FICO is a Software company based in San Jose, California and founded by Bill Fair and Earl Isaac in 1956. Its FICO score, a measure of consumer credit risk, has become a fixture of consumer lending in the United States. In 2013, lenders purchased more than 10 billion FICO scores and about 30 million American consumers accessed their scores themselves. Wikipedia.
FICO (Fair Isaac Corporation) | Date: 2015-09-21
A computer-implemented method of fraud detection includes clustering samples on the tree nodes in the decision tree model on the training dataset, calculating the cluster centroids and determining the high fidelity radius for a preset threshold probability for each cluster and determining the left-over class probability for each node. The new transactional data is classified in three steps: first to determine based on the decision tree what leaf node the transaction is associated, second to determine the membership to a cluster of the leaf node using the shortest distance to the cluster centroid and then third to compare the distance with the high fidelity radius and then to determine the eventual class probability for a new data. The new method demonstrates better performance than the decision-tree alone model.
FICO (Fair Isaac Corporation) | Date: 2015-10-09
A predictive analytics system and method in the setting of multi-class classification are disclosed, for identifying systematic changes in an evaluation dataset processed by a fraud-detection model by examining the time series histories of an ensemble of entities such as accounts. The ensemble of entities is examined and processed both individually and in aggregate, via a set of features determined previously using a distinct training dataset. The specific set of features in question may be calculated from the entitys time series history, and may or may not be used by the model to perform the classification. Certain properties of the detected changes are measured and used to improve the efficacy of the predictive model.
FICO (Fair Isaac Corporation) | Date: 2015-11-18
The state of a system is determined in which data sets are generated that include a plurality of data instances representing states of one or more components of a computer system. The data instances generated by one or more data set sources that are configured to output a data instance in response to a trigger associated with the one or more components. The data instances are normalized by the application of one or more rules. The data instances from individual data set sources are separately collated to generate groups of time-specific collated data instances. State types may be assigned to each of the collated data instance groups. Distributions of state-types across the groups may be determined and a list of infrequent state-types may be generated based on the determined distributions of state-types across the groups.
FICO (Fair Isaac Corporation) | Date: 2016-12-30
A computer-implemented method for reconciling records from a plurality of data sets includes receiving a first data set from a left data source, retrieving data from the first data set, and placing the retrieved data from the first data set into a first abstract record from the left data source. The method also includes receiving a second data set from a right data source, retrieving data from the second data set, and placing the retrieved data from the second data set into a second abstract record from the right data source. The computer-implemented method also includes comparing the first abstract record and the second abstract record.
FICO (Fair Isaac Corporation) | Date: 2015-11-12
A system and method is disclosed as using archetype-based n-grams based on an event sequence of the real-time transactions, the n-grams providing a probability based on a specific sequence of behavioral events and their likelihood, and in which high probability n-grams represent typical behaviors of customers in a same peer group, and low probability n-grams represent rare event sequences and increased risk.
FICO (Fair Isaac Corporation) | Date: 2016-10-31
Data is received that characterizes a score model. Thereafter, the score model is normalized by transforming it into a directed acyclic graph. The directed acyclic graph is then transformed into a structured rules language program. The structured rules language program is then transformed into a program using a concurrent, class-based, object-oriented computer programming language (e.g., JAVA, C, COBOL, etc.). Related apparatus, systems, techniques and articles are also described.
FICO (Fair Isaac Corporation) | Date: 2015-07-24
Generating optimal strategies for providing offers to a plurality of customers is described. A plurality of categorical attributes (for example, gender and residential status) and ordinal attributes (for example, risk score and credit line utilization) can be determined. Values of one of more categorical attributes can be changed as per a transition probability table. Some probabilities can be varied to determine a first tradeoff, based on which a first updated strategy can be generated. Further, noise can be added to one or more ordinal attributes. Standard deviation of a noise distribution associated with the noise can be varied so as to determine a second tradeoff, based on which a second updated strategy can be generated. The second updated strategy can be an update of the first updated strategy. Offers can be provided to the plurality of customers in accordance with the second updated strategy.
FICO (Fair Isaac Corporation) | Date: 2015-02-18
Data is received that characterizes at least one of credit, financial, and demographic data for a consumer. Thereafter, estimated income is determined for the user. Using the estimated income and the data, a second income level for the consumer is determined also using a confidcnce interval model and a pre-defined confidence thereshold Ci. The second income level for the consumer is less than the determined estimated income and is determined such that actual income for the consumer is Ci % likely to exceed the second income level. Data can then be provided that characterizes the second income level. Related apparatus, systems, techniques and articles are also described.
FICO (Fair Isaac Corporation) | Date: 2015-07-10
The subject matter disclosed herein provides methods, apparatus, systems, techniques, and articles for determining the likelihood that a transaction is abnormal. Time-series data associated with active and passive operations of a mobile device and out of band data associated with the user of the mobile device can be collected. The collected data can be processed to generate a set of mobile attribute data that define a behavior of one or more of the user and the mobile device. A profile containing profile variables for selected attributes from the set of mobile attribute data can be generated. The profile can summarize past usage of the user or the mobile device. A set of one or more transactions associated with the mobile device can be monitored. A first score representing a degree to which the transaction is abnormal can be generated. Related apparatus, systems, techniques, and articles are also described.
FICO (Fair Isaac Corporation) | Date: 2015-05-29
The subject matter disclosed herein provides methods, apparatus, systems, techniques, and articles for false positive reduction in abnormality detection models. A date and time of a first transaction by a transaction entity and associated with a transaction characteristic can be stored. Data representing subsequent transactions associated with the transaction characteristic can be stored. A history marker profile specific to the transaction characteristic and transaction entity can be generated and can include the transaction characteristic, the date and time of the first transaction, and maximum and mean abnormality scores. A date and time of a current transaction can be determined. A current abnormality score for the current transaction can be received. A tenure of the observed transaction characteristic can be computed. The current abnormality score can be recalibrated from the transaction entity abnormality detection system according to the maximum, mean, and current abnormality scores and a length of the tenure.