Citibank, N.a.

Ines Teixeira

John E Ortega, Jr

Jason Ryan Engelbrecht

Alberto Cetoli

Yael Man

Minjeong Cho

James Myers

William Franklin Cameron

Ganesh Prasad Bhat

Wei Jie Ng

Subramanian Sankaran

Ramee S Karthikeyan

Alex Michael Eng

Cameron Paul Lim

Matthew Ryan Mitsui

Ryan Bergeron

Systems and methods are disclosed comprising instructions to receive a request to evaluate authorization of a development service that comprises a digital artifact set, each digital artifact in the digital artifact set, access an authorization schema set available for the development service, identify an applicable authorization schema from the authorization schema set via comparing the content embeddings of the digital artifacts and the reference embeddings of the authorization schemas, retrieve a historical artifact attribute set representing tracked development actions for prior development services authorized via the applicable authorization schema, predict an authorization status for the development service using the historical artifact attribute set and the artifact attribute set, configure for display a visual representation of the applicable authorization schema and the mapped at least one digital artifact of the development service.

Robust artifacts mapping and authorization systems and methods for operating the same

The systems and methods disclosed herein receive a dataset including an observed set of values for a set of variables. The system can use a first set of AI models to identify a set of anomalies in the observed set of values by comparing an observed set of patterns against multiple reference patterns. The system can use a second set of AI models to evaluate the identified anomalies by comparing an observed set of association rules with an expected set of association rules. The system can use a third set of AI models to generate reconfiguration commands to remove the identified anomalies. The reconfiguration commands can be automatically executed to modify the observed association rules to align with the expected association rules.

Detecting data anomalies using artificial intelligence

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London, United Kingdom

Ines Teixeira