Abstract:
A method of assessing money-laundering risk of an individual includes gathering information regarding the individual. The gathered information includes geographic information and personal information. The method also includes determining a risk value of the geographic information and a risk value of the personal information and calculating a money-laundering risk score using the geographic information risk value and the personal information risk value.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This patent application is related in subject matter to, and incorporates by reference herein in its entirety, each of the following: 
     a U.S. patent application Ser. No. 11/548,229 entitled METHODS OF AND SYSTEMS FOR MONEY-LAUNDERING RISK ASSESSMENT, filed on the same date as this patent application; and 
     a U.S. patent application Ser. No. 11/548,235 entitled METHODS OF AND SYSTEMS FOR MONEY-LAUNDERING RISK ASSESSMENT, filed on the same date as this patent application. 
     COPYRIGHT NOTICE 
     A portion of the disclosure of this patent document contains material that is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever. 
     BACKGROUND 
     1. Technical Field 
     Disclosed embodiments relate generally, by way of example and not limitation, to systems and methods that permit a risk of money-laundering activity to be assessed. 
     2. History of Related Art 
     Money laundering, the metaphorical “cleaning of money” with regard to appearances in law, is the practice of engaging in specific financial transactions in order to conceal the identity, source, or destination of money. The term “money laundering” has traditionally been applied only to financial transactions related to organized crime. However, in recent years, the definition of money laundering has been expanded by government regulators (e.g., United States Office of the Comptroller of the Currency) to encompass any financial transaction that generates an asset or a value as the result of an illegal act. Thus, money laundering is now recognized as potentially practiced by individuals, small and large business, corrupt officials, members of organized crime (e.g., drug dealers or the Mafia) or of cults, and even corrupt states or intelligence agencies. 
     Anti-money laundering (AML) is a term mainly used in the finance and legal industries to describe legal controls that require financial institutions and other regulated entities to prevent or report money laundering activities. For example, financial institutions must perform due diligence by having proof of a customer&#39;s identity and that the use, source, and destination of funds do not involve money laundering. 
     In part due to stringent requirements of the U.S. Patriot Act, which was enacted after the Sep. 11, 2001 terrorist attacks in an effort to choke the supply of terror funds, anti-money-laundering efforts have achieved an unprecedented importance on the agendas of U.S. financial institutions. In light of the heightened importance to financial institutions of impeding money laundering, it would be advantageous to allow financial institutions to more effectively focus resources on those customers that present a higher risk for money laundering. 
     SUMMARY 
     This summary is not intended to represent each embodiment or every aspect; the following paragraphs of this summary provide representations of some embodiments as aspects thereof. 
     A method of assessing money-laundering risk of an individual includes gathering information regarding the individual. The gathered information includes geographic information and personal information. The method also includes determining a risk value of the geographic information and a risk value of the personal information and calculating a money-laundering risk score using the geographic information risk value and the personal information risk value. 
     A system for assessing money-laundering risk of an individual includes a server adapted to gather information regarding the individual. The gathered information includes geographic information and personal information. The server is also adapted to determine a risk value of the geographic information and a risk value of the personal information and calculate a money-laundering risk score using the geographic information risk value and the personal information risk value. The system also includes at least one database interoperably coupled to the server. 
     An article of manufacture for assessing money-laundering risk of an individual, the article of manufacture includes at least one computer readable medium and processor instructions contained on the at least one computer readable medium. The processor instructions are configured to be readable from the at least one computer readable medium by at least one processor and thereby cause the at least one processor to operate as to gather information regarding the individual. The gathered information includes geographic information and personal information. The processor instructions are also configured to cause the at least one processor to operate as to determine a risk value of the geographic information and a risk value of the personal information and calculate a money-laundering risk score using the geographic information risk value and the personal information risk value. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       A more complete understanding of methods and systems may be obtained by reference to the following Detailed Description when taken in conjunction with the accompanying Drawings wherein: 
         FIG. 1  is a flow diagram of a money-laundering assessment process; and 
         FIG. 2  is a block diagram of an embodiment of a server. 
     
    
    
     DETAILED DESCRIPTION 
     Methods and systems will now be described more fully with reference to the accompanying drawings in which various embodiment(s) are shown. The methods and systems may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the systems and methods to those skilled in the art. 
     Various embodiments of the methods and systems set forth herein include a mathematical algorithm that may be used as part of an AML effort. The mathematical algorithm combines specific attributes about a financial-institution customer, assigns scores and weights to the attributes, and calculates an aggregate customer money-laundering risk score. The risk score may then be used in an effort to determine an extent of due diligence required for the customer as well as a frequency of monitoring that may be applied to the customer&#39;s account activity for AML purposes. 
     Various embodiments group customer attributes into three primary risk categories: 1) people; 2) product; and 3) geography. In a typical embodiment, the aggregate risk score is created by first assigning an overall weight to each of the risk categories and adding together weighted risk measurements. Table 1 depicts illustrative individual customer attributes within each risk category: 
     
       
         
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 People 
                 Product 
                 Geography 
               
               
                   
               
             
             
               
                 Citizenship 
                 Product 
                 City 
               
               
                 Occupation 
                 Channel 
                 State 
               
               
                 Select Customer 
                   
                 Country 
               
               
                 Length of relationship 
                   
                 Zip Code 
               
               
                   
                   
                 Phone Number 
               
               
                   
               
             
          
         
       
     
     Within each risk category, the various individual customer attributes may be assigned risk scores. In various embodiments, the risk scores are multiplied by customer attribute weights to produce an overall risk category score. Certain customer attributes, which are considered key indicators requiring enhanced due diligence, are assigned trump scores. A trump score assures that a minimum risk score is assigned to a risk category whenever a key indicator attribute is present. Trump scores are typically applied to the customer attributes of citizenship, country, and area code. 
     Geography Risk Category 
     Within the Geography risk category, a plurality of attributes can be defined. In some embodiments, five key attributes are used to indicate a customer&#39;s physical location. Knowing where a customer is physically located is helpful for compliance with AML and Office of Foreign Assets Control (OFAC) laws and regulations. The OFAC is an office of the United States Department of the Treasury that administers and enforces economic and trade sanctions based on U.S. foreign policy and national security goals against targeted foreign countries, terrorists, international narcotics traffickers, and those engaged in activities related to the unapproved proliferation of weapons of mass destruction. The OFAC acts under presidential wartime and national emergency powers, as well as authority granted by specific legislation, to impose controls on transactions and freeze foreign assets under U.S. jurisdiction. The Specially Designated Nationals list provides financial and other institutions with the names of those individuals and organizations that are currently prohibited from engaging in financial transactions. 
     In a typical embodiment, government sources, such as lists maintained by the High Intensity Drug Trafficking Areas (HIDTA) and the OFAC, are used to identify geographical areas that pose a higher risk for money laundering activity. Typical geographic attributes and scoring standards include: 1) city; 2) state; 3) country; 4) zip code; and 5) phone number. 
     City Risk Attribute 
     In a typical embodiment, city risk is identified using the Office of National Drug Control Policy&#39;s Profile of Drug Indicator Reports in order to identify cities within the United States that are major drug distribution ports, drug transit areas, drug proceeds hubs, or drug trans-shipment areas. For example, outside of the U.S., foreign cities not specified by these sources may be considered a medium risk due to the lack of money laundering controls and government oversight that may exist in these locations. In various embodiments, city risk may be assigned as follows: 1) high—U.S. cities that function as major drug distribution ports, drug transit areas, drug proceed hubs, and drug trans-shipment areas selected from the High Intensity Drug Traffic Areas (HIDTA) as identified by the Office of National Drug Control Policy; 2) medium—all foreign cities; and 3) low—all other U.S. cities not specified in the high category. 
     State Risk Attribute 
     In a typical embodiment, state risk is identified using the High Intensity Financial Crime Areas (HIFCA) identified by the Financial Crimes Enforcement Network (FinCEN). This HIFCA list identifies specific states and counties that have the highest volume of Bank Secrecy Act filings and the highest levels of law enforcement responses to money laundering concerns within the U.S. Foreign states and provinces not specified by these sources are identified as medium risk due to the lack of money laundering controls and government oversight that may exist in these locations. State risk is assigned as follows: 1) high—domestic states and counties that were identified as HIFCA according to standards established by the FinCEN; 2) medium—all foreign states and provinces; and 3) low—all other states within the U.S. not specified in the high category. 
     Country Risk Attribute 
     In a typical embodiment, countries are evaluated to determine which countries present the greatest money laundering, terrorist financing, and drug trafficking risks. For example, the OFAC list of sanctioned countries and the Financial Action Task Force (FATF) Non-Cooperative Countries and Territories (NCCT) list may be used to identify high risk countries. Additionally, countries identified by the HIDTA as international drug sources and transit zones may be considered to carry high levels of money laundering risk. In various embodiments, all other foreign countries not specified by one of these sources may be considered a medium risk due to the lack of money laundering controls and government oversight that may exist in these locations. Country risk is assigned as follows: 1) high—countries that are sanctioned by the OFAC and countries and territories on the NCCT designated by the FATF and HIDTA International Drug Sources and Transit Zone Countries; 2) medium—all other foreign countries not specified in the high category; and 3) low—domestic states and U.S. territories not specified in the high category. 
     Zip Code Attribute 
     In a typical embodiment, zip codes are identified that represent the geographic areas associated with higher risk cities, counties, and states assessed in other attribute categories. For example, the data source for this attribute may be the primary address of the customer and, as a result, may include a U.S. zip code or a foreign postal code. Since city, county, and state boundaries may or may not align geographically, zip codes allow the mathematical algorithm to include more options. Zip code risk is assigned as follows: 1) high—cities, counties, and states recognized by the HIDTA or HIFCA as areas with a higher risk for money laundering and criminal activity; 2) medium—all other foreign postal codes not specified in the high category; and 3) low—domestic zip codes not specified in the high category. 
     Phone Number Attribute 
     In typical embodiment, international telephone country codes are identified for the countries identified in the country attribute as posing the highest concern for money laundering activity. In particular, the telephone country codes assigned to known OFAC, NCCT, or HIDTA countries may be considered the highest risk. Telephone country codes for all other foreign countries may be considered a medium risk due to the lack of money laundering controls and government oversight that may exist in these locations. Phone number risk is assigned as follows: 1) high—telephone country codes assigned to OFAC-sanctioned countries, HIDTA international drug sources and transit-zone countries, and countries on the NCCT list; 2) medium—all other international telephone country code numbers not listed in the high category, the U.S. Virgin Isles, and Puerto Rico; and 3) low—the U.S. and U.S. territories, excluding Puerto Rico and the U.S. Virgin Isles. 
     People Risk Category 
     Within the People risk category, attributes can be identified that indicate a potentially higher level of risk for money laundering, drug trafficking, or terrorist financing. In a typical embodiment, the mathematical algorithm uses a customer&#39;s citizenship, occupation, net worth, and length of relationship with a financial institution in an effort to assess money-laundering risk. The people risk attributes may include citizenship, occupation, net worth, and length of relationship with the financial institution. 
     Citizenship Risk Attribute 
     In a typical embodiment, the countries of citizenship are evaluated to determine which present the greatest money laundering, terrorist financing, and drug trafficking risks. Individuals who are citizens of OFAC sanctioned countries, FATF NCCT countries, and countries that have been identified as having weak anti-money laundering laws or controls are considered to present the highest risk. All other foreign citizens are typically considered a higher risk due to the lack of money laundering controls and government oversight that may exist in these locations. The citizenship risk scores are assigned as follows: 1) high—citizens of OFAC Sanctioned Countries, NCCT Countries, and HIDTA International Drug Source and Transit Zones Countries; 2) medium—citizens of foreign countries other than the OFAC, NCCT, &amp; HIDTA International Drug Source and Transit Zone Countries; and 3) low—citizens of the U.S. and U.S. territories. 
     Occupation Risk Attribute 
     In a typical embodiment, occupation codes assigned to customers are evaluated to determine which present the greatest money laundering risk. The occupations that are deemed as the highest risk for money laundering, drug trafficking, or terrorist financing are grouped into categories and assigned a risk score of high. Occupations that present a higher risk for money laundering or tax evasion are designated as a medium risk. Occupation risk is assigned as follows: 1) high—occupations or characteristics related to high net worth, cash-intensive, public office, legal/accounting/financial, art/jewelry, antiques, import/export, drug-related, military/law enforcement, and weapons/warfare; 2) medium—other cash-intensive occupations that usually involve smaller dollar amounts; and 3) low—all other occupation selections. 
     Select Customer Attribute 
     In various embodiments, a financial institution assigns select status to customers who maintain assets at the financial institution above certain thresholds or who conduct substantial levels of business with the financial institution across multiple product lines. Select customers are considered a higher risk due to the larger balances usually associated with these accounts. Select customer risk is assigned as follows: 1) high—all customers designated as Select; 2) medium—NA; and 3) low—all customers who do not qualify as a Select customer. 
     Length of Relationship Attribute 
     In various embodiments, the mathematical algorithm considers the length of relationship that the financial institution has with a customer. Extended customer relationships afford the financial institution the opportunity to perform trend analysis and gain a greater understanding of a customer&#39;s transaction behavior. Length of relationship risk is assigned as follows: 1) high—the period of 0-24 months is designated as the high-risk category since the customer&#39;s relationship is new to the financial institution and the customer does not have a lengthy transaction history with the financial institution; 2) medium—the period of 25-120 months is designated as the medium-risk category; and 3) low—the period of 121 months and above is designated as the low-risk category because these customers have longstanding relationships and transaction histories with the financial institution. 
     In addition to the above, the financial institution may perform additional customer risk analyses, for example, by screening customers against lists such as PEP lists and the OFAC Specially Designated Nationals and Blocked Persons (SDN) List. In typical embodiments, financial institutions may refuse to conduct transactions with, or provide a product or service to, any persons listed on the OFAC SDN list. 
     Product Risk Category 
     Within the Product risk category, channel and product risks associated therewith are assessed. 
     Product Risk Attribute 
     In a typical embodiment, risk criteria applied to this assessment include products and services that possess cash value, allowance for cancellation and refunds, accessibility through .com channels, easy convertibility to cash, transactions in fine jewelry, precious stones or diamonds, allowance of debit/ATM withdrawals, and fund transfer capabilities. Product risk scores are assigned as follows: 1) high—brokerage, mutual fund, and bank products that have check writing or debit cards, products that allow wire transfers, credit card and bill pay products, life insurance products with a cash value and cancellation feature, precious stone products, and products available for refund; 2) medium—brokerage, mutual fund and bank products that have a cash value but no check-writing or debit cards, certificates of deposit, and individual retirement accounts; and 3) low—all other financial products. 
     Channel Risk Attribute 
     In a typical embodiment, within the channel risk category, customers that use the internet to conduct business present the highest money-laundering risk. Although many financial institutions maintain strong authentication and controls over internet services, the inherent risks and 24/7 access to these services make the internet services more susceptible to money laundering abuse than other channels. Channel risk scores are assigned as follows: 1) high—customers who had at least one internet session during the preceding three-month period; 2) medium—NA; and 3) low—customers who had no internet sessions in the preceding three-month period. 
     Reference Tables 
     In various embodiments, risk attributes are managed via maintainable reference tables. The mathematical algorithm utilizes the maintainable reference tables as look-up tables in order to assign attribute scores and risk-category weights. Access to the maintainable reference tables is restricted to personnel with proper access credentials and changes are documented in a change control log. As a financial institution&#39;s internal and external environments change, data in the reference tables may be adjusted as desired. When the algorithm encounters a null value for any risk attribute, a default value is assigned by the algorithm for purposes of calculating the customer&#39;s risk score. In various embodiments, a customer AML risk assessment process is run periodically to take into account changes that may occur to a customer&#39;s score over time. 
     A risk-assessment mathematical algorithm may be used by financial institutions to prioritize customers for investigation for money laundering. Those having skill in the art will appreciate that the algorithm does not replace business judgment nor does it determine who has engaged in money laundering. Instead, the algorithm attempts to assess which customers are more likely than others to engage in money laundering based on a combination of people, geography, and product risk attributes. 
     Equation (1) is a mathematical-formula that sets forth a mathematical algorithm that can be used to perform a customer risk assessment in accordance with principles of the methods and systems set forth herein. 
                   Score   =     max   ⁡     [       (         W   Geo     ⁢       ∑     x   =   Geo               ⁢           ⁢     (       w   x     ⁢     v   x       )         +       W   Peo     ⁢       ∑     x   =   Peo               ⁢           ⁢     (       w   x     ⁢     v   x       )         +       W     Pr   ⁢           ⁢   od       ⁢       ∑     x   =     Pr   ⁢           ⁢   od                 ⁢           ⁢     (       w   x     ⁢     v   x       )           )     ,       i   1     ⁢     v   1       ,       i   2     ⁢     v   2       ,   …     ]               (   1   )               
In Equation (1), the following variables are used:
 
W Cat =weight ranging between 0.0-1.0 assigned to a particular category. (W Geo +W Peo +W Prod =1)
 
w x =weight assigned to attribute x within a given category. (Within each category, □w x =1.)
 
v x =value assigned to the member for attribute x. (A higher score denotes a higher level of risk. For example, assume a member lives in the U.S. Then v Country  for that member may be 1. Similarly, for a member living in a high-risk country, v Country  may be 5.)
 
i n =value indicating which attribute(s) should be considered overriding on the member&#39;s score (i.e., overriding variables) (For example, a member living in a high-risk country may have v Country =5. Regardless of the other factors, if i Country =1, the member&#39;s score would be 5.)
 
i n =0 or 1.
 
     Equation (1) can be used to calculate an average weight for Geography, People, and Product attributes. These averages may then be weighted together to come up with an average risk score. A trump function is included in Equation (1) so that customers who score high on specific values (e.g., country, citizenship, or area code) can be considered higher risk, regardless of their average scores. The Score is, therefore, the greater of the average score and the scores received on any of these trump-function values. 
     Calculation Examples 
     Assume categories and attributes weighted as follows: 
     
       
         
               
               
               
               
               
               
             
           
               
                 TABLE 2 
               
               
                   
               
               
                 Geography 
                 0.40 
                 People 
                 0.45 
                 Product 
                 0.15 
               
               
                   
               
             
             
               
                 City 
                 0.15 
                 Citizenship 
                 0.50 
                 Products 
                 0.40 
               
               
                 State 
                 0.05 
                 Occupation 
                 0.20 
                 Channel 
                 0.60 
               
               
                 Country 
                 0.30 
                 Select 
                 0.05 
                 Total 
                 1.00 
               
               
                 Zip Code 
                 0.25 
                 Relationship 
                 0.25 
               
               
                 Phone No. 
                 0.25 
                 Total 
                 1.00 
               
               
                 Total 
                 1.00 
               
               
                   
               
             
          
         
       
     
     In addition to the above, assume that country, citizenship, and area code are overriding variables (i.e., i Country =1, i Citizenship =1, i AreaCode =1, i AllOther =0). 
     Consider the following two customers: 
     
       
         
               
               
               
             
               
               
               
               
               
             
           
               
                   
                 TABLE 3 
               
             
             
               
                   
                   
               
               
                   
                 Customer 1 
                 Customer 2 
               
             
          
           
               
                 Attributes 
                 Characteristics 
                 Value (v x ) 
                 Characteristics 
                 Value (v x ) 
               
               
                   
               
               
                 City 
                 San Antonio 
                 2 
                 San Antonio 
                 2 
               
               
                 State 
                 TX 
                 1 
                 TX 
                 1 
               
               
                 Country 
                 USA 
                 1 
                 USA 
                 1 
               
               
                 Zip Code 
                 78288 
                 1 
                 78288 
                 1 
               
               
                 Phone No. 
                 210 
                 1 
                 210 
                 1 
               
               
                 Citizenship 
                 U.S. 
                 1 
                 Colombian 
                 3 
               
               
                 Occupation 
                 Actuary 
                 3 
                 Actuary 
                 3 
               
               
                 Select 
                 No 
                 1 
                 No 
                 1 
               
               
                 Customer 
               
               
                 Relationship 
                 20 years 
                 1 
                 20 years 
                 1 
               
               
                 Length 
               
               
                 Products 
                 3 
                 3 
                 3 
                 3 
               
               
                 Owned 
               
               
                 Channel 
                 .com 
                 1 
                 .com 
                 1 
               
               
                   
               
             
          
         
       
     
     For this example using Tables 2 and 3, values ranging from 1 (low) to 3 (high) are used. 
     
       
         
           
             
               
                 
                   
                     Score 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     Customer 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     1 
                   
                   = 
                     
                   ⁢ 
                   
                     max 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       { 
                       
                         [ 
                         
                           .40 
                           ⁢ 
                           
                             ( 
                             
                               
                                 .15 
                                 × 
                                 2 
                               
                               + 
                               
                                 0.5 
                                 × 
                                 1 
                               
                               + 
                               
                                 .05 
                                 × 
                                 1 
                               
                               + 
                               
                                 .30 
                                 × 
                                 1 
                               
                               + 
                             
                           
                         
                       
                     
                   
                 
               
             
             
               
                 
                   
                       
                     ⁢ 
                     
                       
                         025 
                         × 
                         1 
                       
                       + 
                       
                         .25 
                         × 
                         1 
                       
                     
                     ) 
                   
                   + 
                   
                     .45 
                     ⁢ 
                     
                       ( 
                       
                         
                           .50 
                           × 
                           1 
                         
                         + 
                         
                           .20 
                           × 
                           3 
                         
                         + 
                         
                           .05 
                           × 
                           1 
                         
                         + 
                         
                           .25 
                           × 
                           1 
                         
                       
                       ) 
                     
                   
                   + 
                 
               
             
             
               
                 
                   
                     
                         
                       ⁢ 
                       
                         .15 
                         ⁢ 
                         
                           ( 
                           
                             
                               .40 
                               × 
                               3 
                             
                             + 
                             
                               .60 
                               × 
                               1 
                             
                           
                           ) 
                         
                       
                       ] 
                     
                     , 
                     
                       1 
                       × 
                       1 
                     
                     , 
                     
                       1 
                       × 
                       1 
                     
                     , 
                     
                       0 
                       × 
                       … 
                     
                   
                   } 
                 
               
             
             
               
                 
                   = 
                     
                   ⁢ 
                   
                     max 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       { 
                       
                         
                           [ 
                           
                             
                               .40 
                               × 
                               1.15 
                             
                             + 
                             
                               .45 
                               × 
                               1.40 
                             
                             + 
                             
                               .15 
                               × 
                               1.80 
                             
                           
                           ] 
                         
                         , 
                         1 
                         , 
                         1 
                         , 
                         0 
                       
                       } 
                     
                   
                 
               
             
             
               
                 
                   = 
                     
                   ⁢ 
                   
                     max 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       { 
                       
                         1.36 
                         , 
                         1 
                         , 
                         1 
                         , 
                         0 
                       
                       } 
                     
                   
                 
               
             
             
               
                 
                   = 
                     
                   ⁢ 
                   1.36 
                 
               
             
           
         
       
       
         
           
             
               
                 
                   
                     Score 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     Customer 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     2 
                   
                   = 
                     
                   ⁢ 
                   
                     max 
                     ⁢ 
                     
                       { 
                       
                         [ 
                         
                           .40 
                           ⁢ 
                           
                             ( 
                             
                               
                                 .15 
                                 × 
                                 2 
                               
                               + 
                               
                                 .05 
                                 × 
                                 1 
                               
                               + 
                               
                                 .30 
                                 × 
                                 1 
                               
                               + 
                               
                                 .25 
                                 × 
                                 1 
                               
                               + 
                             
                           
                         
                       
                     
                   
                 
               
             
             
               
                 
                   
                       
                     ⁢ 
                     
                       .25 
                       × 
                       1 
                     
                     ) 
                   
                   + 
                   
                     .45 
                     ⁢ 
                     
                       ( 
                       
                         
                           .50 
                           × 
                           3 
                         
                         + 
                         
                           .20 
                           × 
                           3 
                         
                         + 
                         
                           .05 
                           × 
                           1 
                         
                         + 
                         
                           .25 
                           × 
                           1 
                         
                       
                       ) 
                     
                   
                   + 
                 
               
             
             
               
                 
                   
                     
                         
                       ⁢ 
                       
                         .15 
                         ⁢ 
                         
                           ( 
                           
                             
                               .40 
                               × 
                               3 
                             
                             + 
                             
                               .60 
                               × 
                               1 
                             
                           
                           ) 
                         
                       
                       ] 
                     
                     , 
                     
                       1 
                       × 
                       1 
                     
                     , 
                     
                       1 
                       × 
                       3 
                     
                     , 
                     
                       0 
                       × 
                       … 
                     
                   
                   } 
                 
               
             
             
               
                 
                   = 
                     
                   ⁢ 
                   
                     max 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       { 
                       
                         
                           [ 
                           
                             
                               .40 
                               × 
                               1.15 
                             
                             + 
                             
                               .45 
                               × 
                               2.40 
                             
                             + 
                             
                               .15 
                               × 
                               1.80 
                             
                           
                           ] 
                         
                         , 
                         1 
                         , 
                         3 
                         , 
                         0 
                       
                       } 
                     
                   
                 
               
             
             
               
                 
                   = 
                     
                   ⁢ 
                   
                     max 
                     ⁢ 
                     
                         
                     
                     ⁢ 
                     
                       { 
                       
                         1.81 
                         , 
                         1 
                         , 
                         3 
                         , 
                         0 
                       
                       } 
                     
                   
                 
               
             
             
               
                 
                   = 
                     
                   ⁢ 
                   3 
                 
               
             
           
         
       
     
     It will be apparent to those having skill in the art that the fact that Customer 2 is a Colombian citizen results in Customer 2 having a score of 3, notwithstanding the fact that the average score resulting from Equation (1) without taking the trump function into consideration would have been 1.81. In contrast, Customer 1 had no trump function values that were great enough to exceed the average score resulting from application of Equation (1) to the characteristics of Customer 1; therefore, the risk score of Customer 1 remains 1.36. 
     Referring now to the FIGURES,  FIG. 1  is a flow diagram of a money-laundering assessment process that can employ Equation (1) above. A process flow  100  begins with a customer data extract  102  from a customer database  105 . A single customer database (i.e., the customer database  105 ) is illustrated within the process flow  100 ; however, those having skill in the art will appreciate that customer data may be extracted from a variety of sources without departing from principles of the methods and systems as set forth herein. 
     Following the customer data extract  102 , the extracted customer data is provided to a process  104 . The process  104  generally depicts the operations involved in a typical implementation of Equation (1). In addition to the input of the customer data extract  102  to the process  104 , flat files  106  are also input to the process  104 . The flat files  106  are created from converted reference data  110 . The converted reference data  110  is obtained from a relational database  108 . The relational database  108  houses data regarding the various risk attributes and their assigned values. For example, in the relational database  108 , data may be maintained regarding possible values of citizenship, occupation, select customer, length of relationship, product, channel, city, state, country, zip code, and phone number. In a typical embodiment, every U.S. zip code and a risk value associated therewith is stored in the relational database  108 . The relational database  108  is, in a typical embodiment, used as a look-up table by the process  104 . 
     It will be apparent to those having skill in the art that values for the various risk attributes can be readily updated as desired. As noted above, the process  104  receives as an input the customer data extract  102 . The customer data extract  102  is illustrated within the process  104  as segregated into geographic data  112 , people data  114 , and product data  116 . Geographic scoring  118  has as inputs the geographic data  112  and applicable value data from the flat files  106 . In similar fashion, people scoring  112  has as inputs the people data  114  and applicable value data of the flat files  106 , while product scoring  122  has as inputs the product data  116  and applicable value data of the flat files  106 . The geographic scoring  118 , the people scoring  120 , and the product scoring  122  each represent processes by which subscores of overall scores of individuals are calculated. 
     The geographic scoring  118 , the people scoring  120 , and the product scoring  122  serve as inputs to geographic data with scores  124 , people data with scores  126 , and product data with scores  128 , respectively. The geographic data with scores  124 , people data with scores  126 , and product data with scores  128  combine to create overall scoring  130 . The overall scoring  130  is calculated using as inputs the geographic data with scores  124 , the people data with scores  126 , and the product data with scores  128 . Those having skill in the art will appreciate that, when the overall scoring  130  employs Equation (1), one or more trump functions using an overriding variable may be utilized as dictated by design considerations. 
     The overall scoring  130  is output to detail files  132 . The detail files  132  are utilized to create extract report data  134 . From the extract report data  134 , report detail files  136  are created. From the report detail files  136 , base reports are formatted, which formatting results in reports  140 . 
       FIG. 2  is a block diagram of an embodiment of a server that may be used to implement various processes as set forth herein. In the implementation shown, a server  200  may include a bus  218  or other communication mechanism for communicating information and a processor  202  coupled to the bus  218  for processing information. The server  200  also includes a main memory  204 , such as a random access memory (RAM) or other dynamic storage device, coupled to the bus  218  for storing computer readable instructions to be executed by the processor  202 . 
     The main memory  204  also may be used for storing temporary variables or other intermediate information during execution of the instructions to be executed by the processor  202 . The server  200  further includes a read only memory (ROM)  206  or other static storage device coupled to the bus  218  for storing static information and instructions for the processor  202 . A computer readable storage device  208 , such as a magnetic disk or optical disk, is coupled to the bus  218  for storing information and instructions for the processor  202 . 
     The server  200  may be coupled via the bus  218  to a display  210 , such as a cathode ray tube (CRT), for displaying information to a user. An input device  212 , including, for example, alphanumeric and other keys, is coupled to the bus  218  for communicating information and command selections to the processor  202 . Another type of user input device is a cursor control  214 , such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor  202  and for controlling cursor movement on the display  210 . The cursor control  214  typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow the device to specify positions in a plane. 
     The term “computer readable instructions” as used above refers to any instructions that may be performed by the processor  202  and/or other component of the server  200 . Similarly, the term “computer readable medium” refers to any storage medium that may be used to store the computer readable instructions. Such a medium may take many forms, including, but not limited to, non volatile media, volatile media, and transmission media. Non volatile media include, for example, optical or magnetic disks, such as the storage device  208 . Volatile media include dynamic memory, such as the main memory  204 . Transmission media include coaxial cables, copper wire and fiber optics, including wires of the bus  218 . Transmission can take the form of acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH EPROM, any other memory chip or cartridge, a carrier wave, or any other medium from which a computer can read. 
     Various forms of the computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor  202  for execution. For example, the instructions may initially be borne on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the server  200  can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to the bus  218  can receive the data carried in the infrared signal and place the data on the bus  218 . The bus  218  carries the data to the main memory  204 , from which the processor  202  retrieves and executes the instructions. The instructions received by the main memory  204  may optionally be stored on the storage device  208  either before or after execution by the processor  202 . 
     The server  200  may also include a communication interface  216  coupled to the bus  218 . The communication interface  216  provides a two way data communication coupling between the server  200  and, for example, the database  105 . For example, the communication interface  216  may be an integrated services digital network (ISDN) card or a modem used to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface  216  may be a local area network (LAN) card used to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, the communication interface  216  sends and receives electrical, electromagnetic, optical, or other signals that carry digital data streams representing various types of information. 
     The storage device  208  can further include instructions for carrying out various processes for gathering information about individuals and accessing reference data for purposes of performing an anti-money laundering risk assessment described herein when executed by the processor  202 . The storage device  208  can further include a database for storing customer attributes and reference data. 
     The previous description is of embodiment(s) for implementing the methods and systems described herein, and the scope should not be limited by this description. The scope is instead defined by the following claims.