email addresses , account numbers , crypto wallets , IP addresses , etc .) across banking systems . In LE terms , it means the same thing : linking elements that exist in multiple SARs in order to create a nexus of SARs that otherwise would not have been connected together . This nexus could potentially extend by many previously unknown degrees of separation , allowing for more efficient prosecution of complex crime rings .
▪ NLP : This refers to techniques to break language into pieces , then working to understand relationships between the pieces and how they work together to create meaning . In banking terms , this means using standardized language to refer to crime types and activity ( often found in FinCEN documents ) in order to make it easier for external consumers ( LE , etc .) to connect reports . Standardized verbiage will allow for the creation of typology mapping outside of the traditional checkboxes that currently exist on the SAR form . In LE terms , this means using automation to ascertain that a report has no known source of funds , or that victim demographic information is present , or that a 314 ( a ) or 314 ( b ) reference was made based on data extracted from a pattern in the narrative . It can also be used to identify risk factors in the narrative , such as high-risk countries , PEPs or fentanyl trafficking .
FI SAR optimization
So , how do FIs write SARs that allow for these same technologies to rank the highest priority reports for LE ? The simple answer is to create a template system for each crime type to ensure that each reportable typology has the same information reported each time , provided the information is available to the FI . This would be an excellent way for FinCEN to modernize SAR filing guidance ― stating specifics that should exist for each report typology , allowing for more objective audit / examination standards , and better ingestion and review by LE .
For example , in a SAR reporting elder financial exploitation perpetrated through a romance scam that utilized virtual currency as a mode of transfer , the following data points should be included :
▪ Subject information if there is any .
▪ Victim information . This should be clearly outlined in the narrative . Include the victim ’ s full name , SSN , DOB , address , phone number and email address . Ensure that the victim ’ s internal bank account ( s ) are included .
▪ External bank accounts where funds were sent ( bank name , routing number and account number ).
▪ Virtual currency . Include any information about wallet address or other cryptorelated identifiers . If a Bitcoin ATM was used , include the address .
▪ Methods of communication . Include any phone numbers used to call the victim , any email addresses used and any social media platforms used . Social media handles for the victim and perpetrator should be identified . Include all IP addresses used to access the victim ’ s online banking or mobile banking applications .
Each of these data points can be extracted from the SAR by LE applications and used to resolve entities that may assist in identifying a previously unknown subject and / or may create a network diagram connecting this SAR / case to others within a previously unknown nexus . Identifying both subjects and related cases / criminal networks more quickly will raise the value of the SAR in the eyes of LE .
Conclusion
SARs serve as both the initiation and confirmation of many LE investigations . In order to make the process more efficient for both the producers and consumers of SARs , steps should be taken to standardize report narrative content to improve the creation of reports and their ultimate use . Public-private collaboration is imperative to continued success in the fight against financial crime . SAR optimization will improve prosecution of money laundering and terrorist financing ― a rarity in which we all win .
Jane Bell , CAMS , senior industry consultant , SAS , North Carolina , USA , jane . bell @ sas . com , 1
“ SAR Optimization : Seeing the Forest Beyond the Trees ”, ACAMS Today December 2024-February 2025 , https :// www . acamstoday . org / sar-optimization-seeing-the-forest-beyond-the-trees /
2
“ SAR Optimization : An LE Perspective ,” ACAMS Today , December 19 , 2024 , https :// www . acamstoday . org / sar-optimization-an-le-perspective /
3
“ Suspicious Activity Report Statistics ( SAR Stats ),” Financial Crimes Enforcement Network , https :// www . fincen . gov / reports / sar-stats
4
“ Proposed Rule to Strengthen and Modernize Financial Institution AML / CFT Programs ,” Federal Register , July 3 , 2024 , https :// www . federalregister . gov / documents / 2024 / 07 / 03 / 2024-14414 / anti-money-laundering-and-countering-the-financing-of-terrorism-programs
5
“ BSA / AML Examination Manual ,” Federal Financial Institutions Examination Council , https :// bsaaml . ffiec . gov / manual
6
“ Anti-Money Laundering and Countering the Financing of Terrorism National Priorities ,” Financial Crimes Enforcement Network , June 30 , 2021 , https :// www . fincen . gov / sites / default / files / shared / AML _ CFT % 20Priorities % 20 ( June % 2030 % 2C % 202021 ). pdf
7
“ Alerts / Advisories / Notices / Bulletins / Fact Sheets ,” Financial Crimes Enforcement Network , https :// www . fincen . gov / resources / advisoriesbulletinsfact-sheets
8
“ SAR Optimization : An LE Perspective ,” ACAMS Today , December 19 , 2024 , https :// www . acamstoday . org / sar-optimization-an-le-perspective /
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