Fraud detection methods and systems
First Claim
1. A fraud detection method, comprising:
- obtaining data relating to a sample set of claims or transactions made to one of an insurer, guarantor, financial institution, and payor;
obtaining external data relating to at least one of the claims, submissions, claimants, incidents and transactions giving rise to the claims or transactions in the set;
using at least in part at least one data processing device, identifying from the data and the external data a set of variables usable to discover patterns in the data;
using the at least one data processing device, discovering patterns in the set of variables that at least one of;
indicate a normal profile of said claims or transactions, indicate an anomalous profile of said claims or transactions, and indicate a high propensity of fraud in said claims or transactions;
assigning a new claim, not in the sample set, to at least one of the profiles; and
outputting the identified potentially fraudulent new claims to a user as a basis for an investigative course of action.
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Accused Products
Abstract
An unsupervised statistical analytics approach to detecting fraud utilizes cluster analysis to identify specific clusters of claims or transactions for additional investigation, or utilizes association rules as tripwires to identify outliers. The clusters or sets of rules define a “normal” profile for the claims or transactions used to filter out normal claims, leaving “not normal” claims for potential investigation. To generate clusters or association rules, data relating to a sample set of claims or transactions may be obtained, and a set of variables used to discover patterns in the data that indicate a normal profile. New claims may be filtered, and not normal claims analyzed further. Alternatively, patterns for both a normal profile and an anomalous profile may be discovered, and a new claim filtered by the normal filter. If the claim is “not normal” it may be further filtered to detect potential fraud.
235 Citations
64 Claims
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1. A fraud detection method, comprising:
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obtaining data relating to a sample set of claims or transactions made to one of an insurer, guarantor, financial institution, and payor; obtaining external data relating to at least one of the claims, submissions, claimants, incidents and transactions giving rise to the claims or transactions in the set; using at least in part at least one data processing device, identifying from the data and the external data a set of variables usable to discover patterns in the data; using the at least one data processing device, discovering patterns in the set of variables that at least one of;
indicate a normal profile of said claims or transactions, indicate an anomalous profile of said claims or transactions, and indicate a high propensity of fraud in said claims or transactions;assigning a new claim, not in the sample set, to at least one of the profiles; and outputting the identified potentially fraudulent new claims to a user as a basis for an investigative course of action. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26)
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27. A non-transitory computer readable medium containing instructions that, when executed by at least one processor of a computing device, cause the computing device to:
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receive a set of patterns in a set of predictive variables that at least one of; indicate a normal profile of claims or transactions, indicate an anomalous profile of said claims or transactions, and indicate a high propensity of fraud in said claims or transactions; receive at least one new claim or transaction; assign the at least one new claim or transaction to at least one of the profiles; and output any identified potentially fraudulent new claims to a user as a basis for an investigative course of action. - View Dependent Claims (30, 33, 40)
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46. A system for fraud detection, comprising:
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one or more data processors; and memory containing instructions that, when executed, cause one or more processors to, at least in part; obtain data relating to a sample set of claims or transactions made to one of an insurer, guarantor, financial institution, and payor; obtain external data relating to at least one of the claims, submissions, claimants, incidents and transactions giving rise to the claims or transactions in the set; identify from the data and the external data a set of variables usable to discover patterns in the data; discover patterns in the set of variables that at least one of indicate a normal profile of said claims or transactions, indicate an anomalous profile of said claims or transactions, and indicate a high propensity of fraud in said claims or transactions; assign a new claim, not in the sample set, to at least one of the profiles; and output the identified potentially fraudulent new claims to a user as a basis for an investigative course of action.
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49. A system for fraud detection, comprising:
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one or more data processors; and memory containing instructions that, when executed, cause one or more processors to, at least in part; receive a set of patterns in a set of predictive variables that at least one of; indicate a normal profile of claims or transactions, indicate an anomalous profile of said claims or transactions, and indicate a high propensity of fraud in said claims or transactions; receive at least one new claim or transaction; assign the at least one new claim or transaction to at least one of the profiles; and output any identified potentially fraudulent new claims to a user as a basis for an investigative course of action. - View Dependent Claims (62)
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Specification