Database for check risk decisions populated with check activity data from banks of first deposit

• US 7,383,227 B2
• Filed: 05/14/2002
• Issued: 06/03/2008
• Est. Priority Date: 05/14/2002
• Status: Active Grant

• Current Assignee: Early Warning Services, LLC
• Original Assignee: Early Warning Services, LLC
• Inventors: Spooner, Larry W., Weinflash, Laura E.
• Primary Examiner(s): Trammell; James P.
• Assistant Examiner(s): Swartz; Jamie H
• Application Number:
  US10/144,740
• Publication Number:
  US 20030217003A1
• Time in Patent Office: 2,212 Days
• Field of Search: 235/379, 235/7.R, 705/38, 705/39, 705/35, 705/1, 705/33, 705/45, 382/137
• US Class Current: 705/45
• CPC Class Codes: G06Q 20/042 characterized in that the payment protocol involves at least one cheque
• CPC Class Codes: G06Q 20/108 Remote banking, e.g. home banking
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/128 Check-book balancing, updating or printing arrangements

Abstract

A plurality of banks of first deposit provide checking account activity data for both transit items (checks received for deposit that need to be cleared) and incoming returns (bounced checks) to a statistical model which determines from the data the likelihood that a check from a specific checking account will be returned. This data is used to populate a database of checking accounts to be used for making check risk decisions, such as check hold policy decisions, check acceptance decisions, and open to buy decisions.

Claims

1. A computer implemented method of populating a database of checking account with statistical data regarding the likelihood that a check from a specific checking account will be returned, the method comprising:

(a) receiving checking account activity data directly from a plurality of banks of first deposit, the checking account activity data including;

(i) transit items, and (ii) incoming returns;

(b) analyzing the checking account activity data received from the banks of first deposit using a statistical model to determine the likelihood that a check from a specific checking account will be returned, wherein the statistical model utilizes a plurality of weighting factors applied to the transit items and the incoming returns in order to create a score;

(c) populating a database with checking account data received from the banks of first deposit including;

(i) checking account numbers, and (ii) likelihood that a check from a specific checking account number will be returned; and

(d) periodically repeating steps (a) and (b) with new checking account activity data and updating the database of checking account data with the new checking account activity data;

wherein step (b) further comprises determining if there is enough checking account activity data for a specific checking account number to determine the likelihood that a check from a specific checking account will be returned, and if not, then flagging the checking account data for that specific checking account number and step (d) further comprises periodically reviewing the flagged checking account numbers and populating the database with checking account data for any flagged checking account numbers which are determined to have enough checking account activity data to make a determination of the likelihood that a check from a specific checking account will be returned.

2. The method of claim 1 wherein step (b) further comprises determining if there is enough checking account activity data for a specific checking account number to determine the likelihood that a check from a specific checking account will be returned, and if not, then flagging the checking account data for that specific checking account number and populating the database with only the checking account number for that specific checking account, and step (d) further comprises periodically reviewing the flagged checking account numbers and populating the database with the likelihood data for any flagged checking account numbers which are determined to have enough checking account activity data to make a determination of the likelihood that a check from a specific checking account will be returned.

3. The method of claim 1 wherein step (a) further comprises receiving the checking account activity data solely from a plurality of banks of first deposit.

4. A computer implemented method of populating a database of checking account with statistical data regarding the likelihood that a check from a specific checking account will be returned, the method comprising:

(a) receiving checking account activity data from a single entity that receives such data from a plurality of banks of first deposit, the checking account activity data including;

(i) transit items, and (ii) incoming returns;

(b) analyzing the checking account activity data received from the banks of first deposit using a statistical model to determine the likelihood that a check from a specific checking account will be returned, wherein the statistical model utilizes a plurality of weighting factors applied to the transit items and the incoming returns in order to create a score;

(c) populating a database with checking account data received from the banks of first deposit including;

(i) checking account numbers, and (ii) the score which indicates the likelihood that a check from a specific checking account number will be returned; and

(d) periodically repeating steps (a) and (b) with new checking account activity data and updating the database of checking account data with the new checking account activity data.

5. The method of claim 4 wherein the single entity is the Federal Reserve.

6. The method of claim 4 wherein the single entity is a clearinghouse.

7. The method of claim 4 wherein the single entity is a check processor.

8. A computer implemented method of populating a database of checking account with statistical data regarding the likelihood that a check from a specific checking account will be returned, the method comprising:

(a) receiving checking account activity data directly from a plurality of banks of first deposit, the checking account activity data including;

(i) transit items, and (ii) incoming returns;

(b) filtering out at least a majority of a participant checking account activity data in order to leave non-participant checking account activity data;

(c) analyzing the non-participant checking account activity data received from the banks of first deposit using a statistical model to determine the likelihood that a check from a specific checking account will be returned, wherein the statistical model utilizes a plurality of weighting factors applied to the transit items and the incoming returns in order to create a score;

(d) populating a database with non-participant checking account data received from the banks of first deposit including;

(i) checking account numbers, and (ii) likelihood that a check from a specific checking account number will be returned; and

(e) periodically repeating steps (a), (b), and (c) with new checking account activity data and updating the database of checking account data with the new checking account activity data.

9. The method of claim 8 wherein step (b) further comprises determining if there is enough non-participant checking account activity data for a specific checking account number to determine the likelihood that a check from a specific checking account will be returned, and if not, then flagging the checking account data for that specific checking account number and step (e) further comprises periodically reviewing the flagged checking account numbers and populating the database with checking account data for any flagged checking account numbers which are determined to have enough checking account activity data to make a determination of the likelihood that a check from a specific checking account will be returned.