Transnational criminal organizations, foreign intelligence services, terrorist groups, and other criminals move billions of dollars through the international banking system each year in an effort to conceal the origin of illicit funds. In the US alone, there are hundreds of billions of dollars in concealed transactions moved around the country annually. 

Establishing a complete picture of who is making these illicit transactions and where these funds are flowing is critical to stemming the tide of money laundering. This is why accessing and integrating third-party data, with the Bank Secrecy Act (BSA) available from the Financial Crimes Enforcement Network (FinCEN) have become so important. 

BSA data offers comprehensive national coverage for a broad range of scenarios involving significant cash transactions, suspicious behaviors, and foreign influenced accounts. Given the extensive regulatory scope across various industries, BSA reporting provides a fairly complete picture of all relevant financial activity nationwide as well as inbound and outbound international transactions that stem beyond our borders. But it doesn’t necessarily tell the whole story. 

Why supplementing BSA data with third-party sources is so important 

To create a more complete picture of these illicit transactional networks, savvy investigators are finding that by incorporating additional data sources on top of information gleaned from sources like BSA, they can further enhance their analysis and uncover more complex patterns. Augmenting BSA data in this manner allows investigators to gain far deeper insights into more specific activities such as human trafficking, terrorist financing, and narcotics distribution. 

Sources used to enhance the analysis of BSA data fall into several types, each with its own complexities, restrictions, and associated costs: 

1. Internal data includes data sources controlled by the primary analytical authority (private/government) such as local databases managed by an organization. Access to this data is typically regulated and restricted to official use. 

2. Government data encompasses a wide range of government data sources, including BSA data, Trusted Traveler, Social Security Administration, No-Fly Lists, Motor Vehicle records, licensing/permitting data, and more. 

3. Private business data includes data from various private businesses, such as Western Union, AT&T, Verizon, United Airlines, Ford, Google, Tesla, and others. Access to this data may require specific agreements or partnerships. 

4. Subscription data includes data from subscription-based providers, such as Thomson Reuters, LexisNexis, TransUnion, Moody’s, and Dow Jones. These providers offer curated content focused on specific domains of interest. Some providers offer API access for targeted queries, while others provide fully downloadable, structured content. 

5. Open-source and social media data includes information from publicly accessible sources like Twitter, Facebook, Newsgroups, Wikipedia, Craigslist, Reddit, Quora, and others. While this data is publicly available, extracting and analyzing it can be challenging due to controls (terms-of-use), throughputs, governors, formats, or protocols. 

6. Third-party tools and technologies, including AI/ML, NLP, UFED, TensorFlow, Hugging Face, Jupyter,  LibPostal, NominoData, ShadowDragon and others, are used to process, match, or derive new insights from the integrated data. 

Enriching FinCrime investigations with DMF and BSA data 

A prime example of how to enrich data in financial crime investigations is by integrating the Death Master File (DMF) data with BSA data. Provided by the Social Security Administration (SSA) and available from NTIS, the DMF is a comprehensive database containing records of over 90+ million deceased individuals. 

By cross-referencing DMF with BSA data, investigators can identify instances where subjects listed in financial transactions have used the SSN of a deceased person. This crucial information can help uncover fraudulent activities such as identity theft, fraud, misrepresentation, data-entry errors, or the exploitation of their identities to facilitate illicit financial transactions. 

The DMF contains essential information including the social security number, first name, last name, middle name, date of birth, and date of death. In previous versions, it also included zip codes for birth and death locations. Also, before the SSN-randomization occurred in 2011, an SSN also defined the geographical location where it was issued, a timeframe (year or decade) when it was issued via the High Number List, and if it met the proper sequencing to determine its validity.  

Gathering data is often the easy part. Sorting through what can amount to millions (or billions) of data points to establish relevant content for an investigation is the hard part. This is where technological advances, like entity resolution, are absolutely essential. 

How entity resolution can improve investigation accuracy 

Entity resolution is a straightforward process involving a simple match based on the 9-digit Social Security Number (SSN). Figure 8 visually depicts how these matches are presented to the analyst, significantly improving the accuracy and effectiveness of investigations. 

Figure 1. Subject Using a DMF-SSN 

Advanced matching techniques offered by Quantexa can significantly enhance the effectiveness of cross-referencing the DMF with BSA data. Beyond simple exact matches on the SSN, algorithms can assess the similarity of names, dates of birth, and other relevant attributes. For instance, a match between "Vicky Fenix" in BSA and "Veronica Phoenix" in DMF with the same SSN would be considered a strong match, even though the names are not identical. If the name “Walter Smith” were in the BSA, then a mismatch would be immediately flagged. 

The timing of the death relative to the financial transaction is also crucial. If an individual is listed on a BSA record after their reported date of death, it raises significant red flags. As an FYI, everyone in the BSA data will eventually be on the DMF given enough time, so comparing the dates is critical. 

Here is the link to the NTIS Limited Access Death Master File (LADMF): https://www.ntis.gov/ladmf/ladmf.xhtml 

Here is the link to the Limited Access Death Master File Extract Output Record Specifications: https://dmf.ntis.gov/recordlayout.pdf 

Other data-enrichment sources 

There are many other available sources to consider depending on the focus of an investigation or the nature of the crime. Like the BSA, due to their sensitivity, not all sources are available to the public and may require special access. 

Below are representative examples of additional content sources that would integrate well with existing BSA entities and could prove useful when matches occur. 

• HHS - Office of Inspector General – List of Excluded Individuals and Entities (LEIE): https://oig.hhs.gov/exclusions/exclusions_list.asp 

• SBA - Paycheck Protection Program (PPP): https://data.sba.gov/dataset/ppp-foia 

• FINRA - Broker Check: https://brokercheck.finra.org/ 

• FBI - Terrorist Screening Dataset (TSDS): https://crsreports.congress.gov/product/pdf/IF/IF12669 

• Treasury - OFAC – Sanctions List: https://sanctionssearch.ofac.treas.gov/ 

• CIA – World Leaders: https://www.cia.gov/resources/world-leaders/ 

• Open Sanctions: https://www.opensanctions.org/datasets/ 

• DEA Fugitives: https://www.dea.gov/fugitives 

• ATF – Federal Firearms Licensees: https://www.atf.gov/firearms/listing-federal-firearms-licensees 

• IRS – Tax Exempt Organizations – 990s: https://www.irs.gov/charities-non-profits/tax-exempt-organization-search-bulk-data-downloads 

• FAA – Pilots Records Database: https://www.faa.gov/regulations_policies/pilot_records_database 

• CMS – Providers Data Catalog https://data.cms.gov/provider-data/ 

• BOP – Inmate Search: https://www.bop.gov/mobile/find_inmate/byname.jsp 

Bringing it all together 

The analysis of BSA data, when combined with advanced analytical techniques and enriched with external data sources, provides a powerful tool for combating financial crime. By identifying patterns and anomalies in financial transactions, investigators can uncover complex money-laundering schemes and fraud. 

The integration of data sources also further enhances the effectiveness of BSA analysis. By cross-referencing these datasets, analysts can identify discrepancies, inconsistencies, and potential criminal activities. 

As technology continues to evolve, so too will the sophistication of financial crimes. By leveraging advanced analytics using platforms like Quantexa, investigators can stay ahead of these evolving threats and protect the integrity of the financial system.