Abstract: A strategic default score is determined for an entity that characterizes a likelihood of the entity voluntarily electing to default on a loan. Data characterizing credit data and valuation data of an asset owned by the entity is received. Using the received data, the strategic default score is generated which characterizes a likelihood of the entity to voluntarily elect to default on the loan when the entity is capable of paying-off the loan. Provision (e.g., display, transmission, storage, etc.) of the strategic default score is then initiated. Related apparatus, systems, techniques and articles are also described.

Abstract: The current subject matter describes a generation of a score based on an enhanced market basket analysis (eMBA). An eMBA model can receive historical data characterizing historical purchases of a plurality of products over a specified time-period. In response, the eMBA model can generate baskets, which can include data that is causal and predictive. The generated baskets can be provided as an input to a group generator. The group generator can then generate product groups and confidence values. The product groups and confidence values can be provided to a score generator. In run-time, the score generator can receive current product data, and in return, can use the product groups and confidence values to generate a score. The score can characterize a likelihood of a purchase of the product by a corresponding customer associated with the product group. Related methods, apparatuses, systems, techniques and articles are also described.

Abstract: A scoring model is provided that is trained using historical patient readmission data. The scoring model is used to analyze patient insurance claim data for which patients were readmitted to a healthcare facility in order to characterize whether the corresponding insurance claims are potentially fraudulent or erroneous. Related techniques, apparatus, systems, and articles are also described.

Abstract: A system and method for drawing curved edges in graphs is disclosed. The system and method implement a heuristic algorithm to draw curved edges in graphs using Bezier curves. The algorithm assumes that every pair of nodes has a unique edge between them. It also assumes that the graph is “leveled,” which means the nodes can be grouped such that all the nodes in a group are laid out at the same y location in a vertical layout. Any generic graph can be converted to a leveled graph, so the techniques described in the algorithm are applicable to any graph.

Abstract: Input characterizing one or more economic indicators and a portfolio of accounts can be used to estimate a portfolio level effect of the economic indicators on the portfolio of accounts is estimated. Based on this estimation, an account level effect of the economic indicators is simulated for each of the accounts. The overall affect of the simulated account level effects approximates the portfolio level effect. Simulated account level effects can thereafter be aggregated in order to characterize future risk for the portfolio of accounts. Related apparatus, systems, techniques and articles are also described.

Abstract: Methods and apparatus, including computer program products, related to entity name translation. In general, a set of data records including one or more names of one or more entities in a source language is received and candidate translations are generated for the names of the data records. The candidate translations are strings of characters in a target language that has a different orthography than an orthography of the source language. A data repository is queried for records matching the candidate translations. A translation of at least one of the candidate translations is selected based on a comparison of a combination of candidate translations and properties of the source record comprising of the publication date, entity name co-occurrence and their frequency with the result of the querying the data repository.

Abstract: Methods and related system are described for making decisions. A described method includes selecting a choice from the available choices, receiving an outcome relating to the selected choice, and automatically learning from the received outcome by incorporating the received outcome into subsequent steps of selecting a choice. The method may also include calculating estimated probabilities associated with the each choice using Bayesian networks. The automated learning can be based on a learning rate which is variable with time, and influences the degree on which prior outcomes are relied upon when calculating an estimated probability associated with a choice. The learning rate can be a function of time and an estimate of drift of the probability associated with the selected choice.

Abstract: A number of memory spaces are defined for a memory structure. Transaction data is received, and financial transactions are scored based on the transaction data to generate a batch of scores for the financial transactions. A score range is divided into k bins, each of the k bins representing one memory space of the memory spaces of the memory structure. The batch of scores are aggregated by storing a count of each score in a memory space, and a percentile is computed for each score in the batch of scores based on a set of values associated with each score. Each new financial transaction is scored to generate a new score, and a new percentile is assigned to the new score according to the set of values. The new percentile is then translated to a calibration score with fixed percentile characteristics according to a fixed reference curve.

Abstract: A method and system for estimating potential future outcomes resulting from decision alternatives is presented to enable lenders to make lending related decisions. The estimation is based on a propensity score variable that encompasses an effect of multiple covariates associated with one or more individuals for whom the estimation is being performed. For consistency with empirical testing, the estimation approach assumes conditions of unconfoundedness and localized common support. According to the unconfoundedness assumption, for a given variable, the potential outcomes are conditionally independent of the decision alternatives. According to the localized common support assumption, an overlap is ensured between individual accounts that are categorized together as potentially having the same future outcome. The outcomes and an effect (e.g. comparison) of the outcomes may be displayed graphically.

Abstract: A fraud detection and protection method and system are disclosed. The method and system utilize a fraud detection and protection server to monitor online commercial transactions between a webserver and a client computer, and generate a risk assessment of a user associated with the client computer. The system and method further utilize a number of beacon servers geographically dispersed in an area. Each beacon server is configured to receive packet header information associated with the online commercial transactions, analyze the packet header information for authenticating information, and send the authenticating information to the fraud detection and protection server for the risk assessment.

Abstract: A system and method for automated variable creation for adaptive fraud analytics are disclosed. A data structure for creation of rules is generated. The data structure represents nodes and associations between nodes from inputs for fraud/non-fraud conditions, and is generated from fraud and non-fraud data collected in an adaptive modeling process from past transactions. All unique paths between nodes of the data structure are determined to define a rule for each path. Each rule is then converted to a binary indicator variable to generate a set of binary indicator variables, and one or more complex variables is derived from the set of binary indicator variables. The one or more binary indicator variables and one or more complex variables can be provided to an adaptive scoring engine to score new transactions or to predict future behaviors.

Abstract: 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.

Abstract: Fraud and abuse detection in an entity's payment coding practices includes the ability to search for fraud at all levels of the hierarchical coded payment system within the context of an unsupervised model. The model uses variables derived and profiles created at any level or at all levels of the hierarchical coded payment system to create a comprehensive description of the payment coding activities submitted by the entity. That description is compared with other peer entities to determine unusual and potentially inappropriate activity. The profiles created may themselves be utilized for purposes other than the detection of fraud and abuse.

Abstract: A network assurance analytics (NAA) system and method is disclosed. The NAA can be part of a risk analytic for telecom (RAFT) program. The NAA system is configured to monitor telecommunications networks, detect errors or fraud in those telecommunications networks, and provide solutions to resolve the errors or reduce the fraud. Traffic of a telecommunications network is electronically monitored for at least one pattern that is indicative of a telecommunications anomaly. Based on a set of a set of telecommunications profiles stored in a database, a model score representing a value of the telecommunications anomaly is generated. A solution for the telecommunications network to reduce the model score associated with the telecommunications anomaly is then generated for execution on the telecommunications network.

Abstract: A computer-implemented method and system for automated entity identification for efficient profiling in an event probability prediction system. A first subset of entities belonging to one or more entity classes is defined. At least one historical profile is constructed for each entity in the subset of entities based on a set of possible outcomes of transaction behavior of each entity in the first subset of entities. Based on the historical profiles, a second subset of entities having transaction behavior associated with a transaction is selected, the transaction behavior being predictive of at least one targeted outcome from the set of possible outcomes. The first subset of entities is redefined with the second subset of entities.

Abstract: A time interval during which to present an offering to a consumer is determined using a cadence consistency score calculated for the consumer. In addition, it is determined which of a plurality of items to offer to the consumer is determined using an item diversity score calculated for the consumer. Thereafter, provision an offering for the determined item during the determined time interval is initiated. Related apparatus, systems, techniques and articles are also described.

Abstract: Transaction-based behavioral profiling, whereby the entity to be profiled is represented by a stream of transactions, is required in a variety of data mining and predictive modeling applications. An approach is described for assessing inconsistency in the activity of an entity, as a way of detecting fraud and abuse, using service-code information available on each transaction. Inconsistency is based on the concept that certain service-codes naturally co-occur more than do others. An assessment is made of activity consistency looking at the overall activity of an individual entity, as well as looking at the interaction of entities. Several approaches for measuring consistency are provided, including one inspired by latent semantic analysis as used in text analysis. While the description is in the context of fraud detection in healthcare, the techniques are relevant to application in other industries and for purposes other than fraud detection.

Abstract: A computerized method and system for evaluating customers of a financial institution using customer relationship value tags associated customer treatment actions includes automatically analyzing information about a customer from a database of the financial institution by a customer assessment engine using a predefined statistical model to assess the value of the customer to the financial institution. At least one customer treatment action associated with the assessed value is identified by the assessment engine, and the assessment engine marks a file associated with the customer with a mark representing the assessed value and the associated customer treatment action. The marked file is then accessed by other financial systems of the financial institution, or by customer representatives of the financial institution, and the associated customer treatment action is implemented by such systems or representatives in dealing with the customer.

Abstract: 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.

Abstract: 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.