Monday, April 4, 2016

The Suspects will be Charged with Conspiracy to Import and Distribute Medicines

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 judgement 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 counter-measures, which include the employment of former investigators as staff members in senior compliance roles and advanced technology and data systems.

According to an 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:

  1. 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.
  2. 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
  3. Financial Transaction Data– Transactional structured/semi-structured data is typically held within the exchanges or institutions in which transactions have taken place.
  4. Personal Communications– Communications with counterparties can take many forms and manifest themselves in many systems.

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