COMPLIANCE
resources to the AML task , which will increase the likelihood for better [ LE ] outcomes .” This will cut down on the dreaded “ defensive SAR ” filing ― a report that is filed that is perceived to “ check the box ” to satisfy a regulatory or audit requirement but has little to no value for LE and where the activity in question represents little to no risk to the FI . The end result will be fewer ― but better ― SARs , which will stem the deluge of information that is flooding LE and negatively impacting their investigative efficiency .
SAR narrative content
Once FIs have homed in on the SARs that should be filed , the next logical step is improving what is included in the reports ( as well as what old habits should be left in the past ). Based on numerous information gathering sessions with LE , the SAR content they find most useful in narratives includes :
▪ Previous SAR filing document control numbers , prior arrest or conviction records , and negative news search results for subjects ;
▪ Counterparty information and associated negative news search results ;
▪ Beneficial ownership information ;
▪ Ownership structure and incorporation details for entities ;
▪ Shell company information ;
▪ Victim information ( demographic information such as name , Social Security number [ SSN ], date of birth [ DOB ], address , relationship to the suspect );
▪ External bank account information ( bank name , routing number , account number );
▪ Wiring instructions ;
▪ IP addresses ;
▪ Source of funds ( if known , and if not , a statement to that effect );
▪ Politically exposed persons ( PEPs ); and
▪ High-risk areas ( countries , or jurisdictions like High Intensity Drug Trafficking Areas , High Intensity Financial Crime Areas and Geographic Targeting Orders ).
On the flip side , SAR narrative content that is not useful or necessary includes :
▪ Restating subject information already found in Part I ;
▪ Restating branch transaction address information already listed in Part III ;
▪ Typing in all caps ; and
▪ Including lengthy strings of transaction information that can instead be sent with the report as a CSV attachment .
Of particular interest to SAR consumers in LE is the concept of “ BLUF ”: bottom line up front . This helps the reader to quickly determine if the SAR they are reviewing is of interest and falls within their charging jurisdiction . Neglecting to include a brief summary at the onset may result in an otherwise outstanding SAR being passed over by LE due to failure to engage early in the report .
Technological advances in SAR consumption
“ Optimized SARs ” from FIs ― those that are more organized and contain all of the critical data points ― will result in better , more timely investigations from LE . As outlined in the second article in this series , 8 SAR optimization on the LE side is realized when entity resolution , network generation and natural language processing ( NLP ) come together to form risk-based scoring models that aid in the prioritization of SARs for LE to investigate . These methods enable LE to be able to process SARs in totality , rather than report by report .
We must understand what these concepts mean in order to know why they matter and how they can be used .
▪ Entity resolution : This refers to linking elements in a single or multiple files such that records referring to the same object are treated as a single record . Records are matched based on the information that they have in common . In banking terms , this means resolving disparate customer information in various systems ( deposit , loan , card , etc .) to one “ master record ” that points all systems ’ information to the same person or entity . In LE terms , this means resolving disparate subject information across multiple SARs to one master record that ties that same subject to all of their SARs , regardless of what institution filed them .
▪ Network generation : This refers to how resolved entities and inferred relationships generate networks that can be tuned to prioritize relevant links . In banking terms , this means connecting commonalities in customer records ( postal addresses , phone numbers ,
36 acamstoday . org