Abstract: Disclosed is an approach for evaluating internal consistency of object classifications using machine learning modeling. In an example, models are iteratively trained, using supervised learning, on different majority segments (e.g., about 90%) of a dataset as training data segments. Trained models can be applied to the remaining data (e.g., about 10%) as test data segments to obtain, for each object, a predicted classification and a confidence score. Models in training iterations (e.g., 10 iterations) may be independently trained on substantially non-overlapping test data segments (with each iteration testing, e.g., about 10% of the dataset). When a model's predicted classification disagrees from a prior classification, and the confidence of the prediction is sufficiently high (indicating sufficiently strong disagreement), the object's prior classification may be revised. Training data, other than the data itself being evaluated for consistency, is not necessarily required.

Abstract: Disclosed are systems and method of mapping data entries originating in different systems. A plurality of data entries from different systems are normalized such that they can be compared to each other and mapped, even though the data entries are defined by data fields with differing phrases, descriptive details, and lengths of detail. Data entries may be filtered according to data fields before a mapping operation is employed for mapping. The mapping operation evaluates similarity scores based on the data fields using a combination of exact matching algorithms, dictionary matching algorithms, and text mining algorithms. The mapped data entries and data fields are displayed to a user.

Abstract: Disclosed are systems and method of mapping data entries originating in different systems. A plurality of data entries from different systems are normalized such that they can be compared to each other and mapped, even though the data entries are defined by data fields with differing phrases, descriptive details, and lengths of detail. Data entries may be filtered according to data fields before a mapping operation is employed for mapping. The mapping operation evaluates similarity scores based on the data fields using a combination of exact matching algorithms, dictionary matching algorithms, and text mining algorithms. The mapped data entries and data fields are displayed to a user.

Abstract: Quality associated with an interpretation of data captured as unstructured data can be determined. Attributes can be identified within the unstructured data automatically. Subsequently, sentiment associated with each of the attributes can be determined based on the unstructured data. Correctness of the unstructured data, and thus the interpretation, can be assessed based on a comparison of the attribute and associated sentiment with structured data. A quality score can be generated that captures the quality of the data interpretation in terms of correctness and as well as results of another analysis including completeness, among others. Comparison of the quality score to a threshold can dictate whether or not the interpretation is subject to further review.

Abstract: Automatic decisioning associated with unstructured data is disclosed. Unstructured data, such as that associated with comments of an underwriter regarding a credit decision, can be received. Text mining can be performed to extract features from the unstructured data. The extracted features can subsequently be provided as input to a machine learning model configured to return a prediction of a class associated with the unstructured data. The predicted class, such as approved or rejected, can subsequently be conveyed for display on a display device.