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 20150088783A1
• Filed: 11/19/2014
• Published: 03/26/2015
• Est. Priority Date: 02/11/2009
• Status: Active Grant

• Current Assignee: Johnathan Mun
• Original Assignee: Johnathan Mun
• Inventors: Mun, Johnathan
• Granted Patent: US 10,453,142 B2
• Time in Patent Office: Days
• US Class Current: 705/36.R
• 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 computer-implemented system for qualitative and quantitative modeling and analysis of Asset Liability Management (ALM), as well as Credit Risk, Market Risk, Operational Risk, and Liquidity Risk (CMOL) comprising:

a processor; and

an Asset Liability Management—

Credit Risk, Market Risk, Operational Risk, and Liquidity Risk (ALM-CMOL) analytics module consisting of computer-executable instructions stored in nonvolatile memory,

wherein said processor and said ALM-CMOL analytics module are operably connected and configured to provide a user interface to a user,

wherein said user interface is includes a database of historical assets, liabilities, returns, risks, valuation, foreign exchange rates, and interest rates,

wherein said user interface allows said user to;

organize and manage one or more historical data elements;

receive one or more historical performance inputs from said user,

wherein said one or more historical performance inputs are comprised of one or more input types selected from the group consisting of balance sheets, assets, liabilities, foreign exchange instruments, interest-sensitive investment instruments, historical stock prices and market returns on investment vehicles,

wherein data elements entered by said user into said user interface for each input type are selected from a group consisting of assets, liabilities, and currencies that are interest rate-sensitive historical performance data;

analyze said historical performance input,

wherein said ALM-CMOL analytics module performs a risk-based performance management and analysis is performed on each of said one or more historical performance data elements;

create historical performance and risk-based historical analysis charts,

wherein one or more graphs are generated based on said risk-based historical performance management and analysis of each of said one or more historical performance data elements;

analyze historical- and risk-level trends of said one or more historical performance inputs,

wherein patterns of change in historical and risk levels for said one or more historical performance inputs can be plotted over time;

forecast changes in said historical and risk levels of said one or more historical performance data elements,

wherein said historical- and risk-level trends are evaluated to provide a predictive analysis of future, historical- and risk-level change of said one or more historical performance data elements via stress testing, scenario analysis, historical simulation, and analytical Monte Carlo risk simulation;

compute the required Economic Regulatory Capital (ERC) as prescribed by the three Basel Accords in accordance with the different credit types (credit issues such as loans, credit lines, and debt at the commercial, retail, or personal levels);

compute one or more risk-based results and Key Risk Indicators (KRI) such as Value at Risk (VaR), Loss Given Default (LGD), Exposure at Default (EAD), Economic Capital (EC), and Economic Regulatory Capital (ERC) based on said historical performance and on stress-tested, scenario-driven, and simulated future state of events

provide said one or more risk-based results to said user,

recommend one or more of said one or more risk-based results to said user; and

create a repository of historical and forward-looking KRI metrics and results.

2. The system of claim 1, further comprising a communications means operably connected to said processor and said ALM-CMOL analytics module.

3. The system of claim 1, wherein the one or more historical data elements include one or more historical asset and liability performance data elements, wherein said one or more historical asset and liability performance data elements can be segmented and managed according to groupings consisting of:

(i) by risk type and (ii) by time period.

4. The system of claim 3, wherein one or more of said one or more historical data elements can be entered as balance sheets and income statements.

5. The system of claim 4, wherein said ALM-CMOL analytics module can segment and compute from said one or more historical data elements required inputs for one or more credit risk model, market risk model, liquidity risk model, and interest rate risk model.

6. The system of claim 1, wherein the one or more graphs are selected from the group of graphs consisting of bar graphs, heat map matrixes, Pareto charts, scenario tables, tornado charts, control charts, time-series charts, risk-simulated probability histogram charts, and pie charts.

7. The system of claim 6, wherein each of said heat map matrixes is a Key Risk Indicator (KRI) heat map that is coded to detail a plurality of historical performance levels, as well as plotted in time-series charts to show said KRI levels over time.

8. The system of claim 7, wherein said KRI heat map is organized by risk types and periodicity based on said plurality of historical performance levels as well as on stress-tested, scenario-based, simulated future forecast levels.

9. The system of claim 1, wherein the ALM-CMOL analytics module and said processor are further configured to perform historical performance mapping to identify how each of said one or more historical performance data elements affects each risk type.

10. The system of claim 1, wherein the ALM-CMOL analytics module and the processor are further configured to perform Monte Carlo risk simulations using historical performance data, historical simulation, bootstrapping of historical simulations, future forecasts, management assumptions, scenario analyses, and stress testing under various conditions to determine economic and regulatory capital requirements under the Basel I, II, and III accords, and investment assets are put into a portfolio where investment allocation weights can be optimized to generate the highest returns subject to the least amount of risk while minimizing the Value at Risk (VaR) of the portfolio.

11. The system of claim 10, wherein extreme value theory is applied with the Monte Carlo risk simulations to obtain empirical VaR results based on simulating the occurrence of extreme events in the market, where assumptions of said extreme and catastrophic events can be entered or historical market data of such events can be used to fit to the appropriate extreme event probability distributions.

12. The system of claim 11, wherein said ALM-CMOL analytics module may further be configured to model fluctuations in interest rates by modeling interest rate yield curves, including simple basis point or percentage increases and decreases, as well as complex twists and turns of the yield curve'"'"'s shapes.

13. The system of claim 12, wherein said ALM-CMOL analytics module may further be configured to model the effects of said fluctuations.

14. The system of claim 1, wherein the ALM-CMOL analytics module and the processor are further configured to:

capture information related to operational risks selected from the group consisting of litigation risks, security risks, reputation risks, fraud risks, information technology risks, staffing risks, human resources risks, development risks;

generate one or more models for said operational risks; and

report said one or more models for said operational risks to said user.

15. The system of claim 14, wherein the ALM-CMOL analytics module and the processor are further configured to:

sort the one or more models for said operational risks then sorted as time-dependent, period-specific, risk-type and risk-segment specific;

chart said one or more models for said operational risks as control charts to identify specific risk events; and

predict probability of occurrence of said operational risks using one or more probability distributions'"'"' probability density functions and cumulative distribution functions.

16. The system of claim 15, wherein the ALM-CMOL analytics module and the processor are further configured to fit historical operational risk loss data to probability distributions specific to severity or financial impact, and frequency or likelihood of occurrence.

17. The system of claim 16, wherein the ALM-CMOL analytics module and the processor are further configured to generate probabilistic loss distributions to determine the expected simulated losses based on Value at Risk percentiles using distributional fitting routines as well as force-fitting to extreme value distributions to examine the extreme tails of risk event distributions.

18. The system of claim 1, wherein the ALM-CMOL analytics module is a network-based module for receiving inputs by end users remote from the ALM-CMOL analytics module.

19. The system of claim 1, wherein the ALM-CMOL analytics module is configured to provide local administrative use.

20. A computer-implemented method for qualitative and quantitative modeling and analysis of Asset Liability Management (ALM), as well as Credit Risk, Market Risk, Operational Risk, and Liquidity Risk (CMOL), said method comprising the steps of:

organizing and manage one or more historical data elements;

receiving one or more historical performance inputs from said user,

wherein said one or more historical performance inputs are comprised of one or more input types selected from the group consisting of balance sheets, assets, liabilities, foreign exchange instruments, interest-sensitive investment instruments, historical stock prices and market returns on investment vehicles,

wherein data elements entered by said user into said user interface for each input type are selected from a group consisting of assets, liabilities, and currencies that are interest rate-sensitive historical performance data;

analyzing said historical performance input,

wherein said ALM-CMOL analytics module performs a risk-based performance management and analysis is performed on each of said one or more historical performance data elements;

creating historical performance and risk-based historical analysis charts,

wherein one or more graphs are generated based on said risk-based historical performance management and analysis of each of said one or more historical performance data elements;

analyzing historical- and risk-level trends of said one or more historical performance inputs,

wherein patterns of change in historical and risk levels for said one or more historical performance inputs can be plotted over time;

forecasting changes in said historical and risk levels of said one or more historical performance data elements,

wherein said historical- and risk-level trends are evaluated to provide a predictive analysis of future, historical- and risk-level change of said one or more historical performance data elements via stress testing, scenario analysis, historical simulation, and analytical Monte Carlo risk simulation;

computing the required Economic Regulatory Capital (ERC) as prescribed by the three Basel Accords in accordance with the different credit types (credit issues such as loans, credit lines, and debt at the commercial, retail, or personal levels);

computing one or more risk-based results and Key Risk Indicators (KRI) such as Value at Risk (VaR), Loss Given Default (LGD), Exposure at Default (EAD), Economic Capital (EC), and Economic Regulatory Capital (ERC) based on said historical performance and on stress-tested, scenario-driven, and simulated future state of events

providing said one or more risk-based results to said user,

recommending one or more of said one or more risk-based results to said user; and

creating a repository of historical and forward-looking KRI metrics and results.

21. The method of claim 20, wherein the one or more historical data elements include one or more historical asset and liability performance data elements, wherein said one or more historical asset and liability performance data elements can be segmented and managed according to groupings consisting of:

(i) by risk type and (ii) by time period.

22. The method of claim 21, wherein one or more of said one or more historical data elements can be entered as balance sheets and income statements.

23. The method of claim 22, further comprising the steps of:

segmenting one or more historical data elements into segmented historical data elements; and

computing required inputs for one or more credit risk model, market risk model, liquidity risk model, and interest rate risk model from said segmented historical data elements.

24. The method of claim 20, wherein the one or more graphs are selected from the group of graphs consisting of bar graphs, heat map matrixes, Pareto charts, scenario tables, tornado charts, control charts, time-series charts, risk-simulated probability histogram charts, and pie charts.

25. The method of claim 24, wherein each of said heat map matrixes is a Key Risk Indicator (KRI) heat map that is coded to detail a plurality of historical performance levels, as well as plotted in time-series charts to show said KRI levels over time.

26. The method of claim 25, wherein said KRI heat map is organized by risk types and periodicity based on said plurality of historical performance levels as well as on stress-tested, scenario-based, simulated future forecast levels.

27. The method of claim 20, further comprising the step of performing historical performance mapping to identify how each of said one or more historical performance data elements affects each risk type.

28. The method of claim 20, further comprising the steps of:

performing Monte Carlo risk simulations using historical performance data, historical simulation;

bootstrapping historical simulations, future forecasts, management assumptions, scenario analyses;

stress-testing conditions to determine economic and regulatory capital requirements under the Basel I, II, and III accords; and

placing investment assets into a portfolio where investment allocation weights can be optimized to generate the highest returns subject to the least amount of risk while minimizing the Value at Risk (VaR) of the portfolio.

29. The method of claim 28, further comprising the step of:

applying extreme value theory with the Monte Carlo risk simulations to obtain empirical VaR results based on simulating the occurrence of extreme events in the market, wherein assumptions of said extreme and catastrophic events can be entered or historical market data of such events can be used to fit to the appropriate extreme event probability distributions.

30. The method of claim 29, further comprising the step of modelling fluctuations in interest rates based on interest rate yield curves, including simple basis point or percentage increases and decreases, as well as complex twists and turns of the yield curve'"'"'s shapes.

31. The method of claim 20, further comprising the steps of:

capturing information related to operational risks selected from the group consisting of litigation risks, security risks, reputation risks, fraud risks, information technology risks, staffing risks, human resources risks, development risks;

generating one or more models for said operational risks; and

reporting said one or more models for said operational risks to said user.

32. The method of claim 31, further comprising the steps of:

sorting the one or more models for said operational risks then sorted as time-dependent, period-specific, risk-type and risk-segment specific;

charting said one or more models for said operational risks as control charts to identify specific risk events; and

predicting probability of occurrence of said operational risks using one or more probability distributions'"'"' probability density functions and cumulative distribution functions.

33. The method of claim 32, further comprising the step of fitting historical operational risk loss data to probability distributions specific to severity or financial impact, and frequency or likelihood of occurrence.

34. The method of claim 33, further comprising the step of generating probabilistic loss distributions to determine the expected simulated losses based on Value at Risk percentiles using distributional fitting routines as well as force-fitting to extreme value distributions to examine the extreme tails of risk event distributions.