focused on using big data analytical tools to assist banks in achieving better AML compliance. He describes a situation where financial institutions are experiencing exponential growth in AML compliance requirements and reporting burdens. “By far, the best solution to fulfill rapidly growing compliance requirements is provided by big data analytical tools which drastically lower compliance costs and satisfy the due diligence required by regulatory agencies,” says the former investigator.

Examples of common analytics include reviewing transaction data to see if there are trends in transaction size. Specifically, currency transaction reporting is required for all transactions above $10,000 and money launderers have resorted to completing transactions just below this threshold to avoid those reporting requirements. In the industry, this is known as surfing, and identifying consistent behavior of completing transactions just below this threshold may indicate money-laundering activity.

In addition, by enriching transaction data with client/legal entity data (including names, addresses, and other identifiers), and publicly available OFAC lists, banks can track transactions to determine if they were completed by known high-risk individuals or non-cooperative jurisdictions. Enriching this data further, with verbal and written communications information can help cast the net wider when looking at potential indicators.

In the world we live in today – no individual or institution wants to aid money laundering or provide financing to ISIS or other known terrorist or criminal organizations. With the right investment in the right technology and data platforms, we can all sleep better at night knowing that we are applying analytics to help address the problem.

Data Transformation is Critical to the Process

Effective AML analytics begins and ends with clean, organized, and processed data.  How you transform your data is critical to this, in terms of both process and how.  AML analytics are assembled by analyzing complex and diverse datasets that need to be cleansed, blended, and shaped into final form.  Often times this involves high degrees of collaboration between data engineering and analytics teams.  It also requires rich data documentation to back up compliance processes.

The Datameer SaaS Data Transformation is the industry’s first collaborative, multi-persona data transformation platform integrated into Snowflake.  The easy-to-use, multi-persona UI, with no-code, low-code, and code (SQL) tools, brings together your entire team – data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data.  Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowd-sourced data governance.  Direct integration into Snowflake keeps data secure and lowers costs by leveraging Snowflake’s scalable compute and storage.

Learn more about our innovative data transformation solution by scheduling a demo today!

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