Risk management system

• US 20080288394A1
• Filed: 08/03/2008
• Published: 11/20/2008
• Est. Priority Date: 10/17/2000
• Status: Abandoned Application

• Current Assignee: Jeffrey Eder
• Original Assignee: Jeffrey Eder
• Inventors: Eder, Jeffrey Scott
• Application Number:
  US12/185,093
• Publication Number:
  US 20080288394A1
• Time in Patent Office: Days
• US Class Current: 705/38
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• 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 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

An automated method and system (100) for risk analysis, management and optimization for a plurality of commercial enterprises.

Claims

1. A computer implemented risk transfer method, comprising:

integrate a plurality of transaction data from a plurality of management systems for a plurality of clients in accordance with a common schema,

analyze said data with a series of models as required to quantity a value impact and a risk for one or more elements of value and one or more market value factors for each of one or more segments of value for each of a plurality of customers,

analyzing said data to identify an optimal set of risk transfer transactions for each customer where the optimal set of risk transfer transactions is the set that minimizes the value impact of retained risks within the constraints on risk transfer imposed by the capital available for risk transfer purchases under a scenario selected from the group consisting of normal, extreme and combinations thereof, and

optionally implement an optimal set of risk transfer transactions for one or more customers

where risk transfer transactions are selected from the group consisting of a swap of an element of value risk, a swap of an external factor risk and combinations thereof for one or more segments of value.

2. The method of claim 1, wherein the elements of value are selected from the group consisting of alliances, brands, channels, customers, customer relationships, employees, equipment, partnerships, processes, securities, supply chains, vendors, vendor relationships and combinations thereof.

3. The method of claim 1, wherein the market value factors are selected from the group consisting of commodity prices, inflation rate, gross domestic product, volatility, interest rates, insider trading, consumer confidence, organization performance against expectations, the unemployment rate and combinations thereof.

4. The method of claim 1, wherein the where risks are selected from the group consisting of event risks, contingent liabilities, variability risks, volatility risks and combinations thereof.

5. The method of claim 4, wherein contingent liabilities are quantified using a real option algorithm.

6. The method of claim 1, wherein analyzing data with a series of models, comprises:

using a designated schema classification for each of one or more data records in each system as an aspect of financial performance data record, an element of value data record or a market value factor data record based on a user input or a system origin;

generating a plurality of indicators from said element of value and external factor data, receiving said data and indicators as a first input data into a plurality of initial predictive models for each aspect of financial performance and developing an initial model configuration by selecting a data set for the element of value and external factor data variables from the plurality of predictive models using a variable selection algorithm after a training of each predictive model type is completed;

testing the first input data set for independence and adjusting the schema classification structure for said data set as required to produce accurate results,

receiving the tested input data set as an input into a second, induction model stage to develop an improvement to said initial model configuration as an output;

receiving said second model stage output as an input into a third predictive model stage to develop and output a final predictive model; and

using the final predictive model to quantify a value impact for each element of value and to simulate a financial performance in order to quantify each of one or more risks

where the aspects of financial performance are revenue, expense, capital change, a derivative segment of value, an excess financial asset segment of value, a market sentiment segment of value and combinations thereof.

7. A computer readable medium having sequences of instructions stored therein, which when executed causes a processor in at least one computer to perform risk transfer method, comprising:

integrate a plurality of transaction data from a plurality of management systems for a plurality of clients in accordance with a common schema,

analyze said data with a series of models as required to quantity a value impact and a risk for one or more elements of value and one or more market value factors for each of one or more segments of value for each of a plurality of customers,

analyzing said data to identify an optimal set of risk transfer transactions for each customer where the optimal set of risk transfer transactions is the set that minimizes the value impact of retained risks within the constraints on risk transfer imposed by the capital available for risk transfer purchases under a scenario selected from the group consisting of normal, extreme and combinations thereof, and

optionally implement an optimal set of risk transfer transactions for one or more customers

where risk transfer transactions are selected from the group consisting of a swap of an element of value risk, a swap of an external factor risk, and combinations thereof for one or more segments of value, and

where the segments of value are a current operation, a real option segment and segments of value selected from the group consisting of derivatives, excess financial assets, market sentiment and combinations thereof.

8. The computer readable medium of claim 7, wherein the elements of value are selected from the group consisting of alliances, brands, channels, customers, customer relationships, employees, equipment, partnerships, processes, securities, supply chains, vendors, vendor relationships and combinations thereof.

9. The computer readable medium of claim 7, wherein the market value factors are selected from the group consisting of commodity prices, inflation rate, gross domestic product, volatility, interest rates, insider trading, consumer confidence, organization performance against expectations, the unemployment rate and combinations thereof.

10. The computer readable medium of claim 7, wherein the risks are selected from the group consisting of event risks, contingent liabilities, variability risks, volatility risks and combinations thereof.

11. The computer readable medium of claim 10, wherein the contingent liabilities are quantified using a real option algorithm.

12. The computer readable medium of claim 7, wherein analyzing data with a series of models, comprises:

using a designated schema classification for each of one or more data records in each system as an aspect of financial performance data record, an element of value data record or a market value factor data record based on a user input or a system origin;

generating a plurality of indicators from said element of value and external factor data, receiving said data and indicators as a first input data into a plurality of initial predictive models for each aspect of financial performance and developing an initial model configuration by selecting a data set for the element of value and external factor data variables from the plurality of predictive models using a variable selection algorithm after a training of each predictive model type is completed;

testing the first input data set for independence and adjusting the schema classification structure for said data set as required to produce accurate results,

receiving the tested input data set as an input into a second, induction model stage to develop an improvement to said initial model configuration as an output;

receiving said second model stage output as an input into a third predictive model stage to develop and output a final predictive model; and

using the final predictive model to quantify a value impact for each element of value and to simulate a financial performance in order to quantify each of one or more risks,

where the aspects of financial performance are revenue, expense, capital change, a derivative segment of value, an excess financial asset segment of value, a market sentiment segment of value and combinations thereof.

13. An enterprise system, comprising a computer with a processor having circuitry to execute instructions;

a storage device available to said processor with sequences of instructions stored therein, which when executed cause the processor to;

integrate a plurality of transaction data from a plurality of management systems for a plurality of clients in accordance with a common schema,

analyze said data with a series of models as required to quantity a value impact and a risk for one or more elements of value and one or more market value factors for each of one or more segments of value for each of a plurality of customers,

analyzing said data to identify an optimal set of risk transfer transactions for each customer where the optimal set of risk transfer transactions is the set that minimizes the value impact of retained risks within the constraints on risk transfer imposed by the capital available for risk transfer purchases under a scenario selected from the group consisting of normal, extreme and combinations thereof, and

optionally implement an optimal set of risk transfer transactions for one or more customers

where risk transfer transactions are selected from the group consisting of a swap of an element of value risk, a swap of an external factor risk and combinations thereof for one or more segments of value, and

where the segments of value are a current operation, a derivative segment, a real option segment and segments of value selected from the group consisting of excess financial assets, market sentiment and combinations thereof.

14. The system of claim 13, wherein the elements of value are selected from the group consisting of alliances, brands, channels, customers, customer relationships, employees, equipment, partnerships, processes, securities, supply chains, vendors, vendor relationships and combinations thereof.

15. The system of claim 13, wherein the market value factors are selected from the group consisting of commodity prices, inflation rate, gross domestic product, volatility, interest rates, insider trading, consumer confidence, organization performance against expectations, the unemployment rate and combinations thereof.

16. The system of claim 13, wherein the where risks are selected from the group consisting of event risks, contingent liabilities, variability risks, volatility risks and combinations thereof.

17. The system of claim 16, wherein the contingent liabilities are quantified using a real option algorithm

18. The system of claim 13, wherein analyzing data with a series of models, comprises:

using a designated schema classification for each of one or more data records in each system as an aspect of financial performance data record, an element of value data record or a market value factor data record based on a user input or a system origin;

generating a plurality of indicators from said element of value and external factor data, receiving said data and indicators as a first input data into a plurality of initial predictive models for each aspect of financial performance and developing an initial model configuration by selecting a data set for the element of value and external factor data variables from the plurality of predictive models using a variable selection algorithm after a training of each predictive model type is completed;

testing the first input data set for independence and adjusting the schema classification structure for said data set as required to produce accurate results,

receiving the tested input data set as an input into a second, induction model stage to develop an improvement to said initial model configuration as an output;

receiving said second model stage output as an input into a third predictive model stage to develop and output a final predictive model; and

using the final predictive model to quantify a value impact for each element of value and to simulate a financial performance in order to quantify each of one or more risks,

where the aspects of financial performance are revenue, expense, capital change, a derivative segment of value, an excess financial asset segment of value, a market sentiment segment of value and combinations thereof.