San Jose, CA, United States

FICO (Fair Isaac Corporation)

www.fico.com
San Jose, CA, United States

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.

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Patent
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.


Patent
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.


Patent
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.


A data object from a data source is received by a distributed process in a data stream. The distributed process has a sequence of categories, each category containing one or more tasks that operate on the data object. The data object includes files that can be processed by the tasks. If the task is able to operate on the data object, then the data object is passed to the task. If the task is unable to operate on the data object, then the files in the data object are passed to a file staging area of the distributed process and stored in memory. The files in the file staging area are passed, in sequence, from the file staging area to the task that was unable to operate on the data object. The data object is outputted to a next category or data sink after being operated on by the task.


Patent
FICO (Fair Isaac Corporation) | Date: 2016-01-20

The current subject matter describes a method and system of detecting frauds or anomalous behavior. The procedures include extracting characteristics from a dataset to generate words and documents, executing a topic model to obtain the respective probabilities of appearance of a document in each latent archetype, dividing the dataset into a plurality of subsets based upon the archetypes. The formed subsets are further utilized to estimate the quantiles and calculate scores using a self-calibrating outlier model. The score of each new transaction is determined based on a single archetype or based on the sum of weighted scores determined from all the archetypes and associated statistics. Such methods are superior to a simple self-calibration outlier model without an LDA archetype. The detection system with the LDA archetypes and self-calibrating outlier model is implemented with the sliding window technique incorporating new transactions into the topic model and it is capable of operating in real-time for the purpose of identifying frauds and outliers.


Patent
FICO (Fair Isaac Corporation) | Date: 2017-08-09

A system and method for rapid data investigation and data integrity analysis is disclosed. A data set is received by a server computer from one or more client computers connected with the server computer via a communications network, and the data set is stored in a distributed storage memory. One or more analytical processes are executed on the data set from the distributed storage memory to generate statistics based on each of the analytical processes, and the statistics are stored in a random access memory, the random access memory being accessible by one or more compute nodes, which generate a graphical representation of at least some statistics stored in the random access memory. The graphical representation of at least some statistics is then formatted for transmission to and display by the one or more client computers.


Patent
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.


Patent
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.


Patent
FICO (Fair Isaac Corporation) | Date: 2016-10-31

As part of neural network sensitivity analyses, base outputs of hidden layer nodes of a neural network model for non-perturbed variables can be reused when perturbing the variables. Such an arrangement greatly reduces complexity of the calculations required to generate outputs of the model. Related apparatus, systems, techniques and articles are also described.


An automated way of learning archetypes which capture many aspects of entity behavior, and assigning entities to a mixture of archetypes, such that each entity is represented as a distribution across multiple archetypes. Given those representations in archetypes, anomalous behavior can be detected by finding misalignment with a plurality of entities archetype clustering within a hard segmentation. Extensions to sequence modeling are also discussed. Applications of this method include anti-money laundering (where the entities can be customers and accounts, as described extensively below), retail banking fraud detection, network security, and general anomaly detection.

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