System and method for modeling and quantifying regulatory capital, key risk indicators, probability of default, exposure at default, loss given default, liquidity ratios, and value at risk, within the areas of asset liability management, credit risk, market risk, operational risk, and liquidity risk for banks

• US 10,453,142 B2
• Filed: 11/19/2014
• Issued: 10/22/2019
• Est. Priority Date: 02/11/2009
• Status: Active Grant

• Current Assignee: Johnathan Mun
• Original Assignee: Johnathan Mun
• Inventors: Mun, Johnathan
• Primary Examiner(s): Jones, Hugh M
• Application Number:
  US14/547,225
• Publication Number:
  US 20150088783A1
• Time in Patent Office: 1,798 Days
• Field of Search: 703 2
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

The present invention is in the field of modeling and quantifying Regulatory Capital, Key Risk Indicators, Probability of Default, Exposure at Default, Loss Given Default, Liquidity Ratios, and Value at Risk, using quantitative models, Monte Carlo risk simulations, credit models, and business statistics, and relates to the modeling and analysis of Asset Liability Management, Credit Risk, Market Risk, Operational Risk, and Liquidity Risk for banks or financial institutions, allowing these firms to properly identify, assess, quantify, value, diversify, hedge, and generate periodic regulatory reports for supervisory authorities and Central Banks on their credit, market, and operational risk areas.

Claims

1. A computerized method for integrating risk management methodologies, risk modeling, and risk analytics into a computing device, the method comprising:

generating a user interface on a display of a computing device, said user interface comprising a plurality of user-selectable risk tabs comprising;

(1) a credit risk tab, (2) a market risk tab, and (3) an operational risk tab,

wherein selection of said credit risk tab causes a processor of said computing device to generate a drop down list of selectable credit loan types comprising;

(1) residential mortgages, (2) revolving credit, (3) other miscellaneous credit, and (4) wholesale corporate and sovereign debt,

wherein each credit loan type has an associated risk model that automatically calculates a credit risk for said credit loan type,

receiving a selected credit loan type;

receiving historical data for said credit loan type;

automatically mapping by the processor said historical data to a plurality of variables required for calculating a credit risk for said credit loan type, wherein said variables comprise;

credit issue data, customer information, product type, central bank ratings, amount of the loan, interest payment, principal payment, and last payment date;

receiving a credit value at risk (VaR) percentile as input in said interface;

automatically calculating and automatically reporting by the processor a value at risk (VaR) based, at least in part, on said VaR percentile;

automatically calculating and reporting by the processor said credit risk using the model corresponding to said credit loan type, wherein said credit risk is reported using Key Risk Indicators (KRI) that include Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and Expected Losses (EL), and wherein said credit risk comprises;

(1) a probability of default (PD) that is based, at least in part, on a percentage of defaults for said credit type over a specified period, (2) a loss given default (LGD) which is a percentage of losses of loans that cannot be recovered in the event of default over said period, (3) an exposure at default (EAD) which is the total amount of loans outstanding over said period, (4) expected losses (EL) which is the product of PD, LGD, and EAD, and (5) required economic capital based on Basel II and Basel III requirements, and automatically create a repository of historical and forward-looking KRI results, and automatically plots and displays one or more graphs showing past or forecast changes in KRI over time.

2. The computerized method of claim 1, wherein selection of said market risk tab causes said processor to generate a market risk user interface on said display, said market risk user interface comprising a market data tab, a value at risk (VaR) tab, a central bank VaR tab, and a Results Visual tab that is configured to automatically display the results, wherein said market risk user interface is configured to receive inputted data comprising:

a number of investment assets and currency assets held in a portfolio;

a current balance for each asset and currency entered, wherein an asset position is calculated for each of said investment and currency assets by multiplying the number of each asset by the current balance for the respective asset;

a total number of historical data points that are used to automatically perform a market risk analysis;

a volatility calculation method;

and said computerized method further comprises the steps of;

automatically calculating and automatically reporting a market value at risk (market VaR) by multiplying at least one of said asset positions by;

(1) a result of said volatility calculation method, (2) an inverse standard normal distribution of said market VaR percentile, and (3) the square root of a time horizon in days for holding said investment and currency assets; and

providing an update option for automatically recalculating said market value at risk (VaR).

3. The computerized method of claim 1, wherein said user interface further comprises an advanced analytics tab,

wherein selection of said advanced analytics tab causes said processor to generate a key risk indicator (KRI) tab, a scenarios tab, a risk simulation tab,

wherein selection of said KM tab causes said processor to generate a KRI dashboard comprising a plurality of automatically generated key risk indicator charts.

4. The computerized method of claim 3, wherein selection of said scenarios tab causes said processor to generate a scenario builder interface, wherein said scenario builder interface provides an option to run interest rate scenarios to automatically calculate an effect of simulated interest rate changes on a set of assets and liabilities, and a second option to run a net interest margin analysis to automatically determine the effect of changed interest rates on net interest margin, wherein said scenario builder interface is configured to automatically receive a plurality of parameters that define different interest rate scenarios, said parameters comprising a range of basis points and a step size, such that the effects of said interest rate changes at each step are automatically calculated and the automatically generated results are automatically displayed in said scenario builder interface.

5. The computerized method of claim 4, wherein each of said automatically generated results is automatically displayed in a color that corresponds to a predetermined value condition satisfied by said result.

6. The computerized method of claim 3, wherein selection of said risk simulation tab causes said processor to generate a risk simulation interface configured to receive:

a starting asset portfolio value,

a portfolio holding period in years,

asset information for each asset in said portfolio, wherein individual asset information comprises;

an asset name,

historical returns for the asset,

asset volatility,

current asset allocation weight,

minimum and maximum allocation weight,

correlation between asset pairs,

wherein, said risk simulation interface provides an option for automatically running a monte carlo simulation on said portfolio value, based at least in part, on said portfolio holding period and said asset information.

7. A computerized method for integrating risk management methodologies, risk modeling, and risk analytics into a computing device, wherein the method generates a user interface on a display of a computing device, said user interface comprising a plurality of user-selectable risk tabs comprising an asset liability management (ALM) tab, wherein selection of said asset liability management (ALM) tab causes said processor to generate an ALM user interface on said display, said ALM user interface comprising an interest rate risk tab and a liquidity risk tab, wherein selection of said interest rate risk tab automatically generates an input assumptions tab, a gap analysis tab, an economic value of equity tab, and a net income margin tab, wherein selection of said input assumptions tab generates a settings tab, a rate sensitive assets &

liabilities tab, and a historical interest rates tab, wherein selection of said settings tab generates an ALM user interface configured to receive inputted data comprising;

a bank regulatory capital requirement, which is a multiple of capital needed to avoid insolvency at a specified degree of probability;

a number of trading days in the current calendar year;

a local currency;

a time period for performing an ALM analysis;

a number of Value at Risk (VaR) percentiles to run;

a number of scenarios to run;

a basis point sensitivity for each scenario; and

foreign currencies held in an investment portfolio;

wherein selection of said rate sensitive assets &

liabilities tab causes said processor to generate a table for receiving asset and liability data;

wherein selection of said historical interest rates tab causes said processor to generate a table for receiving historical interest rate data;

wherein selection of said gap analysis tab causes said processor to generate gap analysis results based on;

(1) the inputted data received in said ALM interface, (2) the asset and liability data, and (3) the historical interest rate data, wherein said gap analysis results are automatically generated for said local currency and any inputted foreign currencies;

wherein selection of said economic value of equity tab causes said processor to automatically generate a chart that indicates an impact on regulatory capital requirements based on interest rate risks defined by said gap analysis results;

wherein selection of said net income margin tab causes said processor to generate a second input assumptions tab and a net income margin (NIM) results tab, wherein selection of said second input assumptions tab automatically generates a table configured to receive inputted data required to automatically calculate a net income margin (NIM);

wherein selection of said NIM results tab causes said processor to automatically generate a chart that shows a net interest income impact based on automatically simulated changes in interest rates, as determined by a specified increase in basis points;

wherein selection of said liquidity risk tab causes said processor to generate a third input assumptions tab, a scenario analysis tab, a stress testing tab, a gap analysis tab, and a charts tab, wherein selection of said third input assumptions tab automatically generates a table for receiving cash flows and historical monthly balances for different classes of assets and liabilities;

wherein selection of said scenario analysis tab causes said processor to generate a user interface configured to receive different scenarios for automatically testing how said assets and liabilities respond to changes in interest rates;

wherein selection of said stress testing tab generates a table that automatically shows fluctuations in said liabilities in response to extreme conditions where deposits are reduced to a lower limit, wherein said lower limit can be entered as a value or percentage change from a base case;

wherein selection of said gap analysis tab causes said processor to generate a table that shows results of the automatic testing of any previously entered scenarios and automatically generated fluctuation displays of entered stress test conditions, wherein a gap is the difference between monthly assets and liabilities;

wherein selection of said analytical models tab causes said processor to generate a structural tab, a time series tab, a portfolio tab, and an analytics tab, wherein selection of the structural models tab causes said processor to generate a user interface for selecting an analysis category, wherein each analysis category has one or more associated mathematical models that may be selected for use in automatically performing calculations as part of the analysis denoted by said analysis category;

wherein, the user selects a number of times the selected model is automatically computed and the degree of precision of the automated results.

8. A system configured to perform financial risk analytics and provide financial risk awareness, said system comprising:

at least one non-transitory computer-readable storage medium on which a plurality of credit risk models are stored;

a sub-system configured to generate at least one graphical user interface (GUI) for automatic display on a computer-based visual sub-system, said GUI comprising a plurality of user-selectable risk tabs comprising;

(1) a credit risk tab, (2) a market risk tab, and (3) an operational risk tab;

at least one sub-system configured to receive input from a user in the form of a selection of one of said user-selectable risk tabs and other input configured by a user in said interactive GUI, wherein said other input includes;

a credit value at risk (VaR) percentile;

a computer machine configured to;

receive said selection of one of said user-selectable tabs as computer machine input;

wherein selection of said credit risk tab causes said GUI sub-system to generate a drop down list of selectable credit loan types comprising;

(1) residential mortgages, (2) revolving credit, (3) other miscellaneous credit, and (4) wholesale corporate and sovereign debt,

wherein each credit loan type has an associated credit risk model stored in said non-transitory computer-readable storage medium that is used to automatically calculate a credit risk for said credit loan type, receive a selected credit loan type;

automatically receive or automatically request historical data for said credit loan type from at least one of;

a remote server, a selected file stored in said non-transitory computer-readable storage medium, and manual entry of said historical data using said user GUI;

automatically map said historical data to a plurality of variables required for calculating said credit risk for said credit loan type, wherein said variables comprise;

credit issue data, customer information, product type, central bank ratings, loan amount, interest payment, principal payment, and last payment date;

automatically calculate and automatically report a value at risk (VaR) based, at least in part, on said VaR percentile;

automatically calculate and report said credit risk based, at least in part, on the model corresponding to said credit loan type, wherein said credit risk is reported using Key Risk Indicators (KRI) that include Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and Expected Losses (EL), and wherein said credit risk comprises;

(1) a probability of default (PD) that is based, at least in part, on a percentage of defaults for said credit type over a specified period, (2) a loss given default (LGD) which is a percentage of losses of loans that cannot be recovered in the event of default over said period, (3) an exposure at default (EAD) which is the total amount of loans outstanding over said period, (4) expected losses (EL) which is the product of PD, LGD, and EAD, and (5) required economic capital based on Basel II and Basel III requirements, and automatically create a repository of historical and forward-looking KRI results, and automatically plots and displays one or more graphs showing past or forecast changes in KRI over time.

9. A system configured to perform financial risk analytics and provide financial risk awareness, said system comprising:

a processor;

a memory;

a display;

a non-transitory computer-readable storage medium on which a plurality of credit risk models are stored and accessed by said processor;

a network communications device;

computer readable instructions residing in said memory, wherein said instructions are configured to;

generate a user interface on said display, said user interface comprising a plurality of user-selectable risk tabs comprising;

(1) a credit risk tab, (2) a market risk tab, and (3) an operational risk tab,

wherein selection of said credit risk tab causes said processor to generate a drop down list of selectable credit loan types comprising;

(1) residential mortgages, (2) revolving credit, (3) other miscellaneous credit, and (4) wholesale corporate and sovereign debt,

wherein each credit loan type has an associated credit risk model stored in said non-transitory computer-readable storage medium that is used to automatically calculate a credit risk for said credit loan type, receive a selected credit loan type;

automatically receive or automatically request historical data for said credit loan type from at least one of;

a remote server using said network communications device, a selected file stored in said non-transitory computer-readable storage medium, and manual entry of said historical data using said user interface;

automatically map said historical data to a plurality of variables required for calculating said credit risk for said credit loan type, wherein said variables comprise;

credit issue data, customer information, product type, central bank ratings, loan amount, interest payment, principal payment, and last payment date;

receive a credit value at risk (VaR) percentile as input in said interface;

automatically calculate and automatically report a value at risk (VaR) based, at least in part, on said VaR percentile;

automatically calculate and automatically report said credit risk based, at least in part, on the model corresponding to said credit loan type, wherein said credit risk is reported using Key Risk Indicators (KRI) that include Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and Expected Losses (EL), and wherein said credit risk comprises;

(1) a probability of default (PD) that is based, at least in part, on a percentage of defaults for said credit type over a specified period, (2) a loss given default (LGD) which is a percentage of losses of loans that cannot be recovered in the event of default over said period, (3) an exposure at default (EAD) which is the total amount of loans outstanding over said period, (4) expected losses (EL) which is the product of PD, LGD, and EAD, and (5) required economic capital based on Basel II and Basel III requirements, and automatically create a repository of historical and forward-looking KRI results, and automatically plots and displays one or more graphs showing past or forecast changes in KRI over time.

10. A computer-implemented system for providing a user with a set of interoperable functions and options facilitating the structured data input and analysis of credit, market, operational, business, and liquidity risk, comprising:

an Asset Liability Management, Credit, Market, Operational, and Liquidity Risk (ALM-CMOL) module comprising physical memory storing instructions that cause the ALM-CMOL module to;

provide a user, via said ALM-CMOL module, a user interface for integrated risk modeling, evaluation of options, and analysis of Asset Liability Management, Credit, Market, Operational, and Liquidity risk;

present said user with a plurality of integrated risk modeling and analysis methodologies comprising quantitative models, Monte Carlo risk simulations, credit models, business statistical models, portfolio optimization, wherein said integrated risk modeling and analysis methodologies are applied to automatically model and automatically quantify Regulatory Capital, Key Risk Indicators (KRI), Probability of Default (PD), Exposure at Default (EAD), Loss Given Default (LGD), Liquidity Ratios, Value at Risk (VaR), and Operational Risk,

wherein said user interface comprises four rows of functions, the first row comprising a plurality of user selectable primary modes, including;

Credit Risk mode, Market Risk mode, Asset Liability Management mode, Analytical Models mode, Advanced Analytics mode, and Operational Risk mode,

wherein selecting a primary mode automatically populates a second row with user selectable functions associated with the selected primary mode, where each of said second row functions is either a primary option tab or a secondary mode,

wherein selecting a secondary mode automatically populates a third row with user selectable functions associated with the selected secondary mode, where each of said third row functions is either a secondary option tab or a tertiary mode,

wherein selecting a tertiary mode automatically populates a fourth row with user selectable functions associated with the selected tertiary mode, where each of said fourth row functions is a tertiary option tab,

wherein said Credit Risk mode is configured to automatically perform a Credit Risk analysis using a combination of inputted historical loan data and credit risk parameters to automatically calculate Probability of Default (PD), Exposure at Default (EAD), Expected Losses (EL), Economic Capital (EC), and Regulatory Capital (RC),

wherein said Market Risk mode is configured to automatically perform a Market Risk analysis using a combination of inputted historical asset data and market risk parameters to automatically calculate and automatically report the Market Value at Risk (VaR) over a time series,

wherein said Asset Liability Management mode is configured to automatically perform an Asset Liability Risk analysis to automatically quantify and manage risks arising from mismatches between a set of inputted assets and liabilities in a portfolio, where said Asset Liability Management mode comprises an Interest Rate Risk secondary mode for automatically calculating the effect of interest rate risk on said portfolio, said Asset Liability Management mode comprising a Gap Analysis, Economic Value of Equity analysis, Net Income Margin (NIM) analysis, and a Liquidity Risk secondary mode for automatically calculating the fluctuations in assets and liabilities and the impact of said fluctuations on the asset-liability management balance of a portfolio of said assets and liabilities, and wherein said Asset Liability Management mode is configured to display an automatically generated yield curve and its numerical interest rates, and to display in the effects of interest rate fluctuations on the Asset Liability Management (ALM) Interest Rate model and Net Interest Margin (NIM) models in an automatically generated results area,

wherein said Analytical Models mode is configured to automatically analyze historical performance levels and automatically forecast simulated performance of a set of inputted historical performance data elements using a plurality of Analytical Model categories comprising Structural, Time Series, Portfolio, and Analytics categories,

wherein said Advanced Analytics mode is configured to automatically;

calculate Economic Capital (EC) and Economic Regulatory Capital (ERC) requirements for said historical performance data elements using Monte Carlo risk simulation, historical bootstrap simulation, and scenario analysis;

optimize investment allocation weights and minimize the Value at Risk (VaR) for a set of said historical performance data elements, where said historical performance data elements comprise a portfolio of assets and liabilities;

simulate the effect of extreme market events on said historical performance data elements using Monte Carlo risk simulations to calculate the Value at Risk (VaR) and best-fitting extreme value distributions;

calculate the effects of interest rate fluctuations by modeling interest rate yield curves using said historical performance data elements;

wherein said Operational Risk mode is configured to automatically perform an Operational Risk analysis for a set of inputted historical or current operational risk events selected from a group of operational risk categories consisting of litigation risks, security risks, reputation risks, fraud risks, information technology risks, staffing risks, human resource risks, and development risks, wherein all entered and automatically calculated operational risk events are automatically reported, summarized and displayed by monthly or yearly periods aggregated by risk segments and counts, and wherein said operational risk events are automatically charted and displayed as control charts that show specific risk events as in- or out-of-control,

wherein said Operational Risk analysis comprises;

automatically calculating the probability of occurrence for each of said operational risk events using probability density and cumulative distribution functions;

automatically analyzing said set of operational risk events using control charts to automatically determine whether each of said risk events are in or out of control; and

automatically calculating the expected simulated losses for said set of operational risk events using Monte Carlo risk simulations and probabilistic loss distributions,

wherein said user interface further comprises a Data Prep function within one of said primary modes, which automatically analyzes one or more user-inputted standard financial statements and automatically generates a set of values and parameters corresponding to the required inputs of one or more of said Credit Risk Analysis, Market Risk Analysis, and Asset Liability Risk Analysis modes, and

wherein the user interface automatically creates a repository of historical and forward-looking KM results comprising Value at Risk (VaR), Loss Given Default (LGD), Exposure at Default (EAD), Economic Capital (EC), and Economic Regulatory Capital (ERC), and automatically plots and displays one or more graphs showing past or forecast changes in KM over time.

11. The computer-implemented method of claim 10, wherein the automatically created KRI results are shown in heat map matrixes that are automatically displayed as a KM heat map and plotted in time-series charts to show said KM levels over time;

and

wherein the heat map comprises one or more risks type and periodicity based on a plurality of historical performance levels and based on stress-tested, scenario-based, simulated future forecast levels; and

wherein said heat map is color coded to show a plurality of historical performance levels.

12. The computer-implemented method of claim 11, wherein the ALM-CMOL module is configured to automatically perform historical performance mapping and automatically display results that show how each of one or more historical performance data elements affects each risk type.

13. A computer-implemented method for providing a user with a set of interoperable functions and options facilitating the structured data input and analysis of credit, market, operational, business, and liquidity risk, comprising the steps of:

providing a user with an Asset Liability Management, Credit, Market, Operational, and Liquidity Risk (ALM-CMOL) module comprising physical memory storing instructions that cause the ALM-CMOL module to;

provide said user, via said ALM-CMOL module, a user interface for integrated risk modeling, evaluation of options, and analysis of Asset Liability Management, Credit, Market, Operational, and Liquidity risk;

present said user with a plurality of integrated risk modeling and analysis methodologies comprising quantitative models, Monte Carlo risk simulations, credit models, business statistical models, portfolio optimization, wherein said integrated risk modeling and analysis methodologies are applied to automatically model and automatically quantify Regulatory Capital, Key Risk Indicators (KRI), Probability of Default (PD), Exposure at Default (EAD), Loss Given Default (LGD), Liquidity Ratios, Value at Risk (VaR), and Operational Risk,

wherein said user interface comprises four rows of functions, the first row comprising a plurality of user selectable primary modes, including;

Credit Risk mode, Market Risk mode, Asset Liability Management mode, Analytical Models mode, Advanced Analytics mode, and Operational Risk mode,

wherein selecting a primary mode automatically populates a second row with user selectable functions associated with the selected primary mode, where each of said second row functions is either a primary option tab or a secondary mode,

wherein selecting a secondary mode automatically populates a third row with user selectable functions associated with the selected secondary mode, where each of said third row functions is either a secondary option tab or a tertiary mode,

wherein selecting a tertiary mode automatically populates a fourth row with user selectable functions associated with the selected tertiary mode, where each of said fourth row functions is a tertiary option tab,

wherein said Credit Risk mode is configured to automatically perform a Credit Risk analysis using a combination of inputted historical loan data and credit risk parameters to automatically calculate Probability of Default (PD), Exposure at Default (EAD), Expected Losses (EL), Economic Capital (EC), and Regulatory Capital (RC),

wherein said Market Risk mode is configured to automatically perform a Market Risk analysis using a combination of inputted historical asset data and market risk parameters to automatically calculate and automatically report the Market Value at Risk (VaR) over a time series,

wherein said Asset Liability Management mode is configured to automatically perform an Asset Liability Risk analysis to automatically quantify and manage risks arising from mismatches between a set of inputted assets and liabilities in a portfolio, where said Asset Liability Management mode comprises an Interest Rate Risk secondary mode for automatically calculating the effect of interest rate risk on said portfolio, said Asset Liability Management mode comprising a Gap Analysis, Economic Value of Equity analysis, Net Income Margin analysis, and a Liquidity Risk secondary mode for automatically calculating the fluctuations in assets and liabilities and the impact of said fluctuations on the asset-liability management balance of a portfolio of said assets and liabilities,

wherein said Analytical Models mode is configured to automatically analyze historical performance levels and automatically forecast simulated performance of a set of inputted historical performance data elements using a plurality of Analytical Model categories comprising Structural, Time Series, Portfolio, and Analytics categories,

wherein said Advanced Analytics mode is configured to automatically;

calculate Economic Capital (EC) and Economic Regulatory Capital (ERC) requirements for said historical performance data elements using Monte Carlo risk simulation, historical bootstrap simulation, and scenario analysis;

optimize investment allocation weights and minimize the Value at Risk (VaR) for a set of said historical performance data elements, where said historical performance data elements comprise a portfolio of assets and liabilities;

simulate the effect of extreme market events on said historical performance data elements using Monte Carlo risk simulations to calculate the Value at Risk (VaR) and best-fitting extreme value distributions;

calculate the effects of interest rate fluctuations by modeling interest rate yield curves using said historical performance data elements;

wherein said Operational Risk mode is configured to automatically perform an Operational Risk analysis for a set of inputted historical or current operational risk events selected from a group of operational risk categories consisting of litigation risks, security risks, reputation risks, fraud risks, information technology risks, staffing risks, human resource risks, and development risks, wherein all entered and automatically calculated operational risk events are automatically reported, summarized and displayed by monthly or yearly periods aggregated by risk segments and counts, and wherein said operational risk events are automatically charted and displayed as control charts that show specific risk events as in- or out-of-control,

wherein said Operational Risk analysis comprises;

automatically calculating the probability of occurrence for each of said operational risk events using probability density and cumulative distribution functions;

automatically analyzing said set of operational risk events using control charts to determine whether each of said risk events are in or out of control; and

automatically calculating the expected simulated losses for said set of operational risk events using Monte Carlo risk simulations and probabilistic loss distributions,

wherein said user interface further comprises a Data Prep function within one of said primary modes, which automatically analyzes one or more user-inputted standard financial statements and automatically generates a set of values and parameters corresponding to the required inputs of one or more of said Credit Risk Analysis, Market Risk Analysis, and Asset Liability Risk Analysis modes, and

wherein the user interface automatically creates a repository of historical and forward-looking KRI results comprising Value at Risk (VaR), Loss Given Default (LGD), Exposure at Default (EAD), Economic Capital (EC), and Economic Regulatory Capital (ERC), and automatically plots and displays one or more graphs showing past or forecast changes in KRI over time.