IBM > Case Studies > Brazil’s REDE-LAB Identifies Illicit Assets with IBM Watson Explorer

Brazil’s REDE-LAB Identifies Illicit Assets with IBM Watson Explorer

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Technology Category
  • Analytics & Modeling - Big Data Analytics
  • Analytics & Modeling - Data Mining
Applicable Industries
  • Finance & Insurance
  • Mining
Applicable Functions
  • Quality Assurance
Use Cases
  • Asset Health Management (AHM)
  • Time Sensitive Networking
Services
  • Data Science Services
  • Testing & Certification
The Challenge
Brazil’s Ministry of Justice, specifically the Department of Assets Recovery and International Legal Cooperation, was tasked with the challenge of identifying and investigating the illicit proceeds of criminal activities such as corruption, organized crime, drug trafficking, and money laundering. The department created the Technology Laboratory Against Money Laundering (LAB-LD) to support complex investigations into corruption and money laundering. As of 2014, there are 43 laboratories across Brazil, which make up the Federal Laboratory Network Against Money Laundering (REDE-LAB). These laboratories analyze a vast amount of data to uncover and freeze illicit assets. However, the process of analyzing the data was complex and time-consuming, often taking months and thousands of person-hours. The challenge was further compounded by the fact that 60 percent of the data came from structured sources such as databases and spreadsheets, and the remaining 40 percent from unstructured sources, such as social media and email.
About The Customer
The customer in this case study is Brazil’s Ministry of Justice, specifically the Department of Assets Recovery and International Legal Cooperation. Established in 2003, this department is responsible for recovering the proceeds of corruption, organized crime, drug trafficking, and money laundering. In 2007, the department created the Technology Laboratory Against Money Laundering (LAB-LD) to support complex investigations into corruption and money laundering. As of 2014, there are 43 laboratories across Brazil, which make up the Federal Laboratory Network Against Money Laundering (REDE-LAB). These laboratories analyze a vast amount of data to uncover and freeze illicit assets.
The Solution
To automate and dramatically accelerate its analytics processes, REDE-LAB selected IBM Watson Explorer, delivered by IBM Business Partner Via Appia. Watson Explorer was implemented and configured to mine a comprehensive set of source data. At the start of an investigation, Watson Explorer builds a staging area to collate all of the pertinent data. By assessing the quality of the data in motion, terabyte-sized hard disks can be reduced to only a few gigabytes of relevant data. After indexing the remaining data, the solution enables the laboratories to perform keyword and semantic searches, regardless of whether the underlying data is in a structured or unstructured format. This has freed investigators to spend less time identifying the relevant data, and more time on the all-important analysis. The solution also uses TheXML software and search appliance from Via Appia to build ‘ontologies’ for IBM Watson Explorer at the start of investigations.
Operational Impact
  • The implementation of IBM Watson Explorer has significantly improved the operational efficiency of REDE-LAB. The solution has automated complex data mining processes, enabling investigators to accelerate their work dramatically. The time taken to identify illicit assets has been significantly reduced, allowing investigators to move faster than the criminals they are investigating. The solution has also enabled the laboratories to handle both structured and unstructured data, which has been a significant challenge in the past. The ability to perform keyword and semantic searches, regardless of the format of the underlying data, has been a major operational benefit. Furthermore, the use of ontologies has improved the precision and relevance of the investigations.
Quantitative Benefit
  • Reduced the data size from terabytes to a few gigabytes of relevant data
  • Identified the equivalent of USD8.9 billion in illicit assets
  • Accelerated investigations exponentially

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