Abstract: Systems and methods for automated path-based recommendation for risk mitigation are provided. An entity assessment server, responsive to a request for a recommendation for modifying a current risk assessment score of an entity to a target nsk assessment score, accesses an input attribute vector for the entity and clusters of entities defined by historical attribute vectors. The entity assessment server assigns the input attribute vector to a particular cluster and determines a requirement on movement from a first point to a second point in a multi-dimensional space based on the statistics computed from the particular cluster. The first point corresponds to the current risk assessment score and the second point corresponds to the target risk assessment score. The entity assessment server computes an attribute-change vector so that a path defined by the attribute-change vector complies with the requirement and generates the recommendation from the attribute-change vector.

Abstract: In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

Abstract: Certain aspects involve an automated recommendation for risk mitigation. An entity assessment server, responsive to a request for a recommendation for achieving a target status of a risk indicator, accesses a set of input attributes for the entity and obtains a quantity of available resource useable for modifying at least resource-dependent attribute values of the entity. The entity assessment server generates a resource allocation plan for the available resource according to a first risk assessment model and updates the set of input attribute values based on the resource allocation plan. The entity assessment server further determines an updated value of the risk indicator for the entity based on the updated set of input attribute values according to a second risk assessment model and generates the recommendation to include the resource allocation plan of the available resource if the updated value of the risk indicator achieves the target status.

Abstract: In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

Abstract: Systems and methods for automated path-based recommendation for risk mitigation are provided. An entity assessment server, responsive to a request for a recommendation for modifying a current risk assessment score of an entity to a target nsk assessment score, accesses an input attribute veclor for the entity and clusters of entities defined by historical attribute vectors. The entity assessment server assigns the input attribute vector to a particular cluster and determines a requirement on movement from a first point to a second point in a multi-dimensional space based on tire statistics computed from tltc particular cluster The first point corresponds to tire current risk assessment score and the second point corresponds to the target nsk assessment score. The entity assessment server computes an attribute-changc vecior so that a path defined by the attribute-change vector complies with the requirement and generates tlic recommendation from the attribute-changc vector.

Abstract: In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

Abstract: Disclosed is method and system for generating recommendations to a user. System receives real time data associated with users for scenarios and batch data associated with multiple users from different data sources, received from different data channels. The user is online user. System pre-processes batch data and real time data to generate pre-processed data and stores preprocessed data in distributed, scalable big data store. System filters pre-processed data based on rules to obtain filtered data. System applies combination of machine learning techniques on filtered data, based on the scenarios associated with the user, leveraging inter-play between machine learning techniques, to generate personalized recommendations for individual user and storing the personalized recommendations in distributed database. Machine learning techniques are customized to work in distributed processing mode to reduce overall processing time.

Abstract: In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

Abstract: In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

Abstract: Certain aspects produce a scoring model that can automatically classify future text samples. In some examples, a processing device perform operations for producing a scoring model using active learning. The operations includes receiving existing text samples and searching a stored, pre-trained corpus defining embedding vectors for selected words, phrases, or documents to produce nearest neighbor vectors for each embedding vector. Nearest neighbor selections are identified based on distance between each nearest neighbor vector and the embedding vector for each selection to produce a text cloud. Text samples are selected from the text cloud to produce seed data that is used to train a text classifier. A scoring model can be produced based on the text classifier. The scoring model can receive a plurality of new text samples and provide a score indicative of a likelihood of being a member of a selected class.

Abstract: In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

Abstract: In some aspects, a computing system can generate and optimize a neural network for risk assessment. The neural network can be trained to enforce a monotonic relationship between each of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under a monotonic constraint. This constrained optimization problem can be converted to an unconstrained problem by introducing a Lagrangian expression and by introducing a term approximating the monotonic constraint. Additional regularization terms can also be introduced into the optimization problem. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.

Abstract: Certain aspects produce a scoring model that can automatically classify future text samples. In some examples, a processing device perform operations for producing a scoring model using active learning. The operations includes receiving existing text samples and searching a stored, pre-trained corpus defining embedding vectors for selected words, phrases, or documents to produce nearest neighbor vectors for each embedding vector. Nearest neighbor selections are identified based on distance between each nearest neighbor vector and the embedding vector for each selection to produce a text cloud. Text samples are selected from the text cloud to produce seed data that is used to train a text classifier. A scoring model can be produced based on the text classifier. The scoring model can receive a plurality of new text samples and provide a score indicative of a likelihood of being a member of a selected class.

Abstract: A method and apparatus for forming a bonded joint on a composite structure is presented. An apparatus carrying a first workpiece is positioned relative to a second workpiece. A vacuum is applied to the first workpiece and a portion of the second workpiece. A bladder on a first surface of a housing of the apparatus is deflated. Deflating the bladder positions the first workpiece such that adhesive having a desired thickness is positioned between the first workpiece and the second workpiece. The adhesive positioned between the first workpiece and the second workpiece is cured.

Abstract: Disclosed is method and system for generating recommendations to a user. System receives real time data associated with users for scenarios and batch data associated with multiple users from different data sources, received from different data channels. The user is online user. System pre-processes batch data and real time data to generate pre-processed data and stores preprocessed data in distributed, scalable big data store. System filters pre-processed data based on rules to obtain filtered data. System applies combination of machine learning techniques on filtered data, based on the scenarios associated with the user, leveraging inter-play between machine learning techniques, to generate personalized recommendations for individual user and storing the personalized recommendations in distributed database. Machine learning techniques are customized to work in distributed processing mode to reduce overall processing time.

Abstract: A method and apparatus for forming a bonded joint on a composite structure is presented. An apparatus carrying a first workpiece is positioned relative to a second workpiece. A vacuum is applied to the first workpiece and a portion of the second workpiece. A bladder on a first surface of a housing of the apparatus is deflated. Deflating the bladder positions the first workpiece such that adhesive having a desired thickness is positioned between the first workpiece and the second workpiece. The adhesive positioned between the first workpiece and the second workpiece is cured.

Abstract: Disclosed are systems and methods of creating altered adhesive bonded joints between metal or composite substrates, including bonds that are weaker in strength than selected reference bonds. One method of creating an altered adhesive bond includes providing a first substrate and a second substrate, selecting a manufacturing process having a plurality of steps designed to produce a desired, or reference, adhesive bond having a desired strength, and selectively altering at least one of the plurality of steps during performance of the manufacturing process to produce an altered bond between the first substrate and the second substrate, the altered bond having an altered strength that is weaker than the strength of the desired adhesive bond. The systems may include systems that may be utilized to create the altered adhesive bonds and/or test standards.