Wells Fargo Bank, N.a.

Manish Pandey

Carmel Nadav

Rameshchandra Bhaskar Ketharaju

Shanmukeswara Rao Donkada

Connor Patrick Jennings

Mahima Rawat

Prabal Nandi

Jie Chen

The innovation disclosed and claimed herein, in one aspect thereof, comprises systems, methods and computer program products that enable transactions requested by a user using a trade asset card. The innovation enables the transactions to be completed in real time or near real time per pre-determined rules and settings, or audited changes to rules and settings, while staying perpetually invested in the user's investment portfolio.

Trade asset card

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

Growing community of inventors

Bengaluru, India

Manish Pandey