Abstract: Systems and methods for generating concise explanations of scored observations that strike good, and computationally efficient, trade-offs between rank-ordering performance and explainability of scored observations are disclosed. The systems and methods described herein for explaining scored observations are based on a framework of partial dependence functions (PDFs), multi-layered neural networks (MNNs), and Latent Explanations Neural Network Scoring (LENNS).

Abstract: A method, a system, and a computer program product for generating an interpretable set of features. One or more search parameters and one or more constraints on one or more search parameters for searching data received from one or more data sources are defined. The data received from one or more data sources is searched using the defined search parameters and constraints. One or more first features are extracted from the searched data. The first features are associated with one or more predictive score values. The searching is repeated in response to receiving a feedback data responsive to the extracted first features. One or more second features resulting from the repeated searching are generated.

Abstract: Computer-implemented systems, methods and products for modeling sensitivities to potential disruptions by observing performances of entities in a first sub-population and a second sub-population using a machine learning model comprising a set of predictors and a binary indicator variable associated with a first entity subjected to a first event associated with the first sub-population, the machine learning model trained to predict an expected performance for the first entity based on at least one of a known attribute associated with the first entity in relation to the first event and a value of the binary indicator variable associated with the first event.

Abstract: A method, a system, and a computer program product for generating a refined synthetic data from one or more sources of data. One or more source data are received from one or more data sources. One or more encoded source data are generated from the one or more source data. A synthetic data is generated by decoding one or more encoded source data. One or more variables in the synthetic data are selected and one or more predetermined identifiability values and one or more predetermined anonymity values are associated with them. The generated synthetic data including the selected variables is decoded using associated one or more predetermined identifiability values and one or more predetermined anonymity values. The decoded synthetic data is outputted.

Abstract: A method, a system, and a computer program product for generating an interpretable set of features. One or more search parameters and one or more constraints on one or more search parameters for searching data received from one or more data sources are defined. The data received from one or more data sources is searched using the defined search parameters and constraints. One or more first features are extracted from the searched data. The first features are associated with one or more predictive score values. The searching is repeated in response to receiving a feedback data responsive to the extracted first features. One or more second features resulting from the repeated searching are generated.

Abstract: A method, a system, and a computer program product for calibrating synthetic data. A synthetic data is generated based on one or more source data using one or more generative models. The generative models are used to generate a latent space based on one or more source data. One or more latent space vectors associated with the generated latent space are determined in accordance with one or more data profiles associated with the one or more source data. The latent space vectors associated with the generated latent space are sampled. Based on the sampling, an optimized synthetic data is generated by comparing the sampled latent space vectors with one or more baseline data associated with one or more data profiles.

Abstract: A sensitivity index model for predicting the sensitivity of an entity to a potential future disruption can be trained using a process that includes dividing a population of entities for which data attributes are available into matched pairs in a first sub-population and a second sup-population based on matching propensity scores for the entities using supervised machine learning, modeling outcomes for the two sub-populations, using the resultant models to calculate expected performances of the entities under differing conditions, and generating the sensitivity index model using supervised learning techniques based on quantification of differences between the calculated expected performances for the entities.

Abstract: Systems and methods for generating concise explanations of scored observations that strike good, and computationally efficient, trade-offs between rank-ordering performance and explainability of scored observations are disclosed. The systems and methods described herein for explaining scored observations are based on a framework of partial dependence functions (PDFs), multi-layered neural networks (MNNs), and Latent Explanations Neural Network Scoring (LENNS).

Abstract: A sensitivity index model for predicting the sensitivity of an entity to a potential future disruption can be trained using a process that includes dividing a population of entities for which data attributes are available into matched pairs in a first sub-population and a second sup-population based on matching propensity scores for the entities using supervised machine learning, modeling outcomes for the two sub-populations, using the resultant models to calculate expected performances of the entities under differing conditions, and generating the sensitivity index model using supervised learning techniques based on quantification of differences between the calculated expected performances for the entities.

Abstract: 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.

Abstract: Optimal strategies for providing offers to a plurality of customers are generated. 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 generate 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.

Abstract: Systems and methods for generating concise explanations of scored observations that strike good, and computationally efficient, trade-offs between rank-ordering performance and explainability of scored observations are disclosed. The systems and methods described herein for explaining scored observations are based on a framework of partial dependence functions (PDFs), multi-layered neural networks (MNNs), and Latent Explanations Neural Network Scoring (LENNS).

Abstract: Systems and methods for generating concise explanations of scored observations that strike good, and computationally efficient, trade-offs between rank-ordering performance and explainability of scored observations are disclosed. The systems and methods described herein for explaining scored observations are based on a framework of partial dependence functions (PDFs), multi-layered neural networks (MNNs), and Latent Explanations Neural Network Scoring (LENNS).

Abstract: In one aspect, a computer implemented method for segmenting a population based on sensitivities to potential disruptions is provided. The method includes receiving one or more attributes associated with a first entity. The method further includes calculating a sensitivity index for the first entity based on the one or more attributes. The method further includes calculating a second risk score for the first entity based on the sensitivity index and the first risk score of the entity. The method further includes outputting the second risk score to a user interface.

Abstract: Systems and methods for generating concise explanations of scored observations that strike good, and computationally efficient, trade-offs between rank-ordering performance and explainability of scored observations are disclosed. The systems and methods described herein for explaining scored observations are based on a framework of partial dependence functions (PDFs), multi-layered neural networks (MNNs), and Latent Explanations Neural Network Scoring (LENNS).

Abstract: Profiles characterizing each of a plurality of consumers are received. Thereafter, each profile is associated with one of a plurality of customer segments (e.g., matched pairs, etc.). Thereafter, a coupon effectiveness index is determined for each of the plurality of consumers for an offering based on the associated customer segment. The coupon effectiveness indices model characterizes causal effects estimates determined using historical data of purchases of individuals having varying coupon treatments for the offering. Subsequently, provision of at least a portion of the determined coupon effectiveness indices is initiated. Related apparatus, systems, techniques and articles are also described.

Abstract: 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.

Abstract: A method for selecting a next action includes reading transaction data, determining insights and relationships between a first entity and a second entity from the collected transaction data. Once these relationships and insights have been determined, the possibility of a future event occurring in one of a number of selected time periods can be determined using a predictive time-to-event component. A system for selecting a next action includes a memory for storing transaction data, an insight/relationship determination module, and a predictive time-to-event module. The memory, the insight/relationship determination module and the predictive time-to-event module carry out the above method. A programmable media having an instruction set can also cause a machine to carry out the above method.

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: 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.