Abstract: Embodiments provide electronic methods and systems for improving edge case classifications. The method performed by a server system includes accessing an input sample dataset including first labeled training data associated with a first class, and second labeled training data associated with a second class, from a database. Method includes executing training of a first autoencoder and a second autoencoder based on the first and second labeled training data, respectively. Method includes providing the first and second labeled training data along with unlabeled training data accessed from the database to the first and second autoencoders. Method includes calculating a common loss function based on a combination of a first reconstruction error associated with the first autoencoder and a second reconstruction error associated with the second autoencoder. Method includes fine-tuning the first autoencoder and the second autoencoder based on the common loss function.

Abstract: Embodiments provide methods and systems for predicting overall account-level risks of cardholders. The method performed by server system includes accessing payment transaction data associated with a cardholder from a transaction database. Method includes generating a set of transaction features based on a set of transaction indicators. The method includes determining a plurality of network risk scores associated with the cardholder based on the set of transaction features and a set of trained machine learning models. The plurality of network risk scores includes a payment capacity risk score, a contactless payment risk score, and a set of account-level risk scores. The method includes aggregating the plurality of network risk scores to calculate an overall account risk score associated with the cardholder based on a statistical model. The method also includes transmitting a notification to the issuer server associated with the cardholder based on the overall account risk score.

Abstract: Embodiments provide methods and systems of predicting server failures. A method may include accessing distinct log clusters representing instructions executed in server, applying first density machine learning model over input vector of distinct log clusters, with length equal to number of distinct log clusters, for obtaining first prediction output, applying first sequential machine learning model over time length sequence of distinct log clusters for obtaining second prediction output, applying second density machine learning model over input vector for obtaining third prediction output, applying second sequential machine learning model over time length sequence of distinct log clusters for obtaining fourth prediction output, aggregating first, second, third and fourth prediction outputs by ensemble model, and predicting likelihood of next log clusters to have anomalous behavior based on the aggregating. First density and first sequential models are trained by normal logs.