Fair Isaac Corporation

Joseph P Milana

Walter W Lee

Wesley Kenneth Wilhelm

Linyu Yang

Gerald Fahner

Stuart L Crawford

Larry Peranich

Paul Charles Dulany

Xuhui Shao

Daniel Shoham

Min Shao

Russell Anderson

Ricardo M Dungca

Khosrow M Hassibi

James C Baker

Gregory Gancarz

Allen P Jost

David Griegel

Bo Zhang

Zuohua Zhang

Bogdan Eugen Popescu

Frank Wall Elliott, Jr

Colin Andrew Shepherd

Kyle Randolph Hinsz

Ronald L Martin

Alan P Jost

Danfeng Li

A computer-implemented method and system for detecting fraud in electronic commerce traffic are disclosed. A global conversion rate is defined that represents activity related to a purchase made or proxy activity executed during electronic commerce traffic to a commercial website that is indicative of non-fraud activity. Subsets of the electronic commerce traffic to a commercial website are monitored for clusters of activity having a conversion rate that is lower than a global conversion rate by a threshold margin. A number of user-initiated input signals from an input device to navigate from each of one or more affiliate websites to the commercial website, and a number of conversions generated from each of the one or more affiliate websites, are registered by a computer for analysis.

Auto adaptive anomaly detection system for streams

A Click Conversion Score (CCS) is a tool to provide a new 'Ad-Exchange' for connecting Publishers and Advertisers in the Pay-per-Click advertising market. The CCS provides checks and balances into the Ad-Exchange by assessing the quality of traffic each Publisher provides Advertisers using an adaptive, multi-dimensional profiling system. This assessment is directly used in the charges billed the Advertisers. The score is adaptive, and highly dependent on the conversion feedback loop provided by Advertisers. A CCS system scores each Click and each resulting Conversion an Advertiser receives from the Ad-Exchange. In the preferred implementation, the score reflects a ratio of the measured conversion rate for a Publisher's traffic with the 'expected' conversion rate based upon Advertisers histories.

Click conversion score

A software system and method for executing secure commercial transactions online is disclosed. The system intercepts and verifies, against consumer provided inputs, Secure Socket Layer (SSL) communications from normal Web browser usage. The system can include a software module loaded onto the consumer's client computer, and which uses independently-derived look-ups to associate a web domain name with its SSL public key to verify that a given web session is appropriately encrypted.

Consumer-driven secure sockets layer modulator

A software system and method for executing secure commercial transactions online is disclosed. A user's password is received to initiate secure socket layer (SSL) communications with a transaction site on a server. A web session associated with the SSL communications is encrypted by associating a domain name of the transaction site with its SSL public key. Then, the user's password is added to a hypertext markup language (HTML) header of a message within the web session. When added, the password is invisible to a hypothetical man-in-the-middle (MITM) attacker, who cannot read the encrypted message nor mimic the user. The MITM is thus unable to compromise the user's account as the MITM is unable to provide the correct password into any fraudulent message.

A data transaction is associated with a plurality of remote data sources so that a score may be generated that characterizes the transaction. The remote data sources are polled to obtain partial scores which can be used by a decisioning system to score the transaction. In some implementations, different scoring models may be used so that the data source is notified which scoring model to use when generating the partial score. Related techniques, apparatus, systems, and articles are described.

Distributed scoring of data transactions

A merchant profile builder (MPB) is provided. The complete merchant profiling process is split into three pieces: Data Preprocessing, Weekly Summary Calculation, and Merchant Profile Calculation. In the Data Preprocessing stage, authorization data and daily fraud data are processed on a weekly basis to yield: an authorization extract file and fraud authorization data, each of which are sorted by a merchant key. In the Weekly Summary Calculation stage, the files output by the Data Preprocessing stage are used as input to compile basic statistics for each merchant based only on data for one week. There are separate weekly summaries for authorizations and fraud authorizations. In the Merchant Profile Calculation stage, the weekly summaries from the 16 most recent weeks are combined together and averages are calculated and output a set of merchant profiles and a set of default profiles.

Method and apparatus for a merchant profile builder

The invention aids an entity operating on the Internet or on another network to selectively request additional data about a user who has made a request for an interaction with the entity. The invention helps an entity to determine when and how to request additional data so as to reduce the likelihood of causing the user to have an adverse reaction, e.g., terminate the interaction. One embodiment of the invention concerns customers requesting transactions with on-line merchants. More specifically, this embodiment aids merchants by detecting Internet credit card transactions that are likely to be fraudulent, and providing the merchants with mechanisms for managing a suspected transaction as it occurs to obtain additional information that can be useful to reducing the likelihood of fraud.

Score based decisioning

Transaction processing of online transactions at merchant sites determines the likelihood that such transactions are fraudulent, accounting for unreliable fields of a transaction order, which fields do not reliably identify a purchaser. A scoring server using statistical model uses multiple profiles associated with key fields, along with weights to indicate the degree to which the profiles identify the purchaser of the transaction.

Identification and management of fraudulent credit/debit card purchases at merchant ecommerce sites

Computer implemented methods and systems of processing transactions to determine the risk of transaction convert high categorical information, such as text data, to low categorical information, such as category or cluster IDs. The text data may be merchant names or other textual content of the transactions, or data related to a consumer, or any other type of entity which engages in the transaction. Content mining techniques are used to provide the conversion from high to low categorical information. In operation, the resulting low categorical information is input, along with other data, into a statistical model. The statistical model provides an output of the level of risk in the transaction. Methods of converting the high categorical information to low categorical clusters, of using such information, and other aspects of the use of such clusters are disclosed.

Detecting and measuring risk with predictive models using content mining

A first set of variables is introduced into a data transaction scoring system. The first set of variables having a maturity level less than a second set of variables previously matured on the data transaction scoring system. The maturity level corresponding to an amount of exposure to the data transaction scoring system. The amount of exposure affecting a degree of precision for the data transaction scoring system. The first set of variables are introduced by expanding a data transaction profile for the data transaction scoring system including second set of variables to further include the first set of variables. Initial values are assigned for the first set of variables based on a statistical model. After such an assignment, scoring of data transactions using the expanded data transaction profile prior to maturing the data transaction system using the first set of variables can be initiated. Related apparatus, systems, techniques, and articles are also described.

Rapidly maturing expanded data transaction profiles

A method of generating a model for performing account-level fraud detection is disclosed, and includes obtaining a plurality of candidate rules for referrals of financial transactions, calculating a savings density for each of the plurality of rules, selecting a rule from the plurality of rules having the maximum savings density, iteratively rebalancing savings densities for the remaining rules of the plurality of rules and selecting a next rule until a volume constraint is satisfied, and applying the selected rules to one or more financial transactions.

Account-level fraud detector and associated methods

Techniques for compressing data transaction history profiles are disclosed. Such profiles can include a plurality of profile variables with each profile variable comprising a real number that provides a factor for determining whether a proposed data transaction is indicative of fraud. A bit string is generated for each profile variable in the profiles that characterizes a first value plus a second value. The first value is equal to a mantissa for the real number corresponding to the profile variable. The second value is equal to a number of orders of magnitude above a minimum required expressed as multiples of the number of orders magnitude required to represent the plurality of real numbers in each the plurality of transaction history profiles divided by a range of bits. The generated bit string is stored as compressed profile variable within the data transaction history profiles. Related systems, apparatus, methods, and/or articles are also described.

Data transaction profile compression

A computer-implemented simulator models the entire analytic value chain so that data generation, model fitting and strategy optimization are an integral part of the simulation. Data collection efforts, data mining algorithms, predictive modeling technologies and strategy development methodologies define the analytic value chain of a business operation: data&#x2192;models&#x2192;strategies&#x2192;profit. Inputs to the simulator include consumer data and potential actions to be taken regarding a consumer or account. The invention maps what is known about a consumer or an account and the potential actions that the business can take on that consumer or account to potential future financial performance. After iteratively performing simulations using varying inputs, modeling the effect of the innovation on a profit model, the simulator outputs a prediction of the commercial value of an analytic innovation.

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San Diego, CA, United States of America

Joseph P Milana