Abstract: Disclosed is an example approach in which network and non-network features are used to train a predictive machine learning model that is implemented to predict financial crime and fraud. Graphical network features may be generated by applying financial entity risk vectors to a network model with representations of various types of networks. The network model may comprise transactional, non-social, and/or social networks, with edges corresponding to linkages that may be weighted according to various characteristics (such as frequency, amount, type, recency, etc.). The graphical network features may be fed to the predictive model to generate a likelihood and/or prediction with respect to a financial crime. A perceptible alert is generated on one or more computing devices if a financial crime is predicted or deemed sufficiently likely. The alert may identify a subset of the set of financial entities involved in the financial crime and present graphical representations of networks and linkages.

Abstract: Disclosed is an example approach in which network and non-network features are used to train a predictive machine learning model that is implemented to predict financial crime and fraud. Graphical network features may be generated by applying financial entity risk vectors to a network model with representations of various types of networks. The network model may comprise transactional, non-social, and/or social networks, with edges corresponding to linkages that may be weighted according to various characteristics (such as frequency, amount, type, recency, etc.). The graphical network features may be fed to the predictive model to generate a likelihood and/or prediction with respect to a financial crime. A perceptible alert is generated on one or more computing devices if a financial crime is predicted or deemed sufficiently likely. The alert may identify a subset of the set of financial entities involved in the financial crime and present graphical representations of networks and linkages.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for screening data instances based on a target text of a target corpus. A screening device analyzes a target corpus to generate at least two term dictionaries for the target corpus. The screening apparatus, based on a frequency of a term in the target corpus, determines a term weight for the term; for each data instance, determines term scores for the data instance and the target text based on the term weights; filters the data instances based on the term scores, to generate a short list of data instances; determines term similarity scores between each data instance of the short list and target text based on the term weights; and provides a data instance determined to likely correspond to the target text and an indication of the corresponding term similarity score(s). A term is a word or an n-gram.

Abstract: A method, a system, and an article are provided for detecting bot users of a software application. An example method can include: providing a client application to a plurality of users; obtaining device-based data and application-based data for each user, the device-based data including a description of at least one computer component used to run the client application, the application-based data including a history of user interactions with the client application; aggregating the data to obtain a plurality of bot signals for each user; analyzing the bot signals to detect a bot among the plurality of users; and preventing the bot from accessing the client application.

Abstract: A method, a system, and an article are provided for detecting bot users of a software application. An example method can include: providing a client application to a plurality of users; obtaining device-based data and application-based data for each user, the device-based data including a description of at least one computer component used to run the client application, the application-based data including a history of user interactions with the client application; aggregating the data to obtain a plurality of bot signals for each user; analyzing the bot signals to detect a bot among the plurality of users; and preventing the bot from accessing the client application.

Abstract: A method, a system, and an article are provided for detecting bot users of a software application. An example method can include: providing a client application to a plurality of users; obtaining device-based data and application-based data for each user, the device-based data including a description of at least one computer component used to run the client application, the application-based data including a history of user interactions with the client application; aggregating the data to obtain a plurality of bot signals for each user; analyzing the bot signals to detect a bot among the plurality of users; and preventing the bot from accessing the client application.

Abstract: Methods, computer readable media, and apparatuses for evaluating models using forecast error attribution are presented. According to one or more aspects, one or more input values corresponding to one or more input variables may be forecast. One or more results of a modeling function may be calculated using the one or more forecasted input values. Thereafter, actual performance data corresponding to the modeling function may be received. One or more holdout values for the modeling function may be calculated using the actual performance data. Subsequently, a graph that includes the one or more results of the modeling function, the actual performance data, and the one or more holdout values for the modeling function may be plotted. In some arrangements, the one or more holdout values for the modeling function may be indicative of one or more assumption errors made with respect to the one or more forecasted input values.

Abstract: A data driven and forward looking risk and reward appetite methodology for consumer and small business is described. The methodology includes account level historical data collection for customers associated with accounts as part of a portfolio. The account level historical data is segmented into groups of customers with similar revenues and loss characteristics. Segmented data is decomposed into seasoning, vintage, and cycle effects. Statistical clusters are formed based upon the data and effects. A simulation is applied to the statistical clusters and prediction data is generated. A simulation strategy to forecast and simulate revenue and loss volatility is developed. Efficient frontier curves of risk (e.g., return volatility) and reward (e.g., expected return) are created for the current portfolio under various economic scenarios.

Abstract: Embodiments of the present invention relate to methods and apparatuses for determining leading indicators and/or for modeling one or more time series. For example, in some embodiments, a method is provided that includes: (a) receiving first data indicating the value of a total income amount for a plurality of consumers over a period of time; (b) receiving second data indicating the value of a total debt amount for a plurality of consumers over a period of time; (c) selecting a consumer leverage time series that compares the total income amount to the total debt amount over a period of time; (d) modeling the consumer leverage time series based at least partially on the first and second data; (e) determining, using a processor, the value of the cycle component for a particular time; and (f) outputting an indication of the value of the cycle component for the particular time.

Abstract: A data driven and forward looking risk and reward appetite methodology for consumer and small business is described. The methodology includes account level historical data collection for customers associated with accounts as part of a portfolio. The account level historical data is segmented into groups of customers with similar revenues and loss characteristics. Segmented data is decomposed into seasoning, vintage, and cycle effects. Statistical clusters are formed based upon the data and effects. A simulation is applied to the statistical clusters and prediction data is generated. A simulation strategy to forecast and simulate revenue and loss volatility is developed. Efficient frontier curves of risk (e.g., return volatility) and reward (e.g., expected return) are created for the current portfolio under various economic scenarios.

Abstract: Methods, computer readable media, and apparatuses for evaluating models using forecast error attribution are presented. According to one or more aspects, one or more input values corresponding to one or more input variables may be forecast. One or more results of a modeling function may be calculated using the one or more forecasted input values. Thereafter, actual performance data corresponding to the modeling function may be received. One or more holdout values for the modeling function may be calculated using the actual performance data. Subsequently, a graph that includes the one or more results of the modeling function, the actual performance data, and the one or more holdout values for the modeling function may be plotted. In some arrangements, the one or more holdout values for the modeling function may be indicative of one or more assumption errors made with respect to the one or more forecasted input values.

Abstract: A data driven and forward looking risk and reward appetite methodology for consumer and small business is described. The methodology includes customer segmentation to create pools of homogeneous assets in terms of revenue and loss characteristics, forward looking simulation to forecast expected values and volatilities of revenue and loss, and risk and reward optimization of the portfolio. One methodology used for modeling revenue and loss is a generalized additive effect decomposition model to fit historical data. Based on the model, a segmentation procedure is performed, which allows for creation of groups of customers with similar revenue and loss characteristics. An estimation procedure for the model is developed and a simulation strategy to forecast and simulate revenue and loss volatility is developed. Efficient frontier curves of risk (e.g., return volatility) and reward (e.g., expected return) are created for the current portfolio under various economic scenarios.

Abstract: Embodiments of the present invention relate to methods and apparatuses for determining leading indicators and/or for modeling one or more time series. For example, in some embodiments, a method is provided that includes: (a) receiving first data indicating the value of a total income amount for a plurality of consumers over a period of time; (b) receiving second data indicating the value of a total debt amount for a plurality of consumers over a period of time; (c) selecting a consumer leverage time series that compares the total income amount to the total debt amount over a period of time; (d) modeling the consumer leverage time series based at least partially on the first and second data; (e) determining, using a processor, the value of the cycle component for a particular time; and (f) outputting an indication of the value of the cycle component for the particular time.

Abstract: Chat categorization uses semi-supervised clustering to provide Voice of the Customer (VOC) analytics over unstructured data via an historical understanding of topic categories discussed to derive an automated methodology of topic categorization for new data; application of semi-supervised clustering (SSC) for VOC analytics; generation of seed data for SSC; and a voting algorithm for use in the absence of domain knowledge/manual tagged data. Customer service interactions are mined and quality of these interactions is measured by “Customer's Vote” which, in turn, is determined by the customer's experience during the interaction and the quality of customer issue resolution. Key features of the interaction that drive a positive experience and resolution are automatically learned via machine learning driven algorithms based on historical data. This, in turn, is used to coach/teach the system/service representative on future interactions.

Abstract: A data driven and forward looking risk and reward appetite methodology for consumer and small business is described. The methodology includes customer segmentation to create pools of homogeneous assets in terms of revenue and loss characteristics, forward looking simulation to forecast expected values and volatilities of revenue and loss, and risk and reward optimization of the portfolio. One methodology used for modeling revenue and loss is a generalized additive effect decomposition model to fit historical data. Based on the model, a segmentation procedure is performed, which allows for creation of groups of customers with similar revenue and loss characteristics. An estimation procedure for the model is developed and a simulation strategy to forecast and simulate revenue and loss volatility is developed. Efficient frontier curves of risk (e.g., return volatility) and reward (e.g., expected return) are created for the current portfolio under various economic scenarios.