WO2021075954A1

WO2021075954A1 - Due-diligence for risk mitigation

Info

Publication number: WO2021075954A1
Application number: PCT/MY2020/050109
Authority: WO
Inventors: Meenakshy R IYER; Rekha VISWANATHAN; Charles BABU; Shrey SINGHANIA; Manasa A GOWDA; Mohd Suhail Amar Suresh ABDULLAH
Original assignee: Malayan Banking Berhad
Priority date: 2019-10-14
Filing date: 2020-10-14
Publication date: 2021-04-22

Abstract

The present invention provides system and method for risk assessment. The system of the invention is a configurable risk assessment system for computing risk score and risk rating of an entity. The system includes at least one computing device, a server support architecture, a data processing and control support architecture, and a database. The data processing and control support mechanism may include a processor and a controller where the processor is coupled to the controller for reconfiguring the set of associated parameters, the set of data models and the data dependency matrix for computing a risk score and a risk rating of the at least one entity based on the new or modified data.

Description

DUE-DILIGENCE FOR RISK MITIGATION

FIELD OF THE INVENTION The present invention relates to risk mitigation in financial institutions. More particularly, the invention relates to system and method for customer risk assessment and mitigation in banks or other financial institutions.

BACKGROUND

In financial institutions like banks while onboarding a customer and during subsequent interactions with them multiple factors are assessed to determine risks involved. Customer Due Diligence (CDD) plays a critical role for any banking facility. The CDD is required by all domestic financial institutions that maintain, administer, or manage private banking accounts or correspondent accounts in any country.

Depending on the customer profile, the risk criteria differ across customers as numerous operational and business environments are associated with diverse level of risk. The existing systems for risk assessment, due to stiff approach and coupled with manual interventions, do not accurately handle the dynamism of changes that occur in operation and business. For optimum risk mitigation, the changes in real time environments and their impact on multiple functions are important criteria to determine the risk involved. Further, the risk assessment may be a time-consuming exercise which may impact the customer service and thereby lead to loss of customer for the bank. Any banking institute invests lot of time and money in marketing to try and increase the customer base, but at the same time banks need to ensure that the risk assessment for every customer is appropriate to avoid losses later. The banks always strive to strike a balance in determining the risks and that too in least possible time to ensure success.

In view of the above, there exists a need of improved systems and methods that overcome the shortcomings associated with existing technologies and prior arts. SUMMARY OF THE INVENTION

Accordingly, the present invention provides a method of risk assessment of at least one entity by a configurable application. The method includes receiving by a processor at least one entity data; storing by seeding the at least one entity data in a database; processing the received data and a set of associated parameters of the data for determining if the received data is a new or modified data wherein a seed data model of the application reconfigures an underlining seeded mapping structure of the application in response to determination of the received data as a new or modified data; analyzing a set of data models stored in a data model database to identify at least one risk model for processing the received data; Al based processing of a data dependency matrix and the identified risk data model to determine by a controller at least one impacted factor, component, sequence and protocol of the application; and reconfiguring the set of associated parameters, the set of data models and the data dependency matrix for computing a risk score and a risk rating of the at least one entity based on the new or modified data.

In an embodiment, the present invention provides a configurable risk assessment system. The system includes an electronic user interface configured for operating on a risk assessment application; at least one entity database configured for storing a plurality of entity data and a set of associated parameters of the entity data wherein a new or modified entity data is stored by seeding the data in the database wherein a seed data model of the application reconfigures an underlining seeded mapping structure of the application in response to determination of the received data as the new or modified data; a controller encoded with instructions enabling the controller to function as a bot wherein the controller is configured to perform functions of: analyzing a set of data models stored in a data model database to identify at least one risk model for processing the received data; and Al based processing of a data dependency matrix and the identified risk data model to determine by a controller at least one impacted factor, component, sequence and protocol of the application; and a processor coupled to the controller for reconfiguring the set of associated parameters, the set of data models and the data dependency matrix for computing a risk score and a risk rating of the at least one entity based on the new or modified data. In an embodiment, the present invention provides a computer-readable non- transitory storage medium storing executable program instructions for risk assessment by configurable application which when executed by a computer causes the computer to perform operations as described above.

In an advantageous aspect, the underlying parameters, dependencies and rules used by the present invention to compute the score and rating are fully configurable. The ease of adaptability to change these underlying factors gives a distinct advantage to the application to handle the dynamic nature of data changes. As the volume of entities onboarded is substantial, the application enables computation of risk score and rating after proper analysis of the entity information in accordance with the processing protocols with flexibility to define dependability, thereby providing colossal gains considering that no manual intervention is required. DESCRIPTION OF THE DRAWINGS

Fig. 1 shows a system architecture for risk assessment application in accordance with an embodiment of the present invention. Fig. 2 shows a flow chart depicting a method for risk assessment and computation of risk score and risk rating in accordance with an embodiment of the present invention.

DESCRIPTION OF THE INVENTION

Various embodiment of the present invention provides system and method for risk assessment of an entity. The following description provides specific details of certain embodiments of the invention illustrated in the drawings to provide a thorough understanding of those embodiments. It should be recognized, however, that the present invention can be reflected in additional embodiments and the invention may be practiced without some of the details in the following description.

The various embodiments including the example embodiments are described more fully with reference to the accompanying drawings, in which the various embodiments of the invention are shown. The invention may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure is thorough and complete, and fully conveys the scope of the invention to those skilled in the art. In the drawings, the sizes of components may be exaggerated for clarity.

It should be understood that when an element or layer is referred to as being “on” “connected to” or “coupled to” another element or layer, it can be directly on, connected to, or coupled to the other element or layer or intervening elements or layers that may be present. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

Spatially relative terms, such as “risk score,” “risk rating”, “entity data” and the like, may be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It should be understood that the spatially relative terms are intended to encompass different orientations of the structure in use or operation in addition to the orientation depicted in the figures.

Embodiments described herein refer to plan views and/or cross-sectional views by way of ideal schematic views. Accordingly, the views may be modified depending on simplistic assembling or manufacturing technologies and/or tolerances. Therefore, example embodiments are not limited to those shown in the views but include modifications in configurations formed on basis of assembling process. Therefore, regions or regions of elements exemplified in the figures have schematic properties and shapes, and do not limit the various embodiments including the example embodiments.

The subject matter of example embodiments, as disclosed herein, is described with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different features or combinations of features similar to the ones described in this document, in conjunction with other technologies. Generally, the various embodiments including the example embodiments relate to system and method for risk assessment. Referring to Fig. 1 , a configurable system application architecture 100 for computation of risk score and risk rating is shown in accordance with an embodiment of the present invention. The system 100 include at least one computing device 110, a server support architecture 120, a data processing and control support architecture/mechanism 130, and a database 140a. The server support architecture may include server 120a and mainframe 120b. The data processing and control support architecture/mechanism 130 may include a processor 130a and a controller 130b. The system may include a data model database 140b.

In an exemplary embodiment, the application has the list of parameters, its reference values, related scores as underlying seeded data in the database (140a). This is used by the logic of risk score calculation for processing - by mapping the received input values against these seeded data. Any addition or change in parameters/reference values/scores in database is made by an interface.

In an embodiment, the at least one entity database is configured for storing a plurality of entity data and a set of associated parameters of the entity data where a new or modified entity data is stored by seeding the data in the database.

In a related embodiment, a seed data model of the application reconfigures an underlining seeded mapping structure of the application in response to determination of the received data as the new or modified data.

In an embodiment the controller is encoded with instructions enabling the controller to function as a bot where the controller is configured to perform functions of analyzing a set of data models stored in the data model database to identify at least one risk model for processing the received data and Al based processing of a data dependency matrix and the identified risk data model to determine by the controller at least one impacted factor, component, sequence and protocol of the application.

In an exemplary embodiment, the impact factors are customer parameters received as an input and used to arrive risk rating. In another embodiment, the components are various parts of risk rating application wherein the risk rating application is the composite entity. The application has model identification component, risk score calculation component and rule engine component.

In yet another embodiment, the sequence is the order in which these components interact with each other to arrive at final risk rating. The protocols or rules are instructions to be followed during presence or absence of certain factors/combination of factors. The modified factors/ rules for model identification and risk score calculation are updated and configured using the user interface for these components which follows a sequence to arrive at risk rating of customer. These components consume the inputs so updated or configured without code change, as methods with relevant integrations and configurable dependencies are embedded within in the application. Hence, the modified or new inputs which are thus configured are auto - identified by the components for model identification and risk rating.

In an example embodiment, the application of the present invention is explained in terms of legal structure as a new parameter. The “legal structure” as a new parameter, is received as input to the application with value “partnership” for a customer. Here, “Legal structure” is the factor. The model identification component analyzes the customer factors received along with this new factor “Legal structure” and its value “partnership” to arrive at a risk rating model. The presence of “partnership” as input will influence the model being identified by this component, in case “legal structure - partnership” combination is configured as an identifying factor for models. The risk score calculation component, consumes this factor “Legal structure” and its value “partnership” as one of the inputs to arrive at risk score, incase “legal structure - partnership” combination is configured as risk score calculation factor for the identified risk rating model for the customer. The rule engine component analyses whether any output (score/rating) is to be overridden at customer level or a more granular level, due to the presence of “Legal structure” as “partnership” according to the model.

In an embodiment of the invention, the processor is coupled to the controller for reconfiguring the set of associated parameters, the set of data models and the data dependency matrix for computing a risk score and a risk rating of the at least one entity based on the new or modified data.

In an embodiment, the system 100 includes an electronic user interface configured for operating the system to compute risk score and risk rating.

In an example embodiment the server 120a may include electronic circuitry for enabling execution of various steps by the processor. The electronic circuity may have various elements including but not limited to a plurality of arithmetic logic units (ALU) and floating-point Units (FPU), and/or the equivalents thereof. The ALU enables processing of binary integers to assist in generating a plurality of data models to be stored in the data model database 140b to identify at least one risk model for processing the received data and Al based processing of a data dependency matrix and the identified risk data model. In an example embodiment, the server electronic circuitry includes at least one arithmetic logic unit, floating point units (FPU), other processors, memory, storage devices, high-speed interfaces connected through buses for connecting to memory and high-speed expansion ports, and a low speed interface connecting to low speed bus and storage device. Each of the components of the electronic circuitry are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor can process instructions for execution within the server 120a, including instructions stored in the memory or on the storage devices to display graphical information for a GUI on an external input/output device, such as display coupled to high speed interface. In other implementations, multiple processors and/or multiple busses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple servers may be connected, with each server providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system). The processor 130a may communicate with a user through a control interface and display interface coupled to a display. The display may be, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface may comprise appropriate circuitry for driving the display to present graphical and other information to an entity/user. The control interface may receive commands from a user and convert them for submission to the processor. In addition, an external interface may be provided in communication with processor 130a, so as to enable near area communication of device with other devices. External interface may be suitable, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.

The database 140a is part of a data storage support architecture 140 that may include memory units that may be a volatile, a non-volatile memory or memory may also be another form of computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. In an embodiment, the artificial intelligence-based Al engine is configured for processing a plurality of data models associated with the entity data for determining the risk score and risk rating.

In an embodiment, the system of the present invention includes a natural language processing (NLP) server configured for processing the protocols based on the identified risk data models to determine the risk score and risk rating.

In an embodiment, the user interface and an underlying logic of the risk assessment application is based on the seeded mapping wherein on introduction of the new or modified data the system automatically reconfigures the underlining seeded mapping structure of the application to include the impacted factors, the components, the sequence and the protocol for determination of the risk score and risk rating. In an embodiment, the application functions on seeded data and generates mapping structures or codes on basis of the seeded data thereby enabling real time processing of received entity data within the application upon receipt of new or modified data. Referring to Fig. 2, a flowchart 200 depicting a method for computing risk score and risk rating for risk assessment is provided in accordance with an embodiment of the present invention. The method comprises the steps of (S210) receiving by a processor at least one entity data; (S220) storing by seeding the at least one entity data in a database; (S230) processing the received data and a set of associated parameters of the data for determining if the received data is a new or modified data wherein a seed data model of the application reconfigures an underlining seeded mapping structure of the application in response to determination of the received data as a new or modified data; (S240) analyzing a set of data models stored in a data model database to identify at least one risk model for processing the received data; (S250) Al based processing of a data dependency matrix and the identified risk data model to determine by a controller at least one impacted factor, component, sequence and protocol of the application; (S260) reconfiguring the set of associated parameters, the set of data models and the data dependency matrix for computing a risk score and a risk rating of the at least one entity based on the new or modified data.

In a related embodiment, the risk score and risk rating are computed. To arrive at risk rating of an individual entity/customer, the customer information captured is to be passed to this application. In case of individual customer, suppose the input information are factors such as name, date of birth, address, income, place of birth, occupation, industry, with their values, then a set of data models are analyzed to arrive at a risk rating model based on the input information (above factors/values) for the customer. Income could be a factor for model identification, wherein, customers with income above a limit are to be rated as per model A and below a limit are to be rated as per model B. Multi-factors could also be determinant to arrive at a model for customer. In case any new input information, i.e., factor/value is to be considered for model identification, the same is to be updated through user interface for the model identifier component to consume for determination of the model. After model is identified, the associated factors for risk calculation which are mapped in the database are matched to the received data to arrive at risk score. So here, if model A is identified due to customer income level and required parameters for model A for risk score calculation are - place of birth, occupation, industry, then the risk score calculation component will filter and consider the values of only these three factors from customer input and match with mapped values to compute the score based on configured logic. In case any new input information, i.e., factor is to be considered for risk score computation, the same is to be updated through user interface for the risk score calculation component to consume. The scores and computation expression such as addition, maximum, weighted average etc. can be defined by user interface.

In another embodiment, protocols/rules can be defined by user interface for the rule engine component to consume. The intermediate rules, if any, during computation of risk score will be executed within the logic of score calculation. Once the computation of risk score is complete, customer level rules (if any) are executed by rule engine component. For individual customer, for model A, in case the customer is from real estate industry and is owner of buildings, rule could be that the risk rating of customer be changed to high. In case of absence of rules, the calculated score will be converted to arrive at risk rating of customer.

In an embodiment, the new or modified data is modification in dependency of entity data or new associated parameter of the data.

In an embodiment, the received data and associated parameters are compared with a set of stored entity data in an entity database to determine if the received data is a new data or a modified data.

In an embodiment, for determining the data as a new or modified data, the received data is identified, sorted, classified and analyzed as part of comparison with the set of stored entity data.

In an embodiment, the method of the present invention includes generating the data dependency matrix that enables identification of the impacted factors, the components, the sequence and triggers the protocols to process the received data for determining the risk score and risk rating on the at least one entity.

In an embodiment, the method of the invention includes storing a set of rules or protocols in a memory to be executed by an Al engine coupled to a processor for generating a plurality of data models associated with the entity data to identify the at least one risk model. In some implementations, the machine learning data model is configured to map entity data and data models with data processing protocol to compute the risk score and risk rating. For example, output generated over the model may provide an indication of whether a particular object or class of objects is present, and optionally user instructions. In some implementations, the machine learning model is configured to map new or modified entity data for determining risk score and risk rating. Accordingly, in those implementations a single pass over a single machine learning model may be utilized to detect whether each of multiple objects is present. It should be apparent that different aspects of the description provided above may be implemented in many different forms of software, firmware, and hardware in the implementations illustrated in the figures. The actual software code or specialized control hardware used to implement these aspects is not limiting of the invention. Thus, the operation and behavior of these aspects were described without reference to the specific software code — it being understood that software and control hardware can be designed to implement these aspects based on the description herein.

In one example embodiment, the customer parameters/questions with values/responses from customer are the data fed to the application, which then utilizes this input data to arrive at an appropriate risk model from set of models in the database. The processor further filters these input data according to the data dependencies of the identified model and arrives at the risk scores for the factors based on the seeded mapping in data base. The method for computation of risk score for the entity is as per the mathematical expressions linked to the model, factors and groups of factors, which can involve data received in multiple layers. The methods are designed to handle input data received in various granularities as well. Multi-level rule engine integration utilizes the required input data to handle specific scenarios. Model level configuration of seeded data for values in case of exceptional scenarios is provided along with relevant dependencies. Assimilation of these components in sequence succors to arrive at output of the application, which is risk rating of the customer. The input, processing and output data is further stored in database. The system risk score as an input is processed with risk levels and threshold to arrive at system risk rating. In an exemplary embodiment, the system and method computes risk score and risk rating for a customer based on definable factors. It considers multiple parameters and their interdependencies for risk score computation. Various protocols are imbibed in the process to arrive at customer risk rating. Thus, multiple parameters of varied customers are factored-in, thereby catering to diverse requests to arrive at the risk levels. This allows the bank to determine the level of risk they expose themselves to by arriving at risk rating of customers with no manual intervention. The parameters, rules and dependencies considered for the risk score and rating computation considered for the risk score and risk rating computation are fully definable. Any future change of these underlying factors and rules used for computation can be absorbed by this service without code change. The invention provides flexibility and ease of use of the application coupled with a design which reflects risk in its entirety. In an example embodiment, the user provides the service with customer parameters. The configurable rule based Customer Due Diligence (CDD) begins by identifying the parameters required for computation of risk score. The information submitted by user is sorted and classified by the underlying configuration. The necessary computations are done using the defined criteria and dependencies that matches each case. These criterions are fully definable by the bank, thereby ensuring relevance in each scenario. The rules for any specific or exceptional cases are executed to compute risk score. Subsequently, risk rating is received by user as output based on defined thresholds. The vibrancy of processing environment is catered to by providing configurability for all the underlying parameters, rules and dependencies. Moreover, no manual intervention will be required to arrive at the risk rating once the customer parameters are provided as inputs, as the process is handled by underlying configuration.

In an advantageous aspect, the system and method of the present invention is robust to absorb multiple real time changes and function without interruptions in case of update of factors as well as their interdependencies. The real time changes are due to modification in external factors or regulatory requirements. Moreover, the interdependencies of the entity parameters considered for risk rating are also subject to change, leading to an expected variation in the risk rating of the entity. The invention provides a configurable risk rating application, which allows configuration of multiple models of risk rating entities. The variation of interdependencies and its effect on the rating is handled by protocols. New models and the protocols are configured at any juncture, thereby allowing addition or update of entity customer parameters and its interdependencies dynamically.

Further, certain portions of the invention may be implemented as a “component” or “system” that performs one or more functions. These components/systems may include hardware, such as a processor, an ASIC (Application Specific Integrated Circuit), or a FPGA (Field Programmable Gate Array), or a combination of hardware and software.

The word “exemplary” is used herein to mean “serving as an example.” Any embodiment or implementation described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or implementations.

No element, act, or instruction used in the present application should be construed as critical or essential to the invention unless explicitly described as such. Also, as used herein, the article “a” and “one of” is intended to include one or more items. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise.

Each of the above identified processes corresponds to a set of instructions for performing a function described above. The above identified programs or sets of instructions need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. For example, embodiments may be constructed in which steps are performed in an order different than illustrated, steps are combined, or steps are performed simultaneously, even though shown as sequential steps in illustrative embodiments. Also, the terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” “having,” “containing” and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The above-described embodiments of the present invention may be implemented in any of numerous ways. For example, the embodiments may be implemented using various combinations of hardware and software and communication protocol(s). Any standard communication or network protocol may be used and more than one protocol may be utilized. For the portion implemented in software, the software code may be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers. Such processors may be implemented as integrated circuits, with one or more processors in an integrated circuit component, or any other suitable circuitry. Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, single board computer, micro-computer, or a tablet computer. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.

Also, the various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools or a combination of programming languages, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or a virtual machine. In this respect, the invention may be embodied as a computer readable storage medium (or multiple computer readable media) (e.g., a computer memory, one or more floppy discs, compact discs (CD), optical discs, digital video disks (DVD), magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above. As is apparent from the foregoing examples, a computer readable storage medium may retain information for a sufficient time to provide computer-executable instructions in a non-transitory form. The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that may be employed to program a computer or other processor to implement various aspects of the present invention as discussed above. Additionally, it should be appreciated that according to one aspect of this embodiment, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention. Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Also, data structures may be stored in computer-readable media in any suitable form. Any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including the use of pointers, tags, or other mechanisms that establish relationship between data elements.

It is to be understood that the above-described embodiments are only illustrative of the application of the principles of the present invention. The illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Various modifications and alternative applications may be devised by those skilled in the art in view of the above teachings and without departing from the spirit and scope of the present invention and the following claims are intended to cover such modifications, applications, and embodiments.

Claims

1. A method of risk assessment of at least one entity by an application, the method comprises the steps of: receiving by a processor at least one entity data; storing by seeding the at least one entity data in a database; processing the received data and a set of associated parameters of the data for determining if the received data is a new or modified data wherein a seed data model of the application reconfigures an underlining seeded mapping structure of the application in response to determination of the received data as a new or modified data; analyzing a set of data models stored in a data model database to identify at least one risk model for processing the received data;

Al based processing of a data dependency matrix and the identified risk data model to determine by a controller at least one impacted factor, component, sequence and protocol of the application; and reconfiguring the set of associated parameters, the set of data models and the data dependency matrix for computing a risk score and a risk rating of the at least one entity based on the new or modified data.

2. The method of claim 1 wherein the new or modified data is modification in dependency of entity data or new associated parameter of the data.

3. The method of claim 1 wherein the received data and associated parameters are compared with a set of stored entity data in an entity database to determine if the received data is a new data or a modified data.

4. The method of claim 3 wherein for determining the data as a new or modified data, the received data is identified, sorted, classified and analyzed as part of comparison with the set of stored entity data.

5. The method of claim 1 further comprises the step of generating the data dependency matrix that enables identification of the impacted factors, the components, the sequence and triggers the protocols to process the received data for determining the risk score and risk rating on the at least one entity.

6. The method of claim 1 further comprises storing a set of rules or protocols in a memory to be executed by an Al engine coupled to a processor for generating a plurality of data models associated with the entity data to identify the at least one risk model.

7. A configurable risk assessment system comprises: an electronic user interface configured for operating on a risk assessment application; at least one entity database configured for storing a plurality of entity data and a set of associated parameters of the entity data wherein a new or modified entity data is stored by seeding the data in the database wherein a seed data model of the application reconfigures an underlining seeded mapping structure of the application in response to determination of the received data as the new or modified data; a controller encoded with instructions enabling the controller to function as a bot wherein the controller is configured to perform functions of: analyzing a set of data models stored in a data model database to identify at least one risk model for processing the received data; and Al based processing of a data dependency matrix and the identified risk data model to determine by a controller at least one impacted factor, component, sequence and protocol of the application; and a processor coupled to the controller for reconfiguring the set of associated parameters, the set of data models and the data dependency matrix for computing a risk score and a risk rating of the at least one entity based on the new or modified data.

8. The system of claim 7 wherein the artificial intelligence-based Al engine is configured for processing a plurality of data models associated with the entity data for determining the risk score and risk rating.

9. The system of claim 7 further comprises a Natural Language Processing (NLP) server configured for processing the protocols based on the identified risk data models to determine the risk score and risk rating.

10. The system of claim 7 wherein the user interface and an underlying logic of the risk assessment application is based on the seeded mapping wherein on introduction of the new or modified data the system automatically reconfigures the underlining seeded mapping structure of the application to include the impacted factors, the components, the sequence and the protocol for determination of the risk score and risk rating.

11. The system of claim 10 wherein the application functions on seeded data and generates mapping structures or codes on basis of the seeded data thereby enabling real time processing of received entity data within the application upon receipt of new or modified data.

12. The system of claim 7 wherein the data dependency matrix enables identification of the impacted factors, the components, the sequence and triggers the protocols to process the received data for determining the risk score and risk rating on the at least one entity.

13. The system of claim 7 wherein a set of rules or protocols is stored in a memory to be executed by the Al engine coupled to a processor for generating a plurality of data models associated with the entity data to identify the at least one risk model.

14. A computer-readable non-transitory storage medium storing executable program instructions for risk assessment which when executed by a computer cause the computer to perform operations comprising: receiving by a processor at least one entity data; storing by seeding the at least one entity data in a database; processing the received data and a set of associated parameters of the data for determining if the received data is a new or modified data wherein a seed data model of the application reconfigures an underlining seeded mapping structure of the application in response to determination of the received data as a new or modified data; analyzing a set of data models stored in a data model database to identify at least one risk model for processing the received data; Al based processing of a data dependency matrix and the identified risk data model to determine by a controller at least one impacted factor, component, sequence and protocol of the application; and reconfiguring the set of associated parameters, the set of data models and the data dependency matrix for computing a risk score and a risk rating of the at least one entity based on the new or modified data.

15. The computer-readable storage medium of claim 14 further comprises executable program instructions in a memory to be executed for generating a plurality of data models.