WO2021220044A1 - System and method to predict regulatory penalty for financial institute and provide escalation mechanism - Google Patents

Abstract

A system to predict regulatory penalty for a financial institute and provide an escalation mechanism is disclosed. The system includes a document validation module to validate each of a multiple of financial documents received from the document submission module. The system includes a document rectification module to rectify each of the multiple of financial documents validated from the document validation module. The system includes a penalty prediction module to predict regulatory penalty for each of a multiple of rectified financial documents from the document rectification module using analysing parameters. The system includes an escalation anticipation module to anticipate a level of escalation according to predicted regulatory penalty from the penalty prediction module and notifies based on the anticipated level of escalation. A behavioural statistical module predicts the likelihood of regulatory bodies to impose fines on a situational error. The system rectifies error before documents are submitted to financial regulators.

Description

SYSTEM AND METHOD TO PREDICT REGULATORY PENALTY FOR FINANCIAL INSTITUTE AND PROVIDE ESCALATION MECHANISM

This International Application claims priority from a complete patent application filed in India having Patent Application No. 202011018283, filed on April 29, 2020 and titled “SYSTEM AND METHOD TO PREDICT REGULATORY PENALTY FOR FINANCIAL INSTITUTE AND PROVIDE ESCALATION MECHANISM”.

FIELD OF INVENTION

Embodiments of a present disclosure relates to a financial regulatory compliance system, and more particularly to a system and a method to predict regulatory penalty for a financial institute and provide an escalation mechanism for notifying the financial institute.

BACKGROUND

In recent times, financial regulators have increased scrutiny over financial institutes and thereby regulatory penalties also increased. Such regulatory penalties decrease bank profitability. Additionally, penalties create uncertainty concerning the solvency and the business model of financial institutes. The financial regulators analyse the transactions over several issues such as timeliness, completeness, accuracy, fraud detection and the like. In one approach, a financial institute uses an analysing system to mitigate the risks associated with financial regulatory compliance for both financial institutions and business clients. Such system enables notification from generated suspicious activity and currency transaction reports from the transaction data. Most effective approach would be to detect an error in financial documents in real time and automatically rectify such error before submission to regulators.

Moreover, efficient approach would be to predict a penalty in real time corresponding to the submitted financial document. And timely notification of such penalties to responsible parties is very much necessary. Hence, there is a need for an improved system for predicting regulatory penalty for financial institute and additionally an escalation mechanism for notifying the financial institute and a method to operate the same and therefore address the aforementioned issues. BRIEF DESCRIPTION

In accordance with one embodiment of the disclosure, a system to predict regulatory penalty for a financial institute and provide an escalation mechanism is disclosed. The system includes one or more processors. The system also includes a document submission module operable by one or more processors. The document submission module is configured to receive a plurality of financial documents from the financial institute.

The system also includes a document validation module operable by the one or more processors. The documentation validation module is configured to validate each of the plurality of financial documents received from the document submission module by a plurality of validation techniques for error. The system also includes a document rectification module operable by the one or more processors. The document rectification module is configured to rectify each of the plurality of financial documents validated from the document validation module.

The system also includes a penalty prediction module operable by the one or more processors. The penalty prediction module is configured to predict regulatory penalty for each of a plurality of rectified financial documents from the document rectification module using analysing parameters. The system also includes a behavioural statistical module operable by the one or more processors. The behavioural statistical module also predicts the likelihood of regulatory bodies to impose fines on a situational error. The system also includes an escalation anticipation module operable by the one or more processors. The escalation anticipation module is configured to anticipate a level of escalation according to predicted regulatory penalty from the penalty prediction module. The escalation anticipation module is also configured to notify based on the anticipated level of escalation. In accordance with one embodiment of the disclosure, a method for predicting regulatory penalty for a financial institute and providing an escalation mechanism is disclosed. The method includes receiving a plurality of financial documents from the financial institute. The method also includes validating the plurality of financial documents that are received by a plurality of validation techniques for error.

The method also includes rectifying the error as presented after validation by the document validation module. The method also includes predicting regulatory penalty for each of a plurality of rectified financial documents using analysing parameters. The method also includes predicting likelihood of regulatory bodies to impose fines on a situational error. The method also includes anticipating a level of escalation according to predicted regulatory penalty. The method also includes notifying based on anticipated level of escalation.

To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

BRIEF DESCRIPTION OF THE DRAWINGS The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

FIG. 1 is a block diagram representation of a system to predict regulatory penalty for a financial institute and provide an escalation mechanism in accordance with an embodiment of the present disclosure; FIG. 2 is a schematic representation of an embodiment representing the system to predict the regulatory penalty for the financial institute and provide the escalation mechanism of FIG. 1 in accordance of an embodiment of the present disclosure;

FIG. 3 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; and FIG. 4 is a flowchart representing the steps of a method for predicting regulatory penalty for a financial institute and providing an escalation mechanism in accordance with an embodiment of the present disclosure.

Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated online platform, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, subsystems, elements, structures, components, additional devices, additional subsystems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

Embodiments of the present disclosure relate to a system to predict regulatory penalty for a financial institute and provide an escalation mechanism. The system includes one or more processors. The document submission module receives a multiple of financial documents from the financial institute.

The system also includes a document validation module operable by the one or more processors. The documentation validation module validates each of the multiple of financial documents received from the document submission module by a plurality of validation techniques for error. The system also includes a document rectification module operable by the one or more processors. The document rectification module rectifies each of the multiple of financial documents validated from the document validation module.

The system also includes a penalty prediction module operable by the one or more processors. The penalty prediction module predicts regulatory penalty for each of a multiple of rectified financial documents from the document rectification module using analysing parameters. The system also includes a behavioural statistical module operable by the one or more processors. The behavioural statistical module also predicts the likelihood of regulatory bodies to impose fines on a situational error.

The system also includes an escalation anticipation module operable by the one or more processors. The escalation anticipation module anticipates a level of escalation according to predicted regulatory penalty from the penalty prediction module. The escalation anticipation module is also notifies based on the anticipated level of escalation.

FIG. 1 is a block diagram representation of a system to predict regulatory penalty (10) for a financial institute and provides an escalation mechanism in accordance with an embodiment of the present disclosure. As used herein, the term “regulatory penalty” means any civil monetary fine or penalty imposed by a federal, state, local or foreign governmental entity in such entity’s regulatory or official capacity pursuant to its order under a "regulatory action". As used herein, the term “financial institute” refers to corporations that provide services as intermediaries of financial markets. As used herein, the term “escalation” refers to highlighting an issue at hand to a higher authority for action.

The system (10) includes a document submission module operable by one or more processors. The document submission module (20) receives multiple of financial documents from the financial institute. In one embodiment, the multiple of financial documents may include various documents such as basic statistical returns, Forex and international operational returns and the like. In such embodiment, the financial institute may comprise a banking corporation.

The system (10) also includes a document validation module (30) operable by the one or more processors. The documentation validation module (30) validates each of the multiple of financial documents received from the document submission module (20) by a plurality of validation techniques for error. In one embodiment, the multiple of validation techniques includes Data Type Definition techniques and XML Schema Definition techniques. In such embodiment, Data Type Definition techniques and XML Schema Definition techniques enable pointing out errors such as value missing, incorrect value, incorrect range and the like.

The system (10) also includes a document rectification module (40) operable by the one or more processors. The document rectification module (40) rectifies each of the multiple of financial documents validated from the document validation module. In one embodiment, the rectification of each of the multiple of financial documents is carried according to first set of rules. In such embodiment, the first set of rules refer to banking regulatory guidelines and compliance rules.

It is pertinent to note that such rectification is very much necessary before submitting the multiple of financial documents to regulators. In such embodiment, as mistakes or errors are removed by the document rectification module (40), the financial institution may easily submit such document to one or more financial regulators. The system (10) also includes a penalty prediction module (50) operable by the one or more processors. The penalty prediction module (50) predicts regulatory penalty for each of a multiple of rectified financial documents from the document rectification module (40) using analysing parameters.

In particular embodiment, the analysing parameters associated with predicted regulatory penalty for each of the multiple of submitted financial documents comprises analysing real time financial regulatory updates.

In such embodiment, the real time financial regulatory updates include real time associated news of a particular error. In one example, a web crawler is used to systematically browse financial market news. The web crawler uses machine learning “classification” technique to pick-up the news related to financial fines levied on financial firms. The objective of the web crawler is to extract three key information from the news, first, the amount of fines imposed on financial institutions, second, number of errors or incorrect transactions reported due to which the fines were imposed and third, the reason of imposing the fine from the news. The web crawler then computes ‘average fines reported per transaction’ by dividing fines imposed on financial institutions from number of errors or incorrect transactions reported and insert this value into large historical financial fine database along with reason of imposing fines. This data is then used to predict penalty for future transactions, thereby, multiplying the estimated errors or incorrect transactions obtained from “regression” technique by the average fines reported per transactions.

The system (10) also includes a behavioural statistical module (55), thereby using machine learning’s sentiment analysis to predict the likelihood of regulatory bodies to impose fines. The sentiment analysis judges whether the specific regulator intend to imposes penalty on a particular situational error. The behavioural statistical module (55) does judging by cross checking electronic mails and communication exchanges corresponding to the specific regulator to evaluate the likelihood of.

The system (10) also includes an escalation anticipation module (60) operable by the one or more processors. The escalation anticipation module (60) anticipates a level of escalation according to predicted regulatory penalty from the penalty prediction module. In one embodiment, the level of escalation according to predicted regulatory penalty includes a subgroup level escalation, a group level escalation, a business unit level escalation and an organizational level escalation.

Further, the escalation anticipation module (60) also notifies based on the anticipated level of escalation. In one embodiment, the notification is in-accordance to predicted penalty range. In one embodiment, if the predicted penalty is below about 0.5 million then the subgroup level escalation will happen. In another embodiment, if the predicted penalty is in the range of about 0.5 million to 2 million then the group level escalation will happen.

In yet another embodiment, if the predicted penalty is in the range of about 2 million and 5 million then the business unit level escalation will happen. In yet another embodiment, if the predicted penalty is above of about 5 million then the escalation will occur at execution level of the organization. The system (10) also stores the predicted penalty details as well as rectified documents via a storage module. The storage may be in a local storage or a remote storage. FIG. 2 is a schematic representation of an embodiment representing the system to predict the regulatory penalty for the financial institute and provide the escalation mechanism (10) of FIG. 1 in accordance of an embodiment of the present disclosure. In an exemplary situation, a bank X (70) may submit regulatory document 1 (80) in the system (10). The system (10) receives the document 1 (80) via a document submission module (20). The submitted document 1 (80) is basically in XML format.

A document validation module (30) enables checking or validating of the document 1 (80). Here, XML Schema Definition technique is being used to authenticate the document 1 (80) XML data. Here, the technique verifies that each item of content in a document adheres to the description of the element in which the content is to be placed.

Furthermore, for any detected error in document 1 (80), a document rectification module (40) enables automatic rectification of the document 1 (80). The rectification is done in accordance to regulatory compliance rules and regulations. After rectification, the document 1 (80) is ready for submission by the bank X (70). Additionally, the system (70) enables prediction of penalty related to any error further existing on the document 1 (80). Such penalties are identified by regulatory body (90). Here, the prediction is being by analysing real time financial regulatory news and analysing behavioural statistical data of the regulatory body (90). A behavioural statistical module (55) predicts the likelihood of regulatory bodies to impose fines on the particular situational error.

Various news updates regarding the same error are being analysed by the module. The regulatory body (90) related penalty is also analysed to predict the penalty quantity. Here, appropriate authority of the bank X (70) gets notification about the penalty. For example, an executive of the bank (70) such as, but not limited to, a Chief Financial Officer (CFO) is notified for a penalty of 5 million or above related to document 1 (80).

The document submission module (20), the document validation module (30), the document rectification module (40), the penalty prediction module (50), the behavioural statistical module (55) and the escalation anticipation module (60) in FIG. 2 is substantially equivalent to the document submission module (20), the document validation module (30), the document rectification module (40), the penalty prediction module (50), the behavioural statistical module (55) and the escalation anticipation module (60) of FIG. 1.

FIG. 3 is a block diagram of a computer or a server (100) in accordance with an embodiment of the present disclosure. The server (100) includes processor(s) (130), and memory (110) coupled to the processor(s) (130). The processor(s) (130), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

The memory (110) includes a plurality of modules stored in the form of executable program which instructs the processor (130) to perform the method steps illustrated in Fig 1. The memory (110) has following modules: the document submission module (20), the document validation module (30), the document rectification module (40), the penalty prediction module (50), the behavioural statistical module (55) and the escalation anticipation module (60).

The document submission module (20) is configured to receive a plurality of financial documents from the financial institute. The documentation validation module (30) is configured to validate each of the plurality of financial documents received from the document submission module by a plurality of validation techniques for error. The document rectification module (40) is configured to rectify each of the plurality of financial documents validated from the document validation module.

The penalty prediction module (50) is configured to predict regulatory penalty for each of a plurality of rectified financial documents from the document rectification module using analysing parameters. The behavioural statistical module (55) also predicts the likelihood of regulatory bodies to impose fines on a situational error.

The escalation anticipation module (60) is configured to anticipate a level of escalation according to predicted regulatory penalty from the penalty prediction module. The escalation anticipation module (60) is also configured to notify based on the anticipated level of escalation.

Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the processor(s) (130).

FIG. 4 is a flowchart representing the steps of a method for predicting regulatory penalty for a financial institute and providing an escalation mechanism in accordance with an embodiment of the present disclosure. The method (140) includes receiving a plurality of financial documents from the financial institute in step 150. In one embodiment, receiving the plurality of financial documents from the financial institute includes receiving the plurality of financial documents from the financial institute by a document submission module.

The method (140) also includes validating the plurality of financial documents that are received by a plurality of validation techniques for error in step 160. In one embodiment, validating the plurality of financial documents that are received by the plurality of validation techniques for error includes validating the plurality of financial documents that are received by the plurality of validation techniques for error by a document validation module.

In another embodiment, validating the plurality of financial documents that are received by the plurality of validation techniques for error includes validating by the plurality of validation techniques for error comprising Data Type Definition technique and XML Schema Definition technique.

The method (140) also includes rectifying the error as presented after validation by the document validation module according to first set of rules in step 170. In one embodiment, rectifying the error as presented after validation by the document validation module according to the first set of rules includes rectifying the error as presented after validation by a document rectification module.

The method (140) also includes predicting regulatory penalty for each of a plurality of rectified financial documents using analysing parameters in step 180. In one embodiment, predicting regulatory penalty for each of the plurality of rectified financial documents using the analysing parameters includes predicting regulatory penalty for each of the plurality of rectified financial documents using the analysing parameters by a penalty prediction module. In another embodiment, predicting regulatory penalty for each of the plurality of rectified financial documents using the analysing parameters includes predicting by the analysing parameters comprising analysing real time financial regulatory updates.

The method (140) also includes predicting likelihood of regulatory bodies to impose fines on a situational error in step 185. In one embodiment, predicting likelihood of regulatory bodies to impose fines on the situational error includes predicting likelihood of regulatory bodies to impose fines on the situational error by a behavioural statistical module. The method (140) also includes anticipating a level of escalation according to predicted regulatory penalty in step 190. In one embodiment, anticipating the level of escalation according to the predicted regulatory penalty includes anticipating the level of escalation according to the predicted regulatory penalty by an escalation anticipation module. In another embodiment, anticipating the level of escalation according to the predicted regulatory penalty includes anticipating the level of escalation comprising a subgroup level escalation, a group level escalation, a business unit level escalation and an organizational level escalation.

The method (140) also includes notifying based on anticipated level of escalation in step 200. In one embodiment, notifying based on the anticipated level of escalation includes notifying based on the anticipated level of escalation by the escalation anticipation module.

Present disclosure of a system to predict regulatory penalty provides real time escalation mechanism for a financial institute. The escalation mechanism enables escalating matter to appropriate authorities at a pre-determined time. In addition to prediction of penalty, the system also enables rectifying an error before documents are submitted to financial regulators. Such proactive fixing of error is very essential to improve the business structure of a financial institute.

While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependant on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

WE CLAIM:

1. A system to predict regulatory penalty for a financial institute and provide an escalation mechanism (10), comprising: one or more processors; a document submission module (20) operable by the one or more processors, wherein the document submission module (20) configured to receive a plurality of financial documents from the financial institute; a document validation module (30) operable by the one or more processors, wherein the document validation module (30) is configured to validate each of the plurality of financial documents received from the document submission module by a plurality of validation techniques for error; a document rectification module (40) operable by the one or more processors, wherein the document rectification module (40) is configured to rectify each of the plurality of financial documents validated from the document validation module (30), wherein the rectification of each of the plurality of financial documents is carried according to first set of rules; a penalty prediction module (50) operable by the one or more processors, wherein the penalty prediction module (50) is configured to predict regulatory penalty for each of a plurality of rectified financial documents from the document rectification module using analysing parameters; a behavioural statistical module (55) operable by the one or more processors, wherein the behavioural statistical module (55) also predicts the likelihood of regulatory bodies to impose fines on a situational error; and an escalation anticipation module (60) operable by the one or more processors, wherein the escalation anticipation module (60) is configured to: anticipate a level of escalation according to predicted regulatory penalty from the penalty prediction module; and notify based on the anticipated level of escalation.

2. The system (10) as claimed in claim 1, wherein the plurality of validation techniques comprises Data Type Definition technique and XML Schema Definition technique.

3. The system (10) as claimed in claim 1, wherein the analysing parameters associated with predicted regulatory penalty for each of the plurality of submitted financial documents comprises analysing real time financial regulatory updates.

4. The system (10) as claimed in claim 1, wherein the level of escalation according to predicted regulatory penalty comprises a subgroup level scalation, a group level escalation, a business unit level escalation and an organizational level escalation.

5. A method for predicting regulatory penalty for a financial institute and providing an escalation mechanism (140), comprising: receiving, by a document submission module, a plurality of financial documents from the financial institute (150); validating, by a document validation module, the plurality of financial documents that are received by a plurality of validation techniques for error (160); rectifying, by a document rectification module, the error as presented after validation by the document validation module according to first set of rules (170); predicting, by a penalty prediction module, regulatory penalty for each of a plurality of rectified financial documents using analysing parameters (180); predicting, by a behavioural statistical module, likelihood of regulatory bodies to impose fines on a situational error (185); anticipating, by an escalation anticipation module, a level of escalation according to predicted regulatory penalty (190); and notifying, by the escalation anticipation module, based on anticipated level of escalation (200).

6. The method (140) as claimed in claim 5, wherein validating, by the document validation module, by the plurality of validation techniques for error comprising Data Type Definition technique and XML Schema Definition technique.

7. The method (140) as claimed in claim 5, wherein predicting, by the penalty prediction module, by the analysing parameters comprising analysing real time financial regulatory updates.

8. The method (140) as claimed in claim 5, wherein anticipating, by the escalation anticipation module, the level of escalation comprising a subgroup level escalation, a group level escalation, a business unit level escalation and an organizational level escalation.