Ge Capital Commercial Finance, Inc.

Ge Corporate Financial Services, Inc.

General Electric Company

Ge Corporate Finanical Services, Inc.

Ge Capital Commerical Finance, Inc.

Tim Kerry Keyes

Christopher Donald Johnson

Marc Thomas Edgar

Richard Paul Messmer

William Cree Steward

Chandrasekhar Pisupati

Catharine Lynn Midkiff

Yu-to Chen

David Jonathan Spencer

Kunter Seref Akbay

Vrinda Rajiv

Brian Nathan Dingman

James Louis Cifarelli

Charles N Allred

Navneet Kapoor

Sean Coleman Keenan

David Richard Nelson

Henry Fred Ander

John Deaver Deets

Mary Kennedy Cole

Santosh Kumar Srinivas

Donna Raye Shorten

Sundeep Ruia

Marshall Gaines Burchard

Murat Doganaksoy

A method for assessing underwriting and distribution risks associated with a portfolio of subordinate debt is provided. The method is performed using a computer system coupled to a database. The method includes storing in the database historical bond issue data for a period of time preceding and proceeding at least one historical liquidity event and generating a plurality of simulated subordinate debt warehouses using the computer and the historical bond issue data stored in the database. The method also includes calculating a historical loss distribution based on the plurality of simulated subordinate debt warehouses generated. The method also includes determining a value at risk for a portfolio of subordinate debt resulting from a potential liquidity event by applying the historical loss distribution to the portfolio of subordinate debt.

Methods and systems for assessing underwriting and distribution risks associated with subordinate debt

A method for predicting expected and unexpected loss outcomes for a portfolio of loans is provided. The loans are issued by a lender to a plurality of borrowers. The method includes recording key account and risk attributes for a historical portfolio of loans, recording actual default and loss information for each borrower included within the historical portfolio of loans, and comparing the key account and risk attributes with the actual default and loss information over a period of time. The method also includes selecting a sample of loans from the historical portfolio of loans to determine loss drivers based on the comparison of the key account and risk attributes with the actual default and loss information, building a regression tree based model representing relationships between the loss drivers, and expected and unexpected loss outcomes for the historical portfolio of loans, and predicting the expected and unexpected loss outcomes for a second portfolio of loans using the regression tree based model and the loss drivers for the second portfolio of loans.

Methods and system for assessing loss severity for commercial loans

A method for valuing portfolio assets using a snapshot approach system is provided. The method includes segmenting portfolio assets into a predetermined number of segments based on financial attributes of each asset, selecting a representative sample of assets from each segment, valuing each asset in the representative asset sample, and calculating a value of the portfolio assets for bidding purposes based on the value of each asset in the representative asset sample.

Snapshot approach for underwriting valuation of asset portfolios

Methods and systems for assessing a loan portfolio for variance are described. In an example embodiment, the method comprises the steps of identifying a milestone for at least one loan in the portfolio at a selected time of assessment, determining planned collections for the loan for the selected time of assessment, determining actual collections for the loan the selected time period of assessment, and populating a spreadsheet identifying a current milestone and a cumulative variance between planned collections and actual collections at the milestone for the loan.

Methods and systems for assessing loan portfolios

In an exemplary embodiment, invention is a method for analyzing portfolios of distressed financial assets for the purpose of bidding to acquire those assets. The method utilizes a network-based system including a server system coupled to a centralized database and at least one client system. The method comprises of various steps from generating cash flow data table from variety of data sources to performing sensitivity analysis using Monte Carlo Simulation Model to provide different scenarios. The method utilizes a variety of assumptions in performing analysis, and exports cash flow projections into a pre-determined format to develop financially attractive bids, which have strong probability of, expected return on investment after taking into account a variety of foreseeable risks.

Methods and systems for portfolio cash flow valuation

A method of valuation of large groups of assets by partial full underwriting, partial sample underwriting and inferred values of the remainder using an iterative and adaptive supervised and unsupervised statistical evaluation of all assets and statistical inferences drawn from the evaluation and applied to generate the inferred values. Individual asset values are developed and listed in tables so that individual asset values can be rapidly taken from the tables and quickly grouped in any desired or prescribed manner for bidding purposes. The assets are collected into a database, divided into categories by credit variable, subdivided by ratings as to those variables and then rated individually. The assets are then regrouped according to a bidding grouping and a collective valuations established by cumulating the individual valuations.

Valuation prediction models in situations with missing inputs

A method of valuation of large groups of assets by selecting a lowest variation assumption for asset valuation in order to limit risk includes the steps of evaluating assets by all known valuation methodologies which have merit for those particular assets, selecting the valuation methodology which is most accurate for the assets being evaluated and assigning values to the assets. Individual asset values are developed and listed in tables so that individual asset values can be taken from the tables and grouped in any desired manner. The asset s are divided into categories by credit variable and then rated individually.

Methods and systems for quantifying cash flow recovery and risk

A method of valuation of large groups of assets using classification and regression trees is described. The method includes defining relevant portfolio segmentations, assessing performance of the classification and regression tree based model against a simple model and ranking all portfolio segments based upon performance of the models. Iterative and adaptive statistical evaluation of all assets and statistical inferences are used to generate the segmentations. The assets are collected into a database, grouped by credit variable, subdivided by ratings as to those variables and then rated individually. The assets are then regrouped and a collective valuation is established by cumulating individual valuations.

Methods and systems for modeling using classification and regression trees

A method of valuation of large groups of assets by partial full underwriting, partial sample underwriting and inferred values of the remainder using an iterative and adaptive statistical evaluation of all assets and statistical inferences drawn from the evaluation and applied to generate inferred values. Individual asset values are developed and listed in tables so that individual asset values can be taken and quickly grouped in any desired or prescribed manner for bidding purposes. The assets are collected into a database, divided by credit variable, subdivided by ratings as to those variables and then rated individually. The assets are then regrouped according to a bidding grouping and a collective valuation established by cumulating the individual valuations.

Methods and systems for optimizing return and present value

A method of valuation of large groups of assets by partial full underwriting, partial sample underwriting and inferred values of the remainder using an iterative and adaptive supervised and unsupervised statistical evaluation of all assets and statistical inferences drawn from the evaluation and applied to generate the inferred asset values. Individual asset values are developed and listed in realtional tables so that individual asset values can be rapidly taken from the tables and quickly grouped in any desired or prescribed manner for bidding purposes. The assets are collected into a database, divided into categories by credit variable, subdivided by ratings as to those variables and then rated individually. The assets are then regrouped according to a bidding grouping and a collective valuations established by cumulating the individual valuations.

Rapid valuation of portfolios of assets such as financial instruments

A method of inferring valuation of large groups of assets by credit scores includes the steps of organizing valuation scores, adjusting valuation scores based on special factors and business decisions, reconciling multiple valuation scores which describe the same assets and making an overall adjustment to override the inferred valuation. Individual asset values are developed and listed in tables so that individual asset values can be rapidly taken from the tables and quickly grouped for bidding purposes. The valuations are collected into a database, divided into categories by credit variable, subdivided by ratings as to those variables and then rated individually.

Methods and systems for automated inferred valuation of credit scoring

A method of valuation of large groups of assets by a partial full underwriting, partial sample underwriting and inferred valuation of the remainder using an iterative and adaptive statistical evaluation of all assets and statistical inferences drawn from the evaluation and applied to generate inferred asset values. Individual asset values are developed and listed so that individual asset values can be rapidly taken and quickly grouped in any manner for bidding purposes. The assets are collected into a database, divided into categories, subdivided by ratings and then rated individually. Asset value is continuously recalculated based on progressively improving asset valuation data. The assets are then regrouped for bidding and a collective valuation is established by cumulating individual valuations.

Methods and systems for finding value and reducing risk

A method of valuation of large groups of assets by partial full underwriting, partial sample underwriting and inferred values of the remainder using an iterative and adaptive supervised and unsupervised statistical evaluation of all assets and statistical inferences drawn from the evaluation and applied to generate the inferred asset values. Individual asset values are developed and listed in relational tables so that individual asset values can be rapidly taken from the tables and quickly grouped in any desired or prescribed manner for bidding purposes. The assets are collected into a database, divided into categories by credit variable, subdivided by ratings as to those variables and then rated individually. The assets are then regrouped according to a bidding grouping and a collective valuations established by cumulating the individual valuations.

Methods and systems for efficiently sampling portfolios for optimal underwriting

The present invention is a method of allowing inclusion of more than one variable in a Classification and Regression Tree (CART) analysis. The method includes predicting y using p exploratory variables, where y is a multivariate, continuous response vector, describing a probability density function at 'parent' and 'child' nodes using a multivariate normal distribution, which is a function of y, and defining a split function where &#x201c;child&#x201d; node distributions are individualized, compared to the parent node. In one embodiment a system is configured to implement the multivariate CART analysis for predicting behavior in a non-performing loan portfolio.

Multivariate responses using classification and regression trees systems and methods

A method of valuation of large groups of assets using an iterative and adaptive statistical evaluation to generate asset values is described. Individual asset values are developed and listed so that individual asset values can be rapidly taken and quickly grouped in any desired or prescribed manner for bidding purposes. One method of automated underwriting includes defining clusters of financial instruments by common attributes, receiving an expert opinion of value from selected samples of the clusters, checking values for combinations of attributes and reconciling the values. A collective valuation of the assets is established by cumulating the individual valuations.

Growing community of inventors

West Redding, CT, United States of America

Tim Kerry Keyes