1. A system to monitor a financial transaction to determine a money laundering event, the system comprises: a first computing device that is arranged to receive a customer registration information from a user; a second computing device that is arranged to receive a customer transaction history data and a current transaction information, from a customer; a network interface; and a remote server that comprises: a non-transitory storage device that is arranged to store a set of executable routines; and a microprocessor which is coupled to the non-transitory storage device and operable to execute the set of routines is arranged to: acquire the customer registration information from the first computing device; receive the customer transaction history data and the current transaction information, from the second computing device; analyse the acquired customer registration information to generate a first set of risk scores; analyse the received customer transaction data to generate a second set of risk scores; combine the generated first set of risk scores and the second set of risk scores to generate a total risk score; analyse the generated total risk score to determine a customer monitoring level; monitor the received current transaction information based on the determined customer monitoring level to identify a transaction pattern; evaluate the identified transaction pattern to determine the money laundering event; and transmit to the first computing device, an alert notification, based on the determined money laundering event.

2. The system as claimed in claim 1, wherein the customer registration information is selected from a customer identity, a customer type, a residential status, a nationality detail, a demographic data and a national registration detail.

3. The system as claimed in claim 1, wherein the microprocessor determines the first set of risk scores and the second set of risk scores by applying a machine learning model.

4. The system as claimed in claim 1, wherein the microprocessor transmits an alert message to the first computing device, if the current transaction information pertains to a financial transaction more than a pre-set monitory value.

5. The system as claimed in claim 1, wherein the microprocessor blocks an account of the customer based on the determined money laundering event.

6. A method for monitoring a financial transaction to determine and prevent a money laundering event, the method comprises: receiving, at a first computing device, a customer registration information from a user; receiving, at a second computing device, a customer transaction history data and a current transaction information, from a customer; acquiring, the customer registration information from the first computing device; receive the customer transaction history data and the current transaction information, from the second computing device; analyzing the acquired customer registration information to generate a first set of risk scores; analyzing the received customer transaction data to generate a second set of risk scores; combine the generated first set of risk scores and the second set of risk scores to generate a total risk score; analyzing the generated total risk score to determine a customer monitoring level; monitoring the received current transaction information based on the determined customer monitoring level to identify a transaction pattern; evaluating the identified transaction pattern to determine the money laundering event; and transmitting to the first computing device, an alert notification, based on the determined money laundering event.

7. The method as claimed in claim 6, wherein the customer registration information is selected from a customer identity, a customer type, a residential status, a nationality detail, a demographic data and a national registration detail.

8. The method as claimed in claim 6, wherein the first set of risk scores and the second set of risk scores by applying a machine learning model.

9. The method as claimed in claim 6, further comprising step of transmitting an alert message to the first computing device, if the current transaction information pertains to a financial transaction more than a pre-set monitory value.

10. The method as claimed in claim 6, further comprising step of blocking an account of the customer based on the determined money laundering event. SMART AUTONOMOUS SYSTEM FOR DETECTING MONEY LAUNDERING ACTIVITIES Abstract In certain implementations, a customer's registration details are collected on a first computer device that is part of a system that monitors financial transactions to identify money laundering events. A customer's transaction history data and real-time transaction details may be received by a second computer in certain embodiments. Networking components are sometimes included into embodiments. One embodiment might also make use of a remote server, which could have a non-volatile storage device built up specifically to house a collection of executable instructions. A microprocessor, which is attached to the non-transitory storage device and can run the set of procedures needed to get the customer registration data from the first computer, is also possible. s:

1. A system to monitor a financial transaction to determine a money laundering event, the system comprises: a first computing device that is arranged to receive a customer registration information from a user; a second computing device that is arranged to receive a customer transaction history data and a current transaction information, from a customer; a network interface; and a remote server that comprises: a non-transitory storage device that is arranged to store a set of executable routines; and a microprocessor which is coupled to the non-transitory storage device and operable to execute the set of routines is arranged to: acquire the customer registration information from the first computing device; receive the customer transaction history data and the current transaction information, from the second computing device; analyse the acquired customer registration information to generate a first set of risk scores; analyse the received customer transaction data to generate a second set of risk scores; combine the generated first set of risk scores and the second set of risk scores to generate a total risk score; analyse the generated total risk score to determine a customer monitoring level; monitor the received current transaction information based on the determined customer monitoring level to identify a transaction pattern; evaluate the identified transaction pattern to determine the money laundering event; and transmit to the first computing device, an alert notification, based on the determined money laundering event.

2. The system as claimed in claim 1, wherein the customer registration information is selected from a customer identity, a customer type, a residential status, a nationality detail, a demographic data and a national registration detail.

3. The system as claimed in claim 1, wherein the microprocessor determines the first set of risk scores and the second set of risk scores by applying a machine learning model.

4. The system as claimed in claim 1, wherein the microprocessor transmits an alert message to the first computing device, if the current transaction information pertains to a financial transaction more than a pre-set monitory value.

5. The system as claimed in claim 1, wherein the microprocessor blocks an account of the customer based on the determined money laundering event.

6. A method for monitoring a financial transaction to determine and prevent a money laundering event, the method comprises: receiving, at a first computing device, a customer registration information from a user; receiving, at a second computing device, a customer transaction history data and a current transaction information, from a customer; acquiring, the customer registration information from the first computing device; receive the customer transaction history data and the current transaction information, from the second computing device; analyzing the acquired customer registration information to generate a first set of risk scores; analyzing the received customer transaction data to generate a second set of risk scores; combine the generated first set of risk scores and the second set of risk scores to generate a total risk score; analyzing the generated total risk score to determine a customer monitoring level; monitoring the received current transaction information based on the determined customer monitoring level to identify a transaction pattern; evaluating the identified transaction pattern to determine the money laundering event; and transmitting to the first computing device, an alert notification, based on the determined money laundering event.

7. The method as claimed in claim 6, wherein the customer registration information is selected from a customer identity, a customer type, a residential status, a nationality detail, a demographic data and a national registration detail.

8. The method as claimed in claim 6, wherein the first set of risk scores and the second set of risk scores by applying a machine learning model.

9. The method as claimed in claim 6, further comprising step of transmitting an alert message to the first computing device, if the current transaction information pertains to a financial transaction more than a pre-set monitory value.

10. The method as claimed in claim 6, further comprising step of blocking an account of the customer based on the determined money laundering event.

Field of the Invention
[0001] The present invention relates generally to a field of system and method for monitoring of monitory transection to enable automatic detection of money laundering event.

Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] The expansion of Internet technology into the financial sector has been made possible by the quick development of computing technologies. "Internet finance" refers to the numerous categories of financial services offered online (such as third-party payments, peer-to-peer lending, crowdfunding, online banking, online money market fund distribution, online insurance, and online brokerage, etc.). Internet finance may improve capital provisioning, open up new channels for financial services, reduce transaction costs, streamline transaction processes, correct flaws in traditional finance, and satisfy a variety of customer requests.
[0004] The Internet's varied attributes, such as its timeliness, convenience, and anonymity, can, however, also create the right conditions for criminal conduct, including online money laundering. Money laundering has recently moved from conventional payment methods (such conventional bank transactions) to those offered by Internet finance. Criminals are increasingly exploiting internet payment services to launder money.
[0005] Various technological solutions for detecting online money laundering (e.g., method, device, and system of detecting mule accounts and accounts used for money laundering, automated money laundering detection, notification, and reporting techniques implemented at casino gaming networks, etc.) are disclosed in patent literature. Few of the exemplary documents are discussed below.
[0006] The CN106960500A (By- Shenzhen Yihua Computer Co Ltd, Shenzhen Yihua Time Technology Co Ltd, Shenzhen Yihua Financial Intelligent Research Institute) relates to a kind of method, terminal device, server and system examined for security credence. Wherein, method includes:Obtain the first identification information of security credence;First identification information is sent to server, so that the server obtains the historical data of the security credence according to first identification information;Receive the server and the whether abnormal feedback information of the security credence is determined according to the historical data, and show the feedback information. Technical scheme provided in an embodiment of the present invention, it is low to solve existing false distinguishing equipment false distinguishing level, easily makes the problem of user's property is damaged.
[0007] The US10762744B2 (By- Fresh Idea Global Ltd, Aristocrat Technologies Inc) relates to different methods, systems, and computer program products for implementing automated money laundering detection, notification, and reporting techniques implemented at casino gaming networks.
[0008] The US10685355B2 (By- BioCatch Ltd) relates to method, device, and system of detecting a mule bank account, or a bank account used for terror funding or money laundering. A method includes: monitoring interactions of a user with a computing device during online access with a banking account; and based on the monitoring, determining that the online banking account is utilized as a mule bank account to illegally receive and transfer money. The method takes into account one or more indicators, such as, utilization of a remote access channel, utilization of a virtual machine or a proxy server, unique behavior across multiple different account, temporal correlation among operations, detection of a set of operations that follow a pre-defined mule account playbook, detection of multiple incoming fund transfers from multiple countries that are followed by a single outgoing fund transfer to a different country, and other suitable indicators.
[0009] However, the technological solutions for detecting online money laundering suffers from various limitations such as, inaccuracy, delay in detection, etc. Thus, there remains a need for further contributions in this area of technology. More specifically, a need exists in the area of technology to detect an online money laundering activity.
[00010] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

Summary of the Invention
[00011] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00012] The present invention relates generally to a field of system and method for monitoring of monitory transection to enable automatic detection of money laundering event
[00013] Embodiments of the present disclosure may include a system to monitor a financial transaction to determine a money laundering event, the system may include a first computing device that may be arranged to receive a customer registration information from a user. Embodiments may also include a second computing device that may be arranged to receive a customer transaction history data and a current transaction information, from a customer.
[00014] Embodiments may also include a network interface. Embodiments may also include a remote server that may include a non-transitory storage device that may be arranged to store a set of executable routines. Embodiments may also include a microprocessor which may be coupled to the non-transitory storage device and operable to execute the set of routines may be arranged to acquire the customer registration information from the first computing device.
[00015] Embodiments may also include receive the customer transaction history data and the current transaction information, from the second computing device. Embodiments may also include analyse the acquired customer registration information to generate a first set of risk scores. Embodiments may also include analyse the received customer transaction data to generate a second set of risk scores.
[00016] Embodiments may also include combine the generated first set of risk scores and the second set of risk scores to generate a total risk score. Embodiments may also include analyse the generated total risk score to determine a customer monitoring level. Embodiments may also include monitor the received current transaction information based on the determined customer monitoring level to identify a transaction pattern. Embodiments may also include evaluate the identified transaction pattern to determine the money laundering event. Embodiments may also include transmit to the first computing device, an alert notification, based on the determined money laundering event.
[00017] In some embodiments, the customer registration information may be selected from a customer identity, a customer type, a residential status, a nationality detail, a demographic data and a national registration detail. In some embodiments, the microprocessor determines the first set of risk scores and the second set of risk scores by applying a machine learning model. In some embodiments, the microprocessor transmits an alert message to the first computing device if the current transaction information pertains to a financial transaction more than a pre-set monitory value. In some embodiments, the microprocessor blocks an account of the customer based on the determined money laundering event.
[00018] Embodiments of the present disclosure may also include a method for monitoring a financial transaction to determine and prevent a money laundering event, the method may include receiving, at a first computing device, a customer registration information from a user. Embodiments may also include receiving, at a second computing device, a customer transaction history data and a current transaction information, from a customer.
[00019] Embodiments may also include acquiring, the customer registration information from the first computing device. Embodiments may also include receive the customer transaction history data and the current transaction information, from the second computing device. Embodiments may also include analysing the acquired customer registration information to generate a first set of risk scores.
[00020] Embodiments may also include analysing the received customer transaction data to generate a second set of risk scores. Embodiments may also include combine the generated first set of risk scores and the second set of risk scores to generate a total risk score. Embodiments may also include analysing the generated total risk score to determine a customer monitoring level.
[00021] Embodiments may also include monitoring the received current transaction information based on the determined customer monitoring level to identify a transaction pattern. Embodiments may also include evaluating the identified transaction pattern to determine the money laundering event. Embodiments may also include transmitting to the first computing device, an alert notification, based on the determined money laundering event.
[00022] In some embodiments, the customer registration information may be selected from a customer identity, a customer type, a residential status, a nationality detail, a demographic data and a national registration detail. In some embodiments, the first set of risk scores and the second set of risk scores by applying a machine learning model. In some embodiments, the method as claimed may include step of transmitting an alert message to the first computing device, if the current transaction information pertains to a financial transaction more than a pre-set monitory value. In some embodiments, the method as claimed may include step of blocking an account of the customer based on the determined money laundering event.
[00023] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

Brief Description of the Drawings
[00024] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00025] FIG. 1 is a block diagram illustrating a smart autonomous system for detecting money laundering activities, according to some embodiments of the present disclosure.
[00026] FIG. 2A is a flowchart illustrating a method for monitoring a financial transaction, according to some embodiments of the present disclosure.
[00027] Figure 2B is a flowchart extending from figure 2A and further illustrating the smart autonomous method for detecting money laundering activities, according to some embodiments of the present disclosure.

Detailed Description
[00028] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00029] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00030] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00031] The present invention relates generally to a field of system and method for monitoring of monitory transection to enable automatic detection of money laundering event.
[00032] FIG. 1 is a block diagram that describes a system 100, according to some embodiments of the present disclosure. In some embodiments, the system 100 may include a first computing device 110 that may be arranged to receive a customer registration information from a user, a network interface 130, a remote server 140 that, a non-transitory storage device 150 that may be arranged to store a set of executable routines, and a microprocessor 160 which may be coupled to the non-transitory storage device 150 and operable to execute the set of routines may be arranged to include a second computing device 120 that may be arranged to receive a customer transaction history data and a current transaction information, from a customer.
[00033] In some embodiments, acquire the customer registration information from the first computing device 110. Receive the customer transaction history data and the current transaction information, from the second computing device 120. Analyse the acquired customer registration information to generate a first set of risk scores. Analyse the received customer transaction data to generate a second set of risk scores. Combine the generated first set of risk scores and the second set of risk scores to generate a total risk score.
[00034] In some embodiments, analyse the generated total risk score to determine a customer monitoring level. Monitor the received current transaction information based on the determined customer monitoring level to identify a transaction pattern. Evaluate the identified transaction pattern to determine the money laundering event. Transmit to the first computing device 110, an alert notification, based on the determined money laundering event.
[00035] In some embodiments, the customer registration information may be selected from a customer identity, a customer type, a residential status, a nationality detail, a demographic data and a national registration detail. In some embodiments, the microprocessor 160 may determine the first set of risk scores and the second set of risk scores by applying a machine learning model. In some embodiments, the microprocessor 160 may transmit an alert message to the first computing device 110, if the current transaction information pertains to a financial transaction more than a pre-set monitory value. In some embodiments, the microprocessor 160 may block an account of the customer based on the determined money laundering event.
[00036] FIGS. 2A to 2B are flowcharts that describe a method for monitoring a financial transaction, according to some embodiments of the present disclosure. In some embodiments, at 202, the method may include receiving, at a first computing device, a customer registration information from a user. At 204, the method may include receiving, at a second computing device, a customer transaction history data and a current transaction information, from a customer. At 206, the method may include acquiring, the customer registration information from the first computing device.
[00037] In some embodiments, at 208, the method may include receive the customer transaction history data and the current transaction information, from the second computing device. At 210, the method may include analyzing the acquired customer registration information to generate a first set of risk scores. At 212, the method may include analyzing the received customer transaction data to generate a second set of risk scores.
[00038] In some embodiments, at 214, the method may include combine the generated first set of risk scores and the second set of risk scores to generate a total risk score. At 216, the method may include analyzing the generated total risk score to determine a customer monitoring level. At 218, the method may include monitoring the received current transaction information based on the determined customer monitoring level to identify a transaction pattern. At 220, the method may include evaluating the identified transaction pattern to determine the money laundering event. At 222, the method may include transmitting to the first computing device, an alert notification, based on the determined money laundering event.
[00039] In some embodiments, the customer registration information may be selected from a customer identity, a customer type, a residential status, a nationality detail, a demographic data and a national registration detail. In some embodiments, the first set of risk scores and the second set of risk scores by applying a machine learning model. In some embodiments, the method as claimed. In some embodiments, the method as claimed.
[00040] In certain implementations, a customer's registration details are collected on a first computer device that is part of a system that monitors financial transactions to identify money laundering events. A customer's transaction history data and real-time transaction details may be received by a second computer in certain embodiments. Networking components are sometimes included into embodiments. One embodiment might also make use of a remote server, which could have a non-volatile storage device built up specifically to house a collection of executable instructions. A microprocessor, which is attached to the non-transitory storage device and can run the set of procedures needed to get the customer registration data from the first computer, is also possible. In certain implementations, the second computer is used to get both the customer's transaction history and the details of the current transaction.
[00041] In certain implementations, an initial set of risk ratings is derived by analysing the collected client registration data. A second set of risk scores may be generated by analysing the received customer transaction data in certain embodiments. In certain implementations, a total risk score is calculated by adding the scores from the first set of risks and the second set of risks. In certain implementations, the amount of client monitoring is determined by analysing the derived overall risk score.
[00042] In certain implementations, identifying a transaction pattern involves monitoring the received current transaction information depending on the customer monitoring level. In certain implementations, the money laundering event is discovered by analysing the transactions that make up the pattern. In certain implementations, after a money laundering event has been identified, an alarm notice is sent to the initial computer device. A client's identification, customer type, residency status, nationality detail, demographic data, and national registration detail are all examples of what might be included in the customer registration information in various implementations. The first set of risk ratings and the second set of risk scores are sometimes calculated by the microprocessor using a machine learning model. If the present transaction information is about a financial transaction with a value more than a pre-set monetary value, the microprocessor may send an alarm message to the first computer device. Depending on the circumstances, the company may then decide to suspend the customer's account as a result of the suspected money laundering activity.
[00043] The current disclosure may further comprise a method for monitoring a financial transaction in order to detect and avoid a money laundering event, wherein the method includes receiving, at a first computer device, a customer registration information from a user. One or more embodiments may include a consumer providing transaction history data and/or transaction information at a second computer device. Obtaining the customer registration data from the first computer system is likewise a viable option in certain embodiments. In certain implementations, the second computer is used to get both the customer's transaction history and the details of the current transaction. In certain implementations, an initial set of risk ratings is calculated by evaluating the collected client registration data. An additional set of risk ratings might be derived from an analysis of the received customer transaction data in certain embodiments. In certain implementations, a total risk score is calculated by adding the scores from the first set of risks and the second set of risks. A customer's monitoring level may be determined in certain embodiments by assessing the produced overall risk score.
[00044] In certain implementations, a customer's monitoring level is used to guide the analysis of the incoming real-time transaction data in an effort to spot unusual behaviour. A money laundering event may be discovered in certain embodiments by evaluating the indicated transaction pattern. Depending on the identified money laundering occurrence, certain embodiments may additionally include sending an alarm notice to the first computer. A client's identification, customer type, residency status, nationality detail, demographic data, and national registration detail are all examples of what might be included in the customer registration information in various implementations. In certain implementations, a machine learning model is used to generate both the initial set of risk ratings and the final set of risk scores. If the current transaction information relates to a financial transaction exceeding a pre-set monetary amount, the method as claimed may comprise the step of sending an alarm message to the first computer device. One possible implementation of the claimed technique involves suspending a customer's account once a money laundering incident has been identified.
[00045] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results.
[00046] In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

Claims
I/We claims:

1. A system to monitor a financial transaction to determine a money laundering event, the system comprises:
a first computing device that is arranged to receive a customer registration information from a user;
a second computing device that is arranged to receive a customer transaction history data and a current transaction information, from a customer;
a network interface; and
a remote server that comprises:
a non-transitory storage device that is arranged to store a set of executable routines; and
a microprocessor which is coupled to the non-transitory storage device and operable to execute the set of routines is arranged to:
acquire the customer registration information from the first computing device;
receive the customer transaction history data and the current transaction information, from the second computing device;
analyse the acquired customer registration information to generate a first set of risk scores;
analyse the received customer transaction data to generate a second set of risk scores;
combine the generated first set of risk scores and the second set of risk scores to generate a total risk score;
analyse the generated total risk score to determine a customer monitoring level;
monitor the received current transaction information based on the determined customer monitoring level to identify a transaction pattern;
evaluate the identified transaction pattern to determine the money laundering event; and
transmit to the first computing device, an alert notification, based on the determined money laundering event.
2. The system as claimed in claim 1, wherein the customer registration information is selected from a customer identity, a customer type, a residential status, a nationality detail, a demographic data and a national registration detail.
3. The system as claimed in claim 1, wherein the microprocessor determines the first set of risk scores and the second set of risk scores by applying a machine learning model.
4. The system as claimed in claim 1, wherein the microprocessor transmits an alert message to the first computing device, if the current transaction information pertains to a financial transaction more than a pre-set monitory value.
5. The system as claimed in claim 1, wherein the microprocessor blocks an account of the customer based on the determined money laundering event.
6. A method for monitoring a financial transaction to determine and prevent a money laundering event, the method comprises:
receiving, at a first computing device, a customer registration information from a user;
receiving, at a second computing device, a customer transaction history data and a current transaction information, from a customer;
acquiring, the customer registration information from the first computing device;
receive the customer transaction history data and the current transaction information, from the second computing device;
analyzing the acquired customer registration information to generate a first set of risk scores;
analyzing the received customer transaction data to generate a second set of risk scores;
combine the generated first set of risk scores and the second set of risk scores to generate a total risk score;
analyzing the generated total risk score to determine a customer monitoring level;
monitoring the received current transaction information based on the determined customer monitoring level to identify a transaction pattern;
evaluating the identified transaction pattern to determine the money laundering event; and
transmitting to the first computing device, an alert notification, based on the determined money laundering event.
7. The method as claimed in claim 6, wherein the customer registration information is selected from a customer identity, a customer type, a residential status, a nationality detail, a demographic data and a national registration detail.
8. The method as claimed in claim 6, wherein the first set of risk scores and the second set of risk scores by applying a machine learning model.
9. The method as claimed in claim 6, further comprising step of transmitting an alert message to the first computing device, if the current transaction information pertains to a financial transaction more than a pre-set monitory value.
10. The method as claimed in claim 6, further comprising step of blocking an account of the customer based on the determined money laundering event.

SMART AUTONOMOUS SYSTEM FOR DETECTING MONEY LAUNDERING ACTIVITIES
Abstract
In certain implementations, a customer's registration details are collected on a first computer device that is part of a system that monitors financial transactions to identify money laundering events. A customer's transaction history data and real-time transaction details may be received by a second computer in certain embodiments. Networking components are sometimes included into embodiments. One embodiment might also make use of a remote server, which could have a non-volatile storage device built up specifically to house a collection of executable instructions. A microprocessor, which is attached to the non-transitory storage device and can run the set of procedures needed to get the customer registration data from the first computer, is also possible.

I/We claims:

1. A system to monitor a financial transaction to determine a money laundering event, the system comprises:
a first computing device that is arranged to receive a customer registration information from a user;
a second computing device that is arranged to receive a customer transaction history data and a current transaction information, from a customer;
a network interface; and
a remote server that comprises:
a non-transitory storage device that is arranged to store a set of executable routines; and
a microprocessor which is coupled to the non-transitory storage device and operable to execute the set of routines is arranged to:
acquire the customer registration information from the first computing device;
receive the customer transaction history data and the current transaction information, from the second computing device;
analyse the acquired customer registration information to generate a first set of risk scores;
analyse the received customer transaction data to generate a second set of risk scores;
combine the generated first set of risk scores and the second set of risk scores to generate a total risk score;
analyse the generated total risk score to determine a customer monitoring level;
monitor the received current transaction information based on the determined customer monitoring level to identify a transaction pattern;
evaluate the identified transaction pattern to determine the money laundering event; and
transmit to the first computing device, an alert notification, based on the determined money laundering event.
2. The system as claimed in claim 1, wherein the customer registration information is selected from a customer identity, a customer type, a residential status, a nationality detail, a demographic data and a national registration detail.
3. The system as claimed in claim 1, wherein the microprocessor determines the first set of risk scores and the second set of risk scores by applying a machine learning model.
4. The system as claimed in claim 1, wherein the microprocessor transmits an alert message to the first computing device, if the current transaction information pertains to a financial transaction more than a pre-set monitory value.
5. The system as claimed in claim 1, wherein the microprocessor blocks an account of the customer based on the determined money laundering event.
6. A method for monitoring a financial transaction to determine and prevent a money laundering event, the method comprises:
receiving, at a first computing device, a customer registration information from a user;
receiving, at a second computing device, a customer transaction history data and a current transaction information, from a customer;
acquiring, the customer registration information from the first computing device;
receive the customer transaction history data and the current transaction information, from the second computing device;
analyzing the acquired customer registration information to generate a first set of risk scores;
analyzing the received customer transaction data to generate a second set of risk scores;
combine the generated first set of risk scores and the second set of risk scores to generate a total risk score;
analyzing the generated total risk score to determine a customer monitoring level;
monitoring the received current transaction information based on the determined customer monitoring level to identify a transaction pattern;
evaluating the identified transaction pattern to determine the money laundering event; and
transmitting to the first computing device, an alert notification, based on the determined money laundering event.
7. The method as claimed in claim 6, wherein the customer registration information is selected from a customer identity, a customer type, a residential status, a nationality detail, a demographic data and a national registration detail.
8. The method as claimed in claim 6, wherein the first set of risk scores and the second set of risk scores by applying a machine learning model.
9. The method as claimed in claim 6, further comprising step of transmitting an alert message to the first computing device, if the current transaction information pertains to a financial transaction more than a pre-set monitory value.
10. The method as claimed in claim 6, further comprising step of blocking an account of the customer based on the determined money laundering event.