SYSTEM AND METHOD FOR PREDICTING A MEASURE OF ANOMALOUSNESS AND SIMILARITY OF RECORDS IN RELATION TO A SET OF REFERENCE RECORDS

• US 20100257092A1
• Filed: 06/17/2008
• Published: 10/07/2010
• Est. Priority Date: 07/18/2007
• Status: Abandoned Application

• Current Assignee: Ori Einhorn
• Original Assignee: Ori Einhorn
• Inventors: Einhorn, Ori
• Application Number:
  US12/664,417
• Publication Number:
  US 20100257092A1
• Time in Patent Office: Days
• US Class Current: 705/38
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06Q 20/40 Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof

Abstract

The present invention presents system and method for predicting a measure of anomalousness and similarity of input records in relation to a set of reference records, both input records and reference records comprising set of parameters.

Claims

1. A method for predicting a measure of anomalousness of candidate record sequences of transactions (CRS) by use of a set of reference record sequences (RRS), each of said RRS being characterized by a measure of anomalousness A_R, said method comprising steps of:

a. storing said RRS in data storage means, and receiving said CRS in real time by use of data storage and transmission means;

b. projecting said RRS and CRS into a multi-dimensional space;

c. computing a distance d from each said RRS to said CRS in said multidimensional space;

d. selecting the subset of RRS falling within a predetermined distance d of said CRS;

e. computing a measure A_C of anomalousness of said CRS based on the measure A_R of the anomalousness of said subset of RRS;

f. outputting said measure of anomalousness A_C by means of an output device;

whereby sequences of candidate records are compared to sequences of reference records to arrive at measures of anomalousness of said candidate records.

2. The method of claim 1 where said measure A_C is a function ƒ

of said A_R and said distance d.

3. The method of claim 2 where said function ƒ

is a monotonically decreasing function of said distance d and a monotonically increasing function of said A_R.

4. The method of claim 1 where said measures of anomalousness are selected from the group consisting of:

Boolean numbers, real numbers, vectors.

5. The method of claim 1 wherein said distance d is computed by means of a distance function selected from a group consisting of:

a monotonic function of said candidate records'"'"' Euclidean distances from said reference records;

a threshold function of said candidate records'"'"' Euclidean distances from said reference records;

a function of non-Euclidean distance between said candidate records and said reference records.

6. The method of claim 1 further adapted to repeat said computation of anomalousness for a group PCRS of CRS and further computing a group anomalousness selected from the set consisting of:

the weighted anomalousness of said PCRS;

the maximum anomalousness of said PCRS.

7. The method of claim 1 further providing a measure of prediction strength based on the size of said subset of RRS falling within said predetermined distance d of said CRS.

8. The method of claim 7 further varying said distance d to control said prediction strength.

9. The method of claim 1 adapted for allowing or denying transactions based on said measure of anomalousness A_C.

13. The system of claim 1 where said measures of anomalousness are selected from the group consisting of:

Boolean numbers, real numbers, vectors.

10. A system for predicting a measure of anomalousness of candidate record sequences (CRS) including current and previous transactions of a given entity, in relation to a set of reference record sequences (RRS), each of said RRS being characterized b a measure of anomalousness A_R, said system comprising:

a. data storage and receipt means adapted to store said RRS and to receive said CRS in real time;

b. projection analyzer connected to said data storage means adapted to project said RRS and CRS into a multi-dimensional space;

c. distance computing means adapted to compute a distance d from said RRS to said CRS in said multidimensional space;

d. selection means adapted to select the subset of RRS falling within a predetermined distance d of said CRS;

e. anomaly computing means adapted to compute a measure A_C of anomalousness of said CRS based on the measure A_R of the anomalousness of said subset of RRS;

f. an output device connected to said anomaly computing means and operative to output said measure of anomalousness A_C;

wherein sequences of candidate records are compared to sequences of reference records to arrive at a measure of anomalousness of said candidate records.

11. The system of claim 10 where said measure A_C is a function ƒ

of said A_R and said distance d.

12. The system of claim 11 where said function ƒ

is a monotonically decreasing function of said distance d and a monotonically increasing function of said A_R.

14. The system of claim 10 wherein said distance d is computed by means of a distance function selected from a group consisting of:

a monotonic function of said candidate records'"'"' Euclidean distances from said reference records;

a threshold function of said candidate records'"'"' Euclidean distances from said reference records;

a function of non-Euclidean distance between said candidate records and said reference records.

15. The system of claim 10 further provided with computing means adapted to repeat said computation of anomalousness for a group PCRS of CRS including said current transaction and some subset of previous transactions, further provided with group anomalousness computation means adapted to compute a group anomalousness selected from the group consisting of:

the weighted anomalousness of said PCRS;

the maximum anomalousness of said PCRS.

16. The system of claim 10 further adapted to provide a measure of prediction strength based on the number of said RRS falling within said predetermined distance d of said CRS.

17. The system of claim 16 further adapted to vary said distance d to control said prediction strength.

18. The system of claim 10 adapted for allowing or denying transactions based on said measure of anomalousness A_C.