1. A system for determining risk factors associated with banking transactions, the system comprising: a data input module configured to receive transaction data associated with a banking transaction; a risk factor determination module configured to analyse the transaction data and determine one or more risk factors associated with the banking transaction based on predefined criteria; a data storage module configured to store the transaction data and associated risk factors; and a user interface module configured to provide access to the transaction data and associated risk factors to one or more users.

2. The system of claim 1, wherein the risk factor determination module is further configured to assign a risk score to each identified risk factor based on the severity of the risk factor.

3. The system of claim 2, wherein the risk factor determination module is further configured to calculate an overall risk score for the banking transaction based on the assigned risk scores for each identified risk factor.

4. The system of claim 1, wherein the risk factor determination module is further configured to apply machine learning techniques to the transaction data to identify patterns and anomalies associated with the banking transaction.

5. The system of claim 1, wherein the user interface module is further configured to allow users to input and modify predefined criteria used by the risk factor determination module.

6. The system of claim 1, further comprising a fraud detection module configured to receive the transaction data and associated risk factors from the risk factor determination module and analyse the transaction data and risk factors to detect fraudulent activity associated with the banking transaction; and a fraud alert module configured to notify one or more users of detected fraudulent activity. 7 The system of claim 6, wherein the fraud detection module is further configured to apply machine learning techniques to the transaction data and associated risk factors to identify patterns and anomalies associated with fraudulent activity.

8. The system of claim 6, wherein the fraud alert module is further configured to automatically flag and block banking transactions associated with detected fraudulent activity.

9. A method for determining risk factors associated with banking transactions, the method comprising: receiving transaction data associated with a banking transaction; analyzing the transaction data to determine one or more risk factors associated with the banking transaction based on predefined criteria; assigning a risk score to each identified risk factor based on the severity of the risk factor; calculating an overall risk score for the banking transaction based on the assigned risk scores for each identified risk factor; and storing the transaction data and associated risk factors in a data storage module.

10. The method of claim 9, further comprising applying machine learning techniques to the transaction data to identify patterns and anomalies associated with the banking transaction.   RISK FACTOR DETERMINATION OF BANKING TRANSACTION Abstract The present invention relates to a system for identifying risk factors associated with banking transactions may be included in certain embodiments of the present disclosure. This system may include a data input module that is set up to receive transaction data associated with a banking transaction. A risk factor determination module that is configured to evaluate the transaction data and determine one or more risk factors associated with the banking transaction based on established criteria may also be included in embodiments. A data storage module that is capable of storing the transaction data and related risk factors is another component that may be included in embodiments. A user interface module that is configured to enable access to the transaction data as well as the associated risk factors may also be included in embodiments. Fig. 1 , Claims:Claims :

1. A system for determining risk factors associated with banking transactions, the system comprising: a data input module configured to receive transaction data associated with a banking transaction; a risk factor determination module configured to analyse the transaction data and determine one or more risk factors associated with the banking transaction based on predefined criteria; a data storage module configured to store the transaction data and associated risk factors; and a user interface module configured to provide access to the transaction data and associated risk factors to one or more users.

2. The system of claim 1, wherein the risk factor determination module is further configured to assign a risk score to each identified risk factor based on the severity of the risk factor.

3. The system of claim 2, wherein the risk factor determination module is further configured to calculate an overall risk score for the banking transaction based on the assigned risk scores for each identified risk factor.

4. The system of claim 1, wherein the risk factor determination module is further configured to apply machine learning techniques to the transaction data to identify patterns and anomalies associated with the banking transaction.

5. The system of claim 1, wherein the user interface module is further configured to allow users to input and modify predefined criteria used by the risk factor determination module.

6. The system of claim 1, further comprising a fraud detection module configured to receive the transaction data and associated risk factors from the risk factor determination module and analyse the transaction data and risk factors to detect fraudulent activity associated with the banking transaction; and a fraud alert module configured to notify one or more users of detected fraudulent activity. 7 The system of claim 6, wherein the fraud detection module is further configured to apply machine learning techniques to the transaction data and associated risk factors to identify patterns and anomalies associated with fraudulent activity.

8. The system of claim 6, wherein the fraud alert module is further configured to automatically flag and block banking transactions associated with detected fraudulent activity.

9. A method for determining risk factors associated with banking transactions, the method comprising: receiving transaction data associated with a banking transaction; analyzing the transaction data to determine one or more risk factors associated with the banking transaction based on predefined criteria; assigning a risk score to each identified risk factor based on the severity of the risk factor; calculating an overall risk score for the banking transaction based on the assigned risk scores for each identified risk factor; and storing the transaction data and associated risk factors in a data storage module.

10. The method of claim 9, further comprising applying machine learning techniques to the transaction data to identify patterns and anomalies associated with the banking transaction.

Description:RISK FACTOR DETERMINATION OF BANKING TRANSACTION
Field of the Invention
[0001] The present invention relates generally to the financial service and banking product industries, and in particular to a system and method for risk assessment and authentication of various transactions.
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] Banking transactions refer to the various activities and services provided by banks to their customers, including individuals, businesses, and other financial institutions. Some of the common banking transactions include: deposits, loan, Wire transfer, Payment service, Investment service, foreign exchange, ATM service, Online and mobile banking and many more. Banking transactions are critical for the functioning of the economy, as they provide a means for individuals and businesses to access financial services and conduct transactions efficiently. However, banking transactions also involve various risks, including credit risk, market risk, operational risk, liquidity risk, and reputational risk, which banks need to manage effectively to ensure their stability and profitability.
[0004] Banking transactions involve a variety of risks, including credit risk, market risk, operational risk, and liquidity risk. The identification and assessment of these risks are critical for banks to ensure their stability and profitability. Various techniques for banking transactions risk factors determination are explored in patent literature, few of them are discussed below.
[0005] The CN115293881 (by: Bank of China Co Ltd) - The invention provides a bank transaction risk control method and device which are applied to the technical field of finance, and the method comprises the steps: constructing a risk rule set according to transaction data of bank related risks, each risk rule comprises a rule head and a rule body, the rule head comprises a risk identifier, the rule body comprises a plurality of attribute units, and the attribute units are connected with the risk identifier; each attribute unit comprises a transaction attribute and an attribute value corresponding to the transaction attribute; for each institution of the bank, determining a risk rule corresponding to the institution according to the transaction data of the institution and the risk rule set; and performing risk control on the real-time transaction data of the mechanism according to the risk rule corresponding to each mechanism. According to the invention, risk control can be carried out on bank transactions in time.
[0006] The US20170300911A1 (by: Abdullah Abdulaziz I. Alnajem) relates toa system for evaluating risk in an electronic banking transaction by estimating an aggregated risk value from a set of risk factors that are either dependent or independent of each other, comprising: user input means for enabling an end user to provide authentication information related to a desired electronic banking transaction; financial institution authentication means for authenticating that an end user is authorized to conduct the desired electronic transaction; risk computation means for imposing authentication requirements upon the end user in adaptation to a risk value of the desired banking electronic banking transaction; transaction session means for tracking an amount of time that the desired electronic banking transaction is taking; and financial institution transaction means for storing data related to the desired electronic banking transaction.
[0007] The AU2006235024B2 (by: Bill Me Later Inc) relates to a risk management system for providing risk data to an entity engaged in a transaction with a consumer. The system includes an authorization denial system having an authorization denial system interface for receiving a transaction data set having a plurality of data fields from the entity; and a denial rule set with multiple rules for outputting risk data directed to the transaction based upon the result of applying the rules to the data fields in the transaction data set. The authorization denial system interface transmits the resulting risk data to the entity. A method of authorizing a transaction between a consumer and an entity is also disclosed.
[0008] However, these techniques are often failed to mitigate advance fraud. Thus, there is immense need in this domain.
Summary
[0009] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00010] The following paragraphs provide additional support for the claims of the subject application.
[00011] The present invention relates generally to the financial service and banking product industries, and in particular to a system and method for risk assessment and authentication of various transactions.
[00012] Embodiments of the present disclosure may include a system for determining risk factors associated with banking transactions, including a data input module configured to receive transaction data associated with a banking transaction. Embodiments may also include a risk factor determination module configured to analyse the transaction data and determine one or more risk factors associated with the banking transaction based on predefined criteria. Embodiments may also include a data storage module configured to store the transaction data and associated risk factors. Embodiments may also include a user interface module configured to provide access to the transaction data and associated risk factors to one or more users.
[00013] In some embodiments, the risk factor determination module may be further configured to assign a risk score to each identified risk factor based on the severity of the risk factor. In some embodiments, the risk factor determination module may be further configured to calculate an overall risk score for the banking transaction based on the assigned risk scores for each identified risk factor.
[00014] In some embodiments, the risk factor determination module may be further configured to apply machine learning techniques to the transaction data to identify patterns and anomalies associated with the banking transaction. In some embodiments, the user interface module may be further configured to allow users to input and modify predefined criteria used by the risk factor determination module.
[00015] In some embodiments, the system may include a fraud detection module configured to receive the transaction data and associated risk factors from the risk factor determination module and analyse the transaction data and risk factors to detect fraudulent activity associated with the banking transaction. Embodiments may also include a fraud alert module configured to notify one or more users of detected fraudulent activity. In some embodiments, the fraud detection module may be further configured to apply machine learning techniques to the transaction data and associated risk factors to identify patterns and anomalies associated with fraudulent activity. In some embodiments, the fraud alert module may be further configured to automatically flag and block banking transactions associated with detected fraudulent activity.
[00016] Embodiments of the present disclosure may also include a method for determining risk factors associated with banking transactions, including receiving transaction data associated with a banking transaction. Embodiments may also include analyzing the transaction data to determine one or more risk factors associated with the banking transaction based on predefined criteria.
[00017] Embodiments may also include assigning a risk score to each identified risk factor based on the severity of the risk factor. Embodiments may also include calculating an overall risk score for the banking transaction based on the assigned risk scores for each identified risk factor. Embodiments may also include storing the transaction data and associated risk factors in a data storage module. In some embodiments, the method may include applying machine learning techniques to the transaction data to identify patterns and anomalies associated with the banking transaction.
Brief Description of the Drawings
[00018] 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:
[00019] FIG. 1 is a block diagram illustrating a system for determining risk factors associated with banking transactions, according to some embodiments of the present disclosure.
[00020] FIG. 2 is a detailed block diagram further illustrating the system from FIG. 1 for determining risk factors associated with banking transactions, according to some embodiments of the present disclosure.
[00021] FIG. 3 is a flowchart illustrating a method for determining risk factors, according to some embodiments of the present disclosure.
Detailed Description
[00022] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00023] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00024] The present invention relates generally to the financial service and banking product industries, and in particular to a system and method for risk assessment and authentication of various transactions.
[00025] In accordance with some implementations of the present disclosure for determining risk factors associated with banking transactions, a system 100 is disassembled into its component parts and diagrammatically depicted in FIG. 1. The system 100 may, in certain implementations, include a data input module 110 configured to receive transaction data associated with a banking transaction, a risk factor determination module 120 configured to analyse the transaction data and determine one or more risk factors associated with the banking transaction based on predefined criteria, a data storage module 130 configured to store the transaction data and associated risk factors, and a user interface module 140.
[00026] The risk factor determination module 120 may, in some implementations, have an additional configuration that enables it to assign a risk score to each identified risk factor based on the degree of severity associated. This may be the case in implementations in which the risk score is determined by the risk factor determination module 120. In some implementations, the risk factor determination module 120 can be further configured to compute an overall risk score for the banking transaction based on the risk scores that have been assigned to each identified risk factor. This can be accomplished by using the risk scores that have been assigned to the individual risk factors.
[00027] In some implementations, the risk factor determination module 120 can be further configured to apply machine learning techniques to the transaction data. These techniques allow the module to recognise patterns and anomalies associated with the banking transaction. In some implementations, the user interface module 140 can be further configured to enable users to input and modify predefined criteria that are used by the risk factor determination module 120.
[00028] In accordance with specific applications of the present disclosure, the system 100 from Figure 1 is depicted in greater detail in FIG. 2, which is a detailed block diagram for determining risk factors associated with banking transactions. A fraud detection module 250 may be included in the system 100 in some implementations. The fraud detection module 250 is intended to receive the transaction data and associated risk factors from the risk factor determination module 120. Following this, the fraud detection module 250 is able to perform an analysis on both the transaction data and the risk factors in order to determine whether or not the banking transaction is associated with fraudulent activity. There is a possibility that the fraud detection module 250 also includes a fraud alert module 252. If this is the case, then the module can be configured to alert one or more users of any fraudulent activity that has been identified. It is possible to further configure the fraud detection module 250 so that it applies machine learning techniques to the transaction data and associated risk factors in order to identify patterns and anomalies associated with fraudulent activity. This can be done by selecting the appropriate configuration option from the drop-down menu. In some implementations, the fraud alert module 252 can be further configured to automatically flag and block banking transactions that are associated with the detection of fraudulent activity.
[00029] Figure 3 is a flowchart that depicts one embodiment of a method for determining risk factors. This method is consistent with certain aspects of the disclosure that is being presented here. In certain implementations of the method, including step 310 in particular, it is possible for the method to include the step of "receiving transaction data associated with a banking transaction. " The method may, as part of step 320, include conducting an analysis of the transaction data in order to identify one or more risk factors connected to the banking transaction by making use of a set of predefined criteria. The step of assigning a risk score to each identified risk factor based on the severity of the risk factor may be included as an optional component of the method at step 330. Calculating an overall risk score for the banking transaction based on the risk scores that were assigned to each identified risk factor can be an option at step 340 of the method, depending on the specific implementation. At step 350 of the method, a data storage module 130 may be used in order to save the transaction data as well as the associated risk factors. In some implementations of the method, it may include applying machine learning techniques to the transaction data in order to identify patterns and anomalies associated with the banking transaction.
[00030] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00031] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00032] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00033] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00034] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

 

Claims
I/We Claim:
1. A system for determining risk factors associated with banking transactions, the system comprising:
a data input module configured to receive transaction data associated with a banking transaction;
a risk factor determination module configured to analyse the transaction data and determine one or more risk factors associated with the banking transaction based on predefined criteria;
a data storage module configured to store the transaction data and associated risk factors; and
a user interface module configured to provide access to the transaction data and associated risk factors to one or more users.

2. The system of claim 1, wherein the risk factor determination module is further configured to assign a risk score to each identified risk factor based on the severity of the risk factor.

3. The system of claim 2, wherein the risk factor determination module is further configured to calculate an overall risk score for the banking transaction based on the assigned risk scores for each identified risk factor.

4. The system of claim 1, wherein the risk factor determination module is further configured to apply machine learning techniques to the transaction data to identify patterns and anomalies associated with the banking transaction.

5. The system of claim 1, wherein the user interface module is further configured to allow users to input and modify predefined criteria used by the risk factor determination module.

6. The system of claim 1, further comprising a fraud detection module configured to receive the transaction data and associated risk factors from the risk factor determination module and analyse the transaction data and risk factors to detect fraudulent activity associated with the banking transaction; and a fraud alert module configured to notify one or more users of detected fraudulent activity.

7 The system of claim 6, wherein the fraud detection module is further configured to apply machine learning techniques to the transaction data and associated risk factors to identify patterns and anomalies associated with fraudulent activity.

8. The system of claim 6, wherein the fraud alert module is further configured to automatically flag and block banking transactions associated with detected fraudulent activity.

9. A method for determining risk factors associated with banking transactions, the method comprising:
receiving transaction data associated with a banking transaction;
analyzing the transaction data to determine one or more risk factors associated with the banking transaction based on predefined criteria;
assigning a risk score to each identified risk factor based on the severity of the risk factor;
calculating an overall risk score for the banking transaction based on the assigned risk scores for each identified risk factor; and
storing the transaction data and associated risk factors in a data storage module.

10. The method of claim 9, further comprising applying machine learning techniques to the transaction data to identify patterns and anomalies associated with the banking transaction.
 

RISK FACTOR DETERMINATION OF BANKING TRANSACTION
Abstract
The present invention relates to a system for identifying risk factors associated with banking transactions may be included in certain embodiments of the present disclosure. This system may include a data input module that is set up to receive transaction data associated with a banking transaction. A risk factor determination module that is configured to evaluate the transaction data and determine one or more risk factors associated with the banking transaction based on established criteria may also be included in embodiments. A data storage module that is capable of storing the transaction data and related risk factors is another component that may be included in embodiments. A user interface module that is configured to enable access to the transaction data as well as the associated risk factors may also be included in embodiments.

Fig. 1 , Claims:Claims
I/We Claim:
1. A system for determining risk factors associated with banking transactions, the system comprising:
a data input module configured to receive transaction data associated with a banking transaction;
a risk factor determination module configured to analyse the transaction data and determine one or more risk factors associated with the banking transaction based on predefined criteria;
a data storage module configured to store the transaction data and associated risk factors; and
a user interface module configured to provide access to the transaction data and associated risk factors to one or more users.

2. The system of claim 1, wherein the risk factor determination module is further configured to assign a risk score to each identified risk factor based on the severity of the risk factor.

3. The system of claim 2, wherein the risk factor determination module is further configured to calculate an overall risk score for the banking transaction based on the assigned risk scores for each identified risk factor.

4. The system of claim 1, wherein the risk factor determination module is further configured to apply machine learning techniques to the transaction data to identify patterns and anomalies associated with the banking transaction.

5. The system of claim 1, wherein the user interface module is further configured to allow users to input and modify predefined criteria used by the risk factor determination module.

6. The system of claim 1, further comprising a fraud detection module configured to receive the transaction data and associated risk factors from the risk factor determination module and analyse the transaction data and risk factors to detect fraudulent activity associated with the banking transaction; and a fraud alert module configured to notify one or more users of detected fraudulent activity.

7 The system of claim 6, wherein the fraud detection module is further configured to apply machine learning techniques to the transaction data and associated risk factors to identify patterns and anomalies associated with fraudulent activity.

8. The system of claim 6, wherein the fraud alert module is further configured to automatically flag and block banking transactions associated with detected fraudulent activity.

9. A method for determining risk factors associated with banking transactions, the method comprising:
receiving transaction data associated with a banking transaction;
analyzing the transaction data to determine one or more risk factors associated with the banking transaction based on predefined criteria;
assigning a risk score to each identified risk factor based on the severity of the risk factor;
calculating an overall risk score for the banking transaction based on the assigned risk scores for each identified risk factor; and
storing the transaction data and associated risk factors in a data storage module.

10. The method of claim 9, further comprising applying machine learning techniques to the transaction data to identify patterns and anomalies associated with the banking transaction.