Abstract: 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.

Abstract: Embodiments for analyzing language content are provided. An ambiguous sentence is identified. A first word and a second word within the ambiguous sentence are selected. At least one synonym outlier for the first word and at least one synonym outlier for the second word are determined. A phrase is generated utilizing the at least one synonym outlier for the first word and the at least one synonym outlier for the second word. A context for the ambiguous sentence is determined based on the generated phrase.

Abstract: Three lattice that includes nodes of liability factors across time are generated. The first lattice includes liability factors related to an asset, the second lattice includes liability factors related to an asset owner across time, and the third lattice includes liability factors related to economic parameters. The three lattices are combined to generate a single polynomial lattice. Liability scenarios of the asset are simulated by capturing traversal of polynomial lattice. A risk score of the asset is determined by analyzing the simulated liability scenarios.

Abstract: 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.

Abstract: 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.

Abstract: A plurality of liability events of an asset is identified. The plurality of liability events impacts an economic liability of the asset. Different subsets of the plurality of liability events chain together in different causal timelines of the asset. A group of liability events to exclude from analysis is identified based on a likelihood of the group of liability events impacting the economic liability being lower than a threshold. Remaining liability events are analyzed. A causal timeline of liability events that has a least amount of economic liability is identified via the analysis.

Abstract: 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.

Abstract: A method, computer system, and a computer program product for complaint identification is provided. The present invention may include processing one or more sentences of a received communication. The present invention may include determining that at least one phrase of the processed sentence has a negative status. The present invention may include contextualizing the at least one phrase of the processed sentence. The present invention may include identifying a complaint within the at least one phrase of the processed sentence.

Abstract: 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.

Abstract: Embodiments for analyzing language content are provided. An ambiguous sentence is identified. A first word and a second word within the ambiguous sentence are selected. At least one synonym outlier for the first word and at least one synonym outlier for the second word are determined. A phrase is generated utilizing the at least one synonym outlier for the first word and the at least one synonym outlier for the second word. A context for the ambiguous sentence is determined based on the generated phrase.