US 2013/0232045 A1 · Automatic Detection Of Fraud And Error Using A Vector-Cluster Model

Abstract

One or more computers retrieve records of transactions to be analyzed together. Each record identifies a date of a transaction, an amount of the transaction, a person associated with the transaction, and a category into which the transaction is classified. The one or more computers automatically prepare in computer memory, a set of tuples (also called “vectors”) corresponding to a set of persons identified in the retrieved records. Each tuple corresponds to one person, and each tuple includes at least one number representing a count within each category, of transactions classified therein, e.g. total number of cash transactions in category X. Then, the one or more computers automatically identify a subset of outliers, e.g. by grouping the tuples into clusters using k-means clustering, followed by marking in memory an indication of inappropriateness of any transaction that had been included in the count of a tuple now identified to be outlier.

Claims21

Dependent claims

A method of processing transactions by using one or more computers, the method comprising:

the one or more computers retrieving a plurality of records of transactions to be analyzed together;
wherein each record in said plurality identifies at least an amount of a transaction, a person associated with the transaction, and a type of the transaction;
the one or more computers automatically preparing in computer memory, a set of tuples for a corresponding set of persons identified in the plurality of records;
wherein a tuple corresponding to said person comprises a group of numbers which are derived from transactions associated with said person, based on types of the transactions;
the one or more computers automatically identifying a subset of said tuples, by analysis of said set to detect outliers;
and
the one or more computers automatically marking in said computer memory, an indication of inappropriateness based on at least one transaction whose associated person corresponds to at least one tuple in the subset.

4 more elements

One or more non-transitory computer-readable storage media comprising a plurality of instructions to cause a computer comprising a memory to:

retrieve from a relational database, a plurality of records of transactions to be analyzed together;
wherein each record in said plurality identifies at least an amount of a transaction, a person associated with the transaction, and a type of the transaction;
automatically prepare in said memory, a set of tuples corresponding to a set of persons identified in the plurality of records;
wherein a tuple corresponding to said person comprises a group of numbers which are derived from transactions associated with said person, based on types of the transactions;
automatically identify a subset of said tuples, by analysis of said set to detect outliers;
and
automatically mark in said memory, an indication of inappropriateness based on at least one transaction from which is derived a number in at least one tuple in the subset.

4 more elements

Dependencies are inferred from the claim text.

Automatic Detection Of Fraud And Error Using A Vector-Cluster Model

Life of this patent

Automatic Detection Of Fraud And Error Using A Vector-Cluster Model

Life of this patent

Abstract

Claims21