Nice Ltd

Nice Systems Ltd.

Yoav Avneon

Pinchas Ben-Or

Diana Shnaider

Simon Robins

Shlomi Cohen-Ganor

System and method for generating case-based data including receiving, input data describing an event of interest; if the input data is not in a format of a property graph then transforming the input data into a first property graph describing the event of interest and representing a first network, wherein the first property graph includes a plurality of network elements and properties of at least some of the plurality of network elements, wherein the network elements include entities and links describing relationships between the entities; changing a network element in the first property graph to create a second property graph of a new network; and using the second property graph as the case-based data. New properties may be generated for the entities and links.

System and method for generation of case-based data for training machine learning classifiers

A computer implemented method and system for optimization of model parameters of at least one predictive model for detecting suspicious financial activity. The processor may select a reduced set of key indicators and corresponding scores to optimize from each of the at least one predictive model, each key indicator and corresponding score in the reduced set having an influence ranking above a predetermined influence ranking. The processor may select a best performing model candidate based on an evaluation of each reduced set of key indicators and corresponding scores. The processor may preform gradient-ascent optimization on the best performing model candidate and the at least one random model to generate a set of at least two new models for each of the best performing model candidate and the at least one random model. The processor may select the new model with the highest performance ranking.

Method for adaptive tuning via automated simulation and optimization

A system or method may include receiving, by a processor, data describing a network, wherein the network includes a plurality of entities and links describing relationships between the plurality of entities. The method may further include identifying a set of seed entities from the plurality of entities based on predefined rules. The method may further include generating a set of sub-networks based on the set of identified seed entities, wherein each of the sub-networks may include one or more other entities of the plurality of entities having at least one link to the at least one seed entity. The method may further include calculating a risk score for each of the generated sub-networks.

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Nes-Ziona, Israel

Yoav Avneon