Prioritizing and Targeting Suspicious Transaction Reports FIU Approach

Prioritizing and Targeting Suspicious Transaction Reports FIU Approach 1

STR Targeting Process Select Targets No Targets Identified Targets Rejected 2

Data mining – information enrichment Operational – tactical casework Statistical – trends, deviations, inventory Strategic – techniques, industry, financial instrument, offense, occupation, geography etc. Types of Analysis 3

Most STR activities contain insufficient details to serve as grounds for criminal suspicion. Requires cross-checking of an STR with information in the FIU’s STR database and threshold-based reports databases. Enrichment from other available data sources may support need for further investigation. STR Targeting 4

STR Structural Requirements The report must be structured to enable automated filtering and evaluation. 5

STR Parts include information on the: Reporting Institution Filing the STR Physical Person Conducting the Transaction Person (Physical or Legal) on Whose Behalf the Transaction is Conducted Transaction Account Narrative STR Structure 6

STR Elements (Fields) 7

STR Elements (Fields) Continued 8

STR Elements (Fields) Continued 9

STR Elements (Fields) Continued 10

STR Elements (Fields) Continued 11

FIU Information Flow DISSEMINATION REPORTING ANALYSIS Banks Securities Dealers Insurers Casinos Accountants Lawyers Other Persons Suspicious Transaction Reports Cash Transaction Reports Electronic Transfer Reports Cross-Border Cash Transaction Reports Prosecutor’s Office Law Enforcement Agencies Other FIUs FIU Database Gov’t Databases Data From Other FIUs Other Data Financial Intelligence Reporting Entities Reports FIU Processes Customers 12

Each received report must be audited by FIU. ID data format must be validated and checked against government sources if possible. Erroneous or incomplete reports must be returned for correction to the reporting institution’s compliance officer. Audit and Validation of STRs 13

FIUs should establish a screening process when STRs are uploaded into FIU database. Compare new STR data with existing STRs (and other threshold based reports) in FIU’s database for preliminary links. Basic rules should be established for: Sharing the STR data with customer agencies, Conducting analysis on STR, Retaining STR for further review, or Archiving STR in FIU database. Preliminary Screening of Individual STRs 14

Scoring of Face Value of Data (Keywords, search of structured fields in reports) STR Score Scoring Tool for relationship of persons listed on STR. Automated Model for STR Screening Scoring Engine FIU policies Compilation of scores Input of statistical tools, work priorities and analyst input. Maintenance of Scoring Rules Topics Formulas Rule definition 15

Basic Rules for Screening Incoming STR 16

STR review based on “face value” [e.g., text analysis, classification of activity by reporting institution]. Identification of persons and data enrichment for relationship analysis. Transaction analysis and comparison with relationships identified. Implication of patterns indicative of money laundering or terrorist financing. Indication of a “cluster” pattern based on persons and transactions identified. Screening STRs With Software Tools 17

A “cluster” is defined as a group of physical and legal persons (real or fictitious) identified in STRs or associated by inference from shared attributes* through further analysis. *financial activity, address, phone, account, business activity, recurrent sequence of events on a time line, etc. “CLUSTER” 18

Database of Accounts and Linked Physical Persons = account = physical person 19

First Level Links 20

3 2 1 5 4 Second Level Links: Identification of Cluster 21

3 2 5 4 1 Cluster Identification 22

7 3 2 1 5 6 4 Cluster Building - Third Level Links 23

3 2 1 5 4 7 6 24

3 2 1 5 4 7 $1,000,000 6 $1,000,000 25

Ms. Pink Mr. Green Measuring Proximity of Mr. Blue and Ms. Pink Weighing Relationships ABC INC. Mr. Blue 1 3 2 26

Married Shareholder Mr. Blue CEO Siblings Shareholder Ms. Pink Mr. Green ABC INC. 27

1 2 4 3 3 4 5 6 7 28

1 Mr. Blue is Ms. Pink’s Spouse Proximity (1) = 0.9 If Mr. Blue is involved in suspicious activity, the probability that Ms. Pink is involved equals 90%. Mr. Blue Ms. Pink 29 Weighing Relationship 1

3 Ms. Pink owns ABC INC equals 10%. Mr. Blue is a Shareholder in ABC INC equals 90%. Ms. Pink Mr. Blue Proximity (2) 0.1 x 0.9 = 0.09 30 ABC INC. 4 Weighing Relationship 2

CEO 0.3 SHAREHOLDER = 0.9 Mr. Blue Ms. Pink Proximity (3) 0.2 x 0.3 x 0.9 = .054 31 Mr. Green ABC INC. 7 SIBLING 0.2 4 5 Weighing Relationship 3

Weighted Proximity (1)(2)(3) = 1- (1-0.9)  (1-0.09)  (1-0.054) = 0.999946 Mr. Blue Ms. Pink Assuming Mr. Blue is conducting suspicious activity, the chance that Ms. Pink is involved is 99.9946% 32 Weighing Relationships 1, 2 and 3 Together

Conclusion STR Form and Structure Screening and Prioritizing Incoming STRs Cluster Development Weighing Relationships Using Simple Algorithm Questions? 33