International Business Machines Corporation

International Business Machines Corboration

Sharath C Athrey

Mohan Nagraj Dani

Balaji Rishi Sundarajan

Noor Mohammed Ashrafi

A computer-implemented system, method and computer program product for capturing data transitions and selecting machine learning models that includes: providing a machine learning model trained with a previous training data set; receiving a new data set; comparing the new data set to the previous data set; and identifying and recording new, removed, and/or changed set of attributes added to the new data set. Future possible data state transitions are generated based upon the present data state; and an implication tree is generated based upon the present data state, the pass-through data states, and the future possible data state transitions. Performance metrics of each node in the implication tree are clustered, the nodes demonstrating a high variance optionally are discarded; reachability scores for remaining nodes are calculated; and a node (representing a machine learning model to run) is selected based upon its reachability score.

System and method to capture data transitions and select optimal data intensive machine learning model

A plurality of liability factors of an asset is identified. Each liability factor impacts a total liability of the asset. A magnitude of a predetermined magnitude scale is identified for each of the plurality of liability factors according to a predetermined magnitude scale. Each magnitude is graphically encoded into a liability image for the asset. A risk score of the asset is determined by analyzing historical correlations between respective magnitudes of the plurality of liability factors using image recognition techniques on the liability image.

Asset assessment via graphical encoding of liability

An approach for training a machine learning model to detect market abuse patterns based on graphical images is disclosed. The approach comprises of creating trade event graphical images based on one or more rules and creating trade risk event graphical images represented by one or more horizontal lines and one or more vertical lines wherein the one or more vertical lines are based on unstructured data. The approach trains a model of a machine learning network to detect market abuse patterns based on the trade event graphical images and the trade risk event graphical images.

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Bengaluru, India

Sharath C Athrey