ACTIMIZE LTD. (Ra’anana, IL)

To detect suspicious or unauthorised financial transactions, a graph convolutional network to prevent financial crimes, a separate node is created for each person, account such as each account, each individual, every address (e.g. email address), etc. Additional attributes are available for aggregate transactions where the node is the sender where the node functions as a receiver that use a particular channel (e.g. ATM); and transactions that are of a certain kind (e.g. online money transfer). In certain embodiments, the attributes exclude data specific to transactions in order to minimize the amount of data required and hence provide more effective computer usage. This technique can be utilized for many purposes such as the prevention of money laundering. Additional options and systems to identify criminals are also offered.

Financial institutions–including banks, brokerage firms and insurance companies–have legal and business pressures to fight money laundering and other illegal activity. Financial institutions must report any suspicious activity that could be related to terrorist financing to the law. The relevant laws currently include the Bank Secrecy Act and the USA PATRIOT Act in the United States, the Third EU Directive in Europe, Articles on theCriminalization of money Laundering in Japan and other countries. As such, anti-money laundering (AML) compliance officers have to create and maintain an effective transaction monitoring program to keep up to date with the latest laws and manage their AML program’s costs. Reputational damage and fines could be incurred due to any errors.

Fraudulent transactions, such as transfer or withdrawals that are not authorized, may lead to large financial losses as well as reputational losses.

Authorities may need to be notified of suspicious patterns, actions and transactions in line with corporate regulations, local laws, and/or international and national laws. The reports must be submitted within a certain period of time, like 48 hours. 48 hours.

Financial institutions use computers to store financial data on financial transactions and also perform some transactions such as Electronic Fund Transfers (EFT), credit card transactions, and more. Computers are able to spot suspicious transactions and activity, as well as to identify unusual transactions.

In light of the massive amount of data that need to be processed to spot suspicious activity, and also the short deadlines dictated by law or requirements, improved computer techniques are sought to prevent financial crimes.

This section describes some aspects of the invention.

Certain embodiments of the disclosed disclosure employ methods of machine learning that are that are based on Artificial Neural Networks (ANN) to identify suspicious activity and identify the pertinent accounts, people and companies, as well as devices (e.g. computers, phones, NetworkInterface Cards, etc. ), addresses or other entities. In some embodiments, it is also possible to calculate the risk score for an entity that is associated with an illegal activity.

Machine learning (ML) is, as is widely known, can give reliable results for any problem. This depends on the Artificial Neural Network’s (ANN) kind and the data preparation. the kind of data input to the ANN. Additionally, machine learning algorithms aren’t always efficient if the ANN algorithms are complicated and the volume of data being processed is huge. In the field of financial crime prevention the ability to detect suspicious activity quickly is crucial to stop illicit flow of cash or othervaluables. It is also important in training the system to make sense of shifting legal, political and business landscapes. High processing speed and reliable results could be two different goals. This is because more specific data could be required, and thus need a slower processing.

The creators of the technique found out that Graph Convolutional Neural Networks can be employed to improve accuracy and speed, if that the input information for financial transactions is properly prepared. The technique employs graphs to depict financial data. GCN graphs are arranged in such a manner that certain data is linked with graph nodes (vertices) as well as other data is linked to graph edges. GCN performance and reliability is affected by the type of data that is associated with each edge or node. It is important to decide on the type of information you would like to link to each node. In certain documents, edges are used to represent transactions between entities and nodes represent entities. It is also possible to use multiple nodes to represent the related details about an entity. In some instances the account’s entity is represented with only one node. The account’s owner, however, is represented using an additional node. An edge is used to indicate the ownership relationship between the two nodes. A family or business relationship between two parties may be also represented with an edge. Certain embodiments employ separate nodes to indicate accounts and other nodes for parties. They can also be used to represent devices, addresses or other details. An address can either be a full street address, or it could be a portion of it (e.g. a country name and/or a city name and/or a portion). An address can also be an IP address, an email address or a layer-2 address, or some other address recognized in computer networks. This representation is thought to enhance GCN reliability for many GCN types, according to the current information.

In some embodiments, information could be duplicated in various node types. For example, an account owner’s name could be provided both in the owner’s node as well as in the account’s node. In other cases, the data isn’t duplicated: the account owner, the device as well as the device. are described and identified only in the relevant nodes.

In some instances, the financial data are processed before being passed to the GCN. Aggregation is used to decrease the volume of data processed by the GCN and thus to increase processing speed, while decreasing memoryrequirements. Generally, aggregation results in the loss of data, which can reduce the reliability of the result. Information that is not essential should be removed and essential information preserved for the detection of suspicious entities. In some embodiments of aggregation according to the present disclosure, the amount of money derived from the individual transactions that are performed over an aforementioned time period are grouped into averages and/or medians, maxima, minima, and/or any other aggregated value. For a given entity (e.g. account) Separate aggregation is performed for transactions in which the entity is a source of transactions and separate aggregation is used for transactions in which the entity is a destination. Additionally, separate aggregation can be performed for each method of transaction (e.g. online banking, ATM, etc.) and/or every type of transaction (e.g. and/or each type of transaction (e.g.) which involves the entity. The aggregation technique is able to preserve the essential information to give more accurate results.

The invention is not limited in its application to the advantages or characteristics that are described above. Furthermore, certain embodiments utilize one or more , but not all of the methods described above. In certain embodiments however, entities and nodes can be described in the same manner as described above, however the aggregation does not happen at the source or in the destination. In other examples, the aggregation process is carried out as described above, but the nodes are defined in a more straightforward manner, e.g. Each account is associated with an independent node. However the account owners as well as other information are not kept separately, but are instead stored as attributes of each account node. Other embodiments and variations are within the range of the invention as described in the disclosure in this document.

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