Wells Fargo Bank, N.a.

Carmel Nadav

Manish Pandey

Connor Patrick Jennings

Mahima Rawat

Jie Chen

Zachary Washam

Systems, apparatuses, methods, and computer program products are disclosed for selecting a classification category based on query data. An example method includes receiving query data and requesting a set of classification categories from a set of classification engines. The example method further includes receiving the set of classification categories and, if a leading classification category occurs, selecting a classification category that includes the first leading classification category. The example method further includes, if the leading classification category does not occur, forming a set of partial-digits categories by removing a first pre-determined number of digits from each classification category, and if a first leading partial-digits category from the first set of partial-digits categories occurs, selecting a second classification category consistent with the first leading partial-digits category. The example method further includes selecting a ranked priority classification category based on the priority rank for each classification engine.

Systems and methods for automated rule-based annotation and validation of business classification

The concept involves efficiently using machine learning to quickly identify possible fraudulent applications in small business loan and credit applications by automatically flagging applications that meet certain criteria. In one preferred implementation, the tool compares a business description to a selected NAICS code in a loan application to assess the potential for fraud. Specifically, an algorithm can match the leftmost two digits of the selected code with the description of the category from an applicant. An engine calculates a probability of a fraud score based on the matching attached to the application. Because the tool detects fraud proactively rather than reactively, it substantially reduces computational costs and resources and reduces the biases associated with highly intensive manual work.

Detecting fraud in credit applications

Various examples are directed to systems and methods for visualizing computerized model decisions. A model system may access result data that comprises event results for a plurality of events and event feature data describing sets of event features. A first event result for a first event of the plurality of events is generated using the computerized model and a first set of event features from the event feature data. The model system may access importance score data comprising importance scores for at least a portion of the first set of event features, wherein a first event feature is assigned a first importance score and a second event feature is assigned a second importance score indicating a greater importance than the first importance score. The model system may also generate a user interface for display to a human user. The user interface may comprise a background area; a first visual indicator positioned on the background area and corresponding to the first event feature. A size of the first visual indicator may correspond to the first importance score. A second visual indicator may be positioned on the background area and corresponding to the second event feature. A size of the second visual indicator corresponds to the second importance score and is larger than the size of the first visual indicator. The model system may display the user interface at a display screen of a computing device.

Anomaly visualization for computerized models

Techniques are described for evaluating message content in each of a plurality of messages in order to improve communication between customers and customer service representatives. The disclosed techniques enable efficient evaluation of the message content in each of the messages in order to determine a readability score for each of the messages. A computing device performs the efficient evaluation by processing the message content into a binary representation using a first value to represent vowels and a second value to represent non-vowels. The binarization of the message content allows the computing device to perform a syllable count and determine a readability score for each of the plurality of messages, which may include millions of messages. Based on the readability scores, the computing device may identify readability gaps between the representative messages and the customer messages, and generate subsequent representative messages that reduce the identified readability gaps.

Analytical tool for evaluation of message content

This disclosure describes techniques that include predicting a business metric associated with a given business enterprise using machine learning algorithms that have been trained on data, from other businesses, about that same business metric. In one example, this disclosure describes a method that includes selecting, from a data set, a training data subset and a test data subset; generating a plurality of candidate models configured to generate a predicted output metric, wherein the plurality of candidate models is generated by applying a plurality of machine learning algorithms to the training data subset; evaluating, using the test data subset, each of the plurality of candidate models to select a chosen model; receiving production data that includes data representing input metrics for a business entity not included within the plurality of business entities; and generating a predicted output metric for the business entity.

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Eagan, MN, United States of America

Carmel Nadav