Abstract: This invention relates generally to the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems. Preferred embodiments of the present invention provide systems and methods for automatically generating high quality adverse action notifications based on identifying variations between a declined borrower's profile and that of approved applicants, both with simple and sophisticated credit scoring systems, using specific algorithms.

Abstract: Systems and methods for generating and processing modeling workflows are disclosed. In some examples, a reference distribution of scores generated by a model is determined. The reference distribution of scores is recorded in a structured database. One or more unexpected scores are detected during execution of the model. To detect the one or more unexpected scores, a production distribution of scores is compared with the reference distribution of scores recorded in the structured database. The production distribution of scores is generated by the model for a production input data set. An alert is then provided to an external system, when an alert condition is determined to be satisfied based on the comparison. The alert indicates detection of the one or more unexpected scores.

Abstract: Systems and methods for training models to improve fairness.

Abstract: Methods, non-transitory computer readable media, and model evaluations systems for understanding diverse machine learning models (MLMs) are disclosed. In some examples, a feature contribution value is determined for features included in a reference or evaluation input data set. The evaluation input data set represents a protected class population and each feature contribution value identifies a contribution by a feature to a difference in output generated by an MLM for the evaluation input data set. Model explanation information is generated using the feature contribution values and execution of the MLM is monitored. The model explanation information explains the difference in output generated by the MLM for the evaluation input data set and includes information relating to a model-based decision. A report is generated from a knowledge graph for the MLM and output via a GUI to an operator device that includes the model explanation information.

Abstract: This technology relates generally to data assembly and analytics, as can be used in the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems. Embodiments of this technology provide systems and methods for creating objects which can be used in multiple implementations to generate scores.

Abstract: Systems and methods for generating tree-based models with improved fairness are disclosed. The disclosed process generates a first tree-based machine learning model, which is preferably trained to predict if a financial loan will be repaid. The process also determines an accuracy of the first tree-based machine learning mode. In addition, the process determines a fairness of the first tree-based machine learning model. The fairness is preferably associated with at least one of gender, race, ethnicity, age, marital status, military status, sexual orientation, and disability status. The process then generates a second different tree-based machine learning model, which is preferably trained based on the accuracy of the first tree-based machine learning model and the fairness of the first tree-based machine learning model. The process then combines the first tree-based machine learning model and the second tree-based machine learning model to produce a gradient-boosted machine learning model.

Abstract: This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features.

Abstract: Systems and methods for model evaluation. A model is evaluated by performing a decomposition process for a model output, relative to a baseline input data set.

Abstract: Systems and methods for model evaluation. A protected class model that satisfies an accuracy threshold is built by using: data sets for use by a modeling system being evaluated, and protected class membership information for each data set. A target for the protected class model is a protected class membership variable indicating membership in a protected class. Each predictor of the protected class model is a predictor of an evaluated model used by the modeling system. A target of the evaluated model is different from the target of the protected class model. Each predictor is a set of one or more variables of the data sets. For each predictor of the protected class model, a protected class model impact ranking value and a modeling system impact ranking value are determined.

Abstract: Systems and methods for training models to improve fairness.

Abstract: Systems and methods for generating and processing modeling workflows.

Abstract: Systems and methods for understanding diverse machine learning models.

Abstract: This invention relates generally to data assembly and analytics, as can be used in the personal finance and banking field, and more particularly to the field of lending and credit notification methods and systems. Preferred embodiments of the present invention provide systems and methods for creating objects which can be used in multiple implementations to generate scores.

Abstract: Systems and methods for generating tree-based models with improved fairness are disclosed. The disclosed process generates a first tree-based machine learning model, which is preferably trained to predict if a financial loan will be repaid. The process also determines an accuracy of the first tree-based machine learning mode. In addition, the process determines a fairness of the first tree-based machine learning model. The fairness is preferably associated with at least one of gender, race, ethnicity, age, marital status, military status, sexual orientation, and disability status. The process then generates a second different tree-based machine learning model, which is preferably trained based on the accuracy of the first tree-based machine learning model and the fairness of the first tree-based machine learning model. The process then combines the first tree-based machine learning model and the second tree-based machine learning model to produce a gradient-boosted machine learning model.

Abstract: Systems and methods for generating explanation information for a result of an application system. Explanation configuration is generated based on received user input. Responsive to an explanation generation event, a plurality of modified input variable value sets are generated for a first applicant by using the explanation configuration. For each modified input variable value set: a request is provided to a first application system for generation of a result for the modified input variable value set, and a result is received for the modified input variable value set. At least one input variable value is selected based on a comparison between a first result of a first input variable value set of the first applicant and results for the modified input variable value set. Explanation information is generated for the first result by using human-readable description information for each selected input variable value, in accordance with the explanation configuration.

Abstract: Systems and methods for generating tree-based models with improved fairness are disclosed. The disclosed process generates a first tree-based machine learning model, which is preferably trained to predict if a financial loan will be repaid. The process also determines an accuracy of the first tree-based machine learning mode. In addition, the process determines a fairness of the first tree-based machine learning model. The fairness is preferably associated with at least one of gender, race, ethnicity, age, marital status, military status, sexual orientation, and disability status. The process then generates a second different tree-based machine learning model, which is preferably trained based on the accuracy of the first tree-based machine learning model and the fairness of the first tree-based machine learning model. The process then combines the first tree-based machine learning model and the second tree-based machine learning model to produce a gradient-boosted machine learning model.

Abstract: Systems and methods for converting a data item provided by an external data provider system into a data type specified by a data processing system for a data field of the data item. A data processing system stores a coercion rule for each data field of a first data set provided by the data provider system. Each stored coercion rule identifies at least one data type for the corresponding data field. Responsive to a second data set provided by the data provider system, the data processing system coerces each data item of the second data set into at least one data type specified by the stored coercion rule for the data field of the data item to generate at least one converted data item of the second data set. The data processing system generates information from at least one converted data item, and provides the information to a consuming system.

Abstract: Systems and methods for a multi-tenant parser generation platform. A human-readable document of a data provider system is accessed. The document includes a data dictionary table for opaque data of the data provider system that has a first type. Data dictionary information of the data dictionary table is extracted, and a schema file is generated from the extracted information. The schema file defines a parsing process for parsing an opaque data record of the first. The first schema file specifies each field of the opaque data record of the first type. Parsing instructions are generated based on the schema file. The parsing instructions are for parsing an opaque data record of the first type provided by the data provider system into a set of data fields. The parsing instructions are provided to an entity system external to the platform. The first entity system is associated with a first platform account.

Abstract: Systems and methods for augmenting data by performing reject inference are disclosed. In one embodiment, the disclosed process trains an auto-encoder based on a subset of known labeled rows (e.g., non-default loan applicants). The process then infers labels for unlabeled rows using the auto-encoder (e.g., label some rows as non-default and some as default). The process then trains a machine learning model based on the known labeled rows and the inferred labeled rows. Applicant data is then processed by this new machine learning model to determine if a loan applicant is likely to default. If the loan applicant is not likely to default, the loan applicant is funded. For example, the loan applicant may be mailed a physical working credit card. However, if the loan applicant is likely to default, the loan applicant is rejected. For example, the loan applicant may be mailed a physical adverse action letter.

Abstract: Systems and methods for model explanation are disclosed. In one embodiment, the disclosed process determines a score based on a scoring function and a plurality of values associated with a plurality of features of a denied credit applicant. (e.g., credit score of 550, no loans repaid, etc.). The process then determines a score of an approved credit applicant. (e.g., credit score of 750, 3 loans repaid, etc.). A next differential credit assignment associated with the current denied/approved pair is then calculated. If a convergence stopping criteria, (e.g., current accuracy>99% based on a statistical t-distribution) is not satisfied, the process repeats for a different approved credit applicant. When the convergence stopping criteria is satisfied, explanation information is generated. For example, the explanation information may include an adverse action reason code, fairness metric, disparate impact metric, human readable text, feature importance metric, credit value, and/or an importance rank.