WO2021075952A1

WO2021075952A1 - Estimated interest income

Info

Publication number: WO2021075952A1
Application number: PCT/MY2020/050107
Authority: WO
Inventors: Vijay JOSEPH KAPANEE; Suraj Peringali ARAYADATH; Apurva Kumar; 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 a system for determining and forecasting an estimated interest income of a financial institution. The system includes a data lake configured for receiving and storing a plurality of entity data and associated parameters, a controller encoded with instructions enabling the controller to function as a bot for generating an interest rate scenario based on an input received from an entity wherein the entity groups a plurality of interest rates into interest rate types, an AI engine configured for forecasting an estimated interest rate for the entity based on the interest rate scenario wherein an impact of the estimated interest rate is quantified, an electronic user interface configured for generating an interest rate curve to be selected for interest rate forecasting wherein a forecast horizon is broken into phases and the interest rate forecast for each phase is determined by tweaking the interest rate curve on the interface, and a processor coupled to the AI engine and a cashflow analytics engine wherein the processor is configured processing the plurality of entity data and associated parameters, the estimated interest rate to amortize entity account and forecast the estimated interest income.

Description

ESTIMATED INTEREST INCOME FIELD OF THE INVENTION

The invention embodiments described herein generally relate to processing financial information within a computer system architecture. In various embodiments, the invention more particularly relates to various computer-implemented tools for managing and processing financial information including interest income management data.

BACKGROUND

Financial institutions depend on accurate assessment of assets and labilities for their appropriate functioning on a day to day basis. The institutions accept different types of deposits and pay interest on those deposits, while simultaneously the institutions also purchase assets and earn interest on those assets. The profitability of a financial institution depends on its ability to earn higher interest rates on its assets than it pays for its deposits. Generally, the longer the maturity of an asset the higher the interest rate paid on it. This creates a performance incentive for financial institution managers to buy longer maturity assets. However, if balance in core deposits that may be subject to withdrawal on demand are used to buy longer maturity assets, a potentially serious asset and liability maturity mismatch may be created.

There are several aspects on which the profitability of the financial institution depends including but not limited to economic scenarios, changes in regulatory policies, performance of the assets in which they invest, appropriate risk assessment and management etc. Since various distinct factors are responsible for changes in the overall profitability of the financial institution, it becomes extremely difficult to forecast the situation in future. Moreover, the response time for financial institution to changes in market rates and regulatory benchmark rates with changes in managed rates (board deposit and base lending rates) is also high. There is no real-time response to such changes.

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 for determining and forecasting an estimated interest income of a financial institution. The method includes the steps of receiving and storing a plurality of entity data and associated parameters in a data lake, generating an interest rate scenario based on input received from the entity wherein the entity groups a plurality of interest rates into interest rate types, forecasting an estimated interest rate for the user based on the interest rate scenario; determining a forecast horizon for which the user targets the interest income, generating on an electronic user interface, an interest rate curve to be selected by the user for interest rate forecasting wherein the horizon is broken into phases and the interest rate forecast for each phase is determined by tweaking the interest rate curve on the interface, determining impact of change in the interest rate forecast; and processing by a cashflow analytics engine, the plurality of entity data and associated parameters, the estimated interest rate to amortize entity account and forecast the estimated interest income.

In an embodiment, the present invention provides a system for determining and forecasting an estimated interest income of a financial institution. The system includes a data lake configured for receiving and storing a plurality of entity data and associated parameters, a controller encoded with instructions enabling the controller to function as a bot for generating an interest rate scenario based on an input received from an entity wherein the entity groups a plurality of interest rates into interest rate types, an AI (artificial intelligence) engine configured for forecasting an estimated interest rate for the entity based on the interest rate scenario wherein an impact of the estimated interest rate is quantified, an electronic user interface configured for generating an interest rate curve to be selected for interest rate forecasting wherein the horizon is broken into phases and the interest rate forecast for each phase is determined by tweaking the interest rate curve on the interface, and a processor coupled to the AI engine and a cashflow analytics engine wherein the processor is configured for processing the plurality of entity data and associated parameters, the estimated interest rate to amortize entity account and forecast the estimated interest income.

In an embodiment, the present invention provides a computer-readable non-transitory storage medium storing executable program instructions for data processing for determining and forecasting an estimated interest income of a financial institution which when executed by a computer cause the computer to perform the operations.

In an advantageous aspect, the cashflow analytics engine is configured to amortize entity- accounts that can also consider entity behaviors like Non-Maturing behavioral Maturity, Non- Performing and Overdues, Prepayments and Redemptions, Roll-overs and Roll-intos. Further, the system of the present invention relates to a highly complex, configurable and callable application which is built from grounds up to cater to the above requirements. It supports multiple Behavioral methodologies along with designer payment patterns, industry standard Amortization/Accrual/Compounding methods .

The system of the present invention suggests new asset additions (volume and type) based on the present interest rate scenario and forecasted interest rate scenario, within a target horizon. Further, the system mandates the new asset additions have to be supported by new sources of funding and existing business roll-overs.

The system of the present invention enables balance sheet balancing by the right sources of funding (liabilities and capital) being prompted by the applications, thus helping the bank with balance sheet optimization and planning. Further, the system, method and applications assist in creating the optimal balance sheet by allowing the bank to respond to the effect of market rates on the managed rate products (savings rates, term deposit rates, base lending rates, etc.).

In another advantageous aspect, the system is able to make ad-hoc calls to the cashflow engine to re-amortize accounts and generate interest cashflows for consumption.

DESCRIPTION OF THE DRAWINGS

Fig. 1 shows a system architecture for determining and forecasting an estimated interest income of a financial institution in accordance with an embodiment of the present invention.

Fig. 2 shows a block diagram of the estimated net interest income system application is shown in accordance with an embodiment of the present invention. Fig. 2a an architectural block diagram of the estimated net interest income system application is shown in accordance with an embodiment of the present invention.

Fig. 3 shows a cashflow analytics flow diagram 300 is shown in accordance with an embodiment of the present invention.

Fig. 4 shows a flowchart depicting a method for determining and forecasting an estimated interest income of a financial institution in accordance with an embodiment of the present invention.

DESCRIPTION OF THE INVENTION

Various embodiments of the present invention provide data processing systems and methods for determining and forecasting an estimated interest income of a financial institution. 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 must 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 combinati ons of one or more of the associated listed items. Spatially relative terras, such as “interest rate,” “interest income,” “forecasting” 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 data processing system and method for determining and forecasting an estimated interest income of a financial institution.

Referring to Fig. 1, a system architecture 100 for determining and forecasting an estimate interest income of a financial institution in accordance with an embodiment of the present invention is disclosed. The system 100 includes at least one computing device 110, a server support architecture 120, a data processing and control support architecture/mechanism 130 and a data storage support architecture 140. The server support architecture may include server 120a. The data processing and control support architecture/mechanism 130 may include a processor 130a, a controller 130b, an AI engine 130c, a cash flow analytics server 130d with cash low analytics engine 132 and proxy server 134. The data storage support architecture 140 may include a data lake 150 configured for storing a plurality of entity data and data models. In an embodiment, the proxy server 134 is hit from the processor 130a, which balances the load between the multiple servers for cash flow engine 132. The cash flow engine 132 is deployed on multiple servers for best performance.

In an embodiment, the AI engine 130c enables functioning of a chat bot feature with certain predefined question and answer support.

In an embodiment, the data lake 150 is configured for receiving and storing a plurality of entity data and associated parameters.

In an embodiment, the controller encoded with instructions enabling the controller to function as a bot is configured for generating an interest rate scenario based on an input received from an entity wherein the entity groups a plurality of interest rates into interest rate types.

In an embodiment, the AI engine configured for forecasting an estimated interest rate for the entity based on the interest rate scenario wherein an impact of the estimated interest rate is quantified.

In an embodiment, the present invention includes an electronic user interface configured for generating an interest rate curve to be selected for interest rate forecasting wherein the horizon is broken into phases and the interest rate forecast for each phase is determined by tweaking the interest rate curve on the interface.

In an embodiment, the processor coupled to the AI engine and a cashflow analytics engine wherein the processor is configured processing the plurality of entity data and associated parameters, the estimated interest rate to amortize entity account and forecast the estimated interest income.

In an example embodiment the server 120a may include electronic circuitry for enabling execution of various steps by the processor 130a. 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 of the data lake 150 and associated with at least one interest rate scenario to determine the interest rate for these scenarios. 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 (graphical user interface) 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 data storage support architecture 140 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 magnetic or optical disk. The data storage 140 may also include storage device capable of providing mass storage. In one implementation, the storage device may be or contain a 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 AI engine is an analyzer enables forecasting of the interest rates, computation of interest cash flows and estimation of a net interest income.

In an embodiment, the electronic user interface is a visualizer which provides a graphical representation of the interest rate curve and enables tweaking of interest rate term points in the interest rate curve to estimate the impact of change in the interest rate forecast.

In an embodiment, a difference between the estimated interest rate by the cashflow engine and an existing interest rate determines an interest rate sensitivity. With the change in rate forecasts, the user can immediately see the impact/sensitivity of the tweaked rates as Earning/Expense Sensitivity = New Earning/Expense - BAU (Business As Usual) Earning/Expense .

In an embodiment, the impact of change in an estimated interest rate is quantified.

In an embodiment, the data lake further comprises a plurality of data model configured to generate the interest rate scenario.

Referring to Fig. 2, a block diagram 200 of the estimated net interest income application is shown in accordance with an embodiment of the present invention. The application includes the cash flow engine 132 and the estimated interest income user interface 210 through which interest rate type, profitability target horizon, and interest rate forecast are defined. The cash flow engine 132 processes amortization method, accrual basis method, compounding methods.

Referring to Fig. 2a, an architectural block diagram 200a of the estimated net interest income application is shown in accordance with an embodiment of the present invention. The system application includes a funding segmentation block 220 that segments the funds by funding type, product type or segment type. The block 220 also considers other dimensions such as volume, historical trend, growth, stickiness (improvement strategies) and cost of liquidity. The system also considers a non-behaviouralization block 230 that includes cohorting, time series analysis of tiered and non-tiered deposits (cliff points), deposit NCC % and amortization pattern design.

Referring to Fig. 3, a cashflow analytics flow diagram 300 is shown in accordance with an embodiment of the present invention. The analytics includes liquidity cash flows 310, interest rate cash flows 320, interest rate risk strategies 330, balance strategies 340, market values 350 and discounted cashflows 360, cashflow curves 370a and discount curves 370b. The liquidity cash flow employs fixed rate method 312 or implied forward method 314 where, interest rate curve is spotted and forwarded to interest risk strategy block.

In an embodiment, the analytics include multiple scenarios related to interest rate cashflows 320 with maturity at reprising. The scenarios include implied forward rates 320a, EMI (Equated Monthly Installments) 320b as missing EMI, non-base rate and base rate.

In another embodiment, balance strategies 340 include scenarios 340a at maturity such as irregular payment, releasing loans, and rate stage loans.

Referring to Fig. 4, a flowchart 400 depicting a method for determining and forecasting an estimated interest income of a financial institution is provided in accordance with an embodiment of the present invention. The method includes the steps of: (S410) receiving and storing a plurality of entity data and associated parameters in a data lake; (S420) generating an interest rate scenario based on input received from the entity wherein the entity groups a plurality of interest rates into interest rate types; (S430) forecasting an estimated interest rate for the user based on the interest rate scenario; (S440) determining a forecast horizon for which the user targets the interest income; (S450) generating on an electronic user interface, an interest rate curve to be selected by the user for interest rate forecasting wherein the horizon is broken into phases and the interest rate forecast for each phase is determined by- tweaking the interest rate curve on the interface; (S460) determining impact of change in the interest rate forecast; and (S470) processing by a cashflow analytics engine, the plurality of entity data and associated parameters, the estimated interest rate to amortize entity account and forecast the estimated interest income.

In an embodiment, the interest rate type includes tracker rates, managed rates and fixed rates.

In some implementations, the machine learning data model is configured and trained to map data from distinct sources and the interest rate scenario with data processing rule to compute the interest rate. 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 and trained to map minimum data from distinct sources for determining and forecasting interest income. 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, assuming the 3month KLIBOR current rate is 3.69% and the interest earned from 3 month KLIBOR linked loans is 100 million Ringgit (over a 6months horizon), earned from 1 billion Ringgit of Assets. The user feels that the 3 month rate could increase to 3.75% from 1 month to 3months time phase and 3.77% from 3 months to 6months time phase. In such a scenario, it needs to be determined as to what would be the impact on the Bank’s earnings from these 3 Month KLIBOR linked Loans?

In the above scenario, the information about the 1 billion Ringgit worth of loans is sent to the cashflow analytics engine, which amortizes the loans with the new forward rates, as mentioned above. The new interest cashflows, for example 110 million Ringgit (over the 6 months horizon), are sent hack by the engine to the estimated interest income system application for it to report.

In an advantageous aspect, the system can provide results at higher scale, with high speed and accuracy, thereby enabling the financial institution like bank to respond to a volatile interest rate environment with speed and confidence.

In an another aspect, the system provides the ability to forecast interest rates and dynamic balance sheet cashflow forecasts along with strategies for on and off balance sheet products that would help the bank gauge its earnings potential, and/or help strengthen the capital base and provide the bank’s investors adequate returns. Further, the system enables a user to process large volume calculations on the current balance sheet granular data, create interest rate forecasts, forecast interest income and expenses, and conduct component analysis and visualization on the processed information. The system enables the institution like bank to conduct a quick analysis of the bank’s estimated net interest income over a forecast horizon, using the latest granular balance sheet data. Further, this enables the banks to give a quick and accurate response to regulatory actions, as an advantage over competing banks and financial institutions .

In an advantageous aspect, the invention provides a navigator to guide through the application. The system provides optimum solutions based on the result of the user actions, application to suggest the next course of action.

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 data processing method for determining and forecasting an estimated interest income of a financial institution, comprising the steps of: receiving and storing a plurality of entity data and associated parameters in a data lake; generating an interest rate scenario based on input received from the entity wherein the entity groups a plurality of interest rates into interest rate types; forecasting an estimated interest rate for the user based on the interest rate scenario; determining a forecast horizon for which the user targets the interest income; generating on an electronic user interface, an interest rate curve to be selected by the user for interest rate forecasting wherein the horizon is broken into phases and the interest rate forecast for each phase is determined by tweaking the interest rate curve on the interface; determining impact of change in the interest rate forecast; and processing by a cashflow analytics engine, the plurality of entity data and associated parameters, the estimated interest rate to amortize entity account and forecast the estimated interest income.

2. The method of claim 1 w'herein the interest rate type includes tracker rates, managed rates and fixed rates.

3. The method of claim 1 wherein an analyzer enables forecasting of the interest rates, computation of interest cash flows and estimation of a net interest income.

4. The method of claim 2 wherein a visualizer provides a graphical representation of the interest rate curve and enables tweaking of interest rate term points in the interest rate curve to estimate the impact of change in the interest rate forecast.

5. The method of claim 1 wherein a difference between the estimated interest rate by the cashflow engine and an existing interest rate determines an interest rate sensitivity.

6. The method of claim 1 wherein the impact of change in the estimated interest rate is quantified.

7. A data processing system for determining and forecasting an estimated interest income of a financial institution, the system comprises: a data lake configured for receiving and storing a plurality of entity data and associated parameters; a controller encoded with instructions enabling the controller to function as a bot for generating an interest rate scenario based on an input received from an entity wherein the entity groups a plurality of interest rates into interest rate types; an AI engine configured for forecasting an estimated interest rate for the entity based on the interest rate scenario wherein an impact of the estimated interest rate is quantified; an electronic user interface configured for generating an interest rate curve to be selected for interest rate forecasting wherein the horizon is broken into phases and the interest rate forecast for each phase is determined by tweaking the interest rate curve on the interface; and a processor coupled to the AI engine and a cashflow analytics engine wherein the processor is configured processing the plurality of entity data and associated parameters, the estimated interest rate to amortize entity account and forecast the estimated interest income.

8. The system of claim 7 wherein the AI engine is an analyzer that enables forecasting of the interest rates, computation of interest cash flows and estimation of a net interest income.

9. The system of claim 7 wherein the electronic user interface is a visualizer that provides a graphical representation of the interest rate curve and enables tweaking of interest rate term points in the interest rate curve to estimate the impact of change in the interest rate forecast.

10. The system of claim 7 wherein the data lake further comprises a plurality of data model configured to generate the interest rate scenario.

11. A computer-readable non-transitory storage medium storing executable program instructions for data processing for determining and forecasting an estimated interest income of a financial institution which when executed by a computer cause the computer to perform operations comprising: receiving and storing a plurality of entity data and associated parameters in a data lake; generating an interest rate scenario based on input received from the entity wherein the entity groups a plurality of interest rates into interest rate types; forecasting an estimated interest rate for the user based on the interest rate scenario; determining a forecast horizon for which the user targets the interest income; generating on an electronic user interface, an interest rate curve to be selected by the user for interest rate forecasting wherein the horizon is broken into phases and the interest rate forecast for each phase is determined by tweaking the interest rate curve on the interface; determining impact of change in the interest rate forecast; and processing by a cashflow analytics engine, the plurality of entity data and associated parameters, the estimated interest rate to amortize entity account and forecast the estimated interest income.

12. The computer-readable storage medium of claim 11 further comprises executable program instructions in a memory to be executed for generating interest rate scenario by a plurality of data models.

13. The computer-readable storage medium of claim 12 further storing instructions that cause the processor to automatically add storage for storing the data models.