TRANSACTIONAL MONITORING SYSTEM

• US 20140058914A1
• Filed: 08/27/2012
• Published: 02/27/2014
• Est. Priority Date: 08/27/2012
• Status: Active Grant

• Current Assignee: Apex Techlink Incorporated
• Original Assignee: Yuh-Shen Song
• Inventors: Song, Yuh-shen, Lew, Catherine, Song, Alexander, Song, Victoria
• Granted Patent: US 10,163,158 B2
• Time in Patent Office: Days
• US Class Current: 705/35
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A computer system conducts transactional monitoring to detect different types of possible cases in order to prevent financial crimes and assist businesses to comply with different types of laws and regulations. The computer system derives a total risk score for each of a group of entities based on risk factors. Each of the risk factors is assigned a risk score. The computer system also detects an entity when the total risk score of the detected entity differs from a reference derived from total risk scores of the group of entities by a pre-determined margin. The computer system also assists a user to identify at least one transaction that has caused the detected entity to have a total risk score that differs from the reference derived from the total risk scores of the group of entities.

Claims

1. A computer system for assisting businesses in complying with anti-money laundering laws and/or regulations, comprising:

a memory device; and

at least one processor coupled to the memory device, the at least one processor being configured;

to derive a total risk score for each of a plurality of customers of a business based on a plurality of risk scores, each of the plurality of risk scores being associated with a risk factor;

to detect a customer when the total risk score of the detected customer differs from a reference value derived from total risk scores of the plurality of customers by a pre-determined margin; and

to assist a user to investigate the detected customer with respect to money laundering activity.

2. The computer system of claim 1, in which the at least one processor is further configured:

to assist the user to report the detected customer.

3. The computer system of claim 1 in which the risk factors include, at least, an industry category of the customer, business type of the customer, geographical area of the customer, country of the address of the customer, nature of the business of the customer, product types of the business, services types of the business, structure of the business, profession of the customer, nationality, historical records, type of the transaction conducted, balance of the accounts, funds inflow, funds outflow, transactional pattern, number of transactions, amount of transactions, transactional volume, transactional frequency, transactional derivative, location of the transaction, time of the transaction, country of the transaction, sender of a money transfer transaction, location of the sender, country of the sender, nature of the sender, recipient of a money transfer transaction, location of the recipient, country of the recipient, nature of the recipient, relationships, social status, political exposure, and/or historical transactions.

4. The computer system of claim 1 in which the at least one processor is further configured to adjust at least one of the risk factors partially based on a size of operations when the customer is a business.

5. The computer system of claim 1 in which the total risk score is obtained by a mathematical transformation of the plurality of risk scores.

6. The computer system of claim 5 in which the mathematical transformation is a summation of the plurality of risk scores.

7. The computer system of claim 1 in which the pre-determined margin is determined partially based on a statistical approach and/or human judgment.

8. The computer system of claim 7 in which the statistical approach is based on a standard deviation of the total risk scores of the plurality of customers.

9. The computer system of claim 2 in which the detected customer is reported to a government organization.

10. The computer system of claim 9 in which the government organization is the Financial Crimes Enforcement Network in the USA.

11. The method of claim 1, in which the reference value derived from the total risk scores of the plurality of customers comprise an average, a mean, a median, weighted average or a statistical value related to the total risk scores of the plurality of customers.

12. A computerized method for assisting businesses in complying with anti-money laundering laws and/or regulations, comprising:

identifying a plurality of customers of a business having at least one common risk factor;

deriving a total risk score for each of the plurality of customers based on a plurality of risk scores, each of the plurality of risk scores being associated with a risk factor;

detecting, by a computer system, a customer when the total risk score of the detected customer differs from a reference value derived from total risk scores of the plurality of customers by a pre-determined margin; and

assisting a user to investigate the detected customer with respect to money laundering activity.