US 2026/0065291 A1Published application

System and method to address compliance coverage for trade surveillance

AssigneeNice Ltd.

InventorsNikhil RUDRAKAR, Girish DESHPANDE, Deep SHIKHA

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Abstract

Systems adapted to provide trade surveillance and compliance coverage and methods, and non-transitory computer readable media, include providing to a machine learning model, trained to output an indication of whether suspicious activity has occurred, input data comprising market data for a unique financial instrument; generating an output based on the input data, using the trained machine learning model, the output comprising a shape detection metric that identifies a suspicious activity; identifying a set of analytical models that correspond with the suspicious activity identified by the shape detection metric, wherein the set of analytical models is enabled once identified; analyzing the input data using the identified set of analytical models to confirm the suspicious activity identified by the shape detection metric; and triggering, based on confirmation of the suspicious activity identified by the shape detection metric, a suspicious activity alert.

Claims20

Dependent claims

A trade surveillance and compliance coverage system comprising:

a trade surveillance and compliance coverage computer system comprising at least one processor and a non-transitory computer readable medium operably coupled thereto, the non-transitory computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the at least one processor, to perform operations which comprise:
disabling a plurality of analytical models to save resources;
generating a trained shape detection machine learning model by training, using training data comprising market data for one or more financial instruments, associated flagged suspicious activity, associated user input, or a combination thereof, a machine learning model to output, based on the market data, an indication of whether suspicious activity has occurred, wherein training the shape detection machine learning model comprises modifying one or more weights of one or more nodes of an artificial neural network;
providing, to the trained shape detection machine learning model, input data comprising market data for a financial instrument traded in a transaction,
wherein the input data is a market data graph comprising market data for the traded financial instrument for a predetermined time period;
generating an output based on the input data, using the trained machine learning model, the output comprising a shape detection metric that identifies a particular suspicious trading activity;
identifying a set of the disabled analytical models that correspond with the particular suspicious trading activity identified by the shape detection metric,
wherein the set of disabled analytical models is automatically enabled once identified;
analyzing the input data using the enabled set of analytical models to confirm the particular suspicious trading activity identified by the shape detection metric;
triggering, based on confirmation of the particular suspicious trading activity identified by the shape detection metric, a suspicious activity alert;
and
correcting the particular suspicious trading activity.

9 more elements

A method of surveilling trades and providing compliance coverage, comprising, with a trade surveillance and compliance coverage computer system comprising at least one processor and a non-transitory computer readable medium operably coupled thereto:

disabling a plurality of analytical models to save resources;
generating a trained shape detection machine learning model by training, using training data comprising market data for one or more financial instruments, associated flagged suspicious activity, associated user input, or a combination thereof, a machine learning model to output, based on the market data, an indication of whether suspicious activity has occurred, wherein training the shape detection machine learning model comprises modifying one or more weights of one or more nodes of an artificial neural network;
providing, to the trained shape detection machine learning model, input data comprising market data for a financial instrument traded in a transaction,
wherein the input data is a market data graph comprising market data for the traded financial instrument for a predetermined time period;
generating an output based on the input data, using the trained machine learning model, the output comprising a shape detection metric that identifies a particular suspicious trading activity;
identifying a set of the disabled analytical models that correspond with the particular suspicious trading activity identified by the shape detection metric,
wherein the set of disabled analytical models is automatically enabled once identified;
analyzing the input data using the enabled set of analytical models to confirm the particular suspicious trading activity identified by the shape detection metric;
triggering, based on confirmation of the particular suspicious trading activity identified by the shape detection metric, a suspicious activity alert;
and
correcting the particular suspicious trading activity.

8 more elements

A non-transitory computer-readable medium having stored thereon computer-readable instructions executable by at least one processor to perform operations which comprise:

disabling a plurality of analytical models to save resources;
generating a trained shape detection machine learning model by training, using training data comprising market data for one or more financial instruments, associated flagged suspicious activity, associated user input, or a combination thereof, a machine learning model to output, based on the market data, an indication of whether suspicious activity has occurred, wherein training the shape detection machine learning model comprises modifying one or more weights of one or more nodes of an artificial neural network;
providing, to the trained shape detection machine learning model, input data comprising market data for a financial instrument traded in a transaction,
wherein the input data is a market data graph comprising market data for the traded financial instrument for a predetermined time period;
generating an output based on the input data, using the trained machine learning model, the output comprising a shape detection metric that identifies a particular suspicious trading activity;
identifying a set of the disabled analytical models that correspond with the particular suspicious trading activity identified by the shape detection metric,
wherein the set of disabled analytical models is automatically enabled once identified;
analyzing the input data using the enabled set of analytical models to confirm the particular suspicious trading activity identified by the shape detection metric;
triggering, based on confirmation of the particular suspicious trading activity identified by the shape detection metric, a suspicious activity alert;
and
correcting the particular suspicious trading activity.

8 more elements

Dependencies are inferred from the claim text.