Radu Neagu

US Patent:

8249981, Aug 21, 2012

Filed:

Dec 16, 2008

Appl. No.:

12/336360

Inventors:

Sean Coleman Keenan - Norwalk CT, US
Vishwanath Avasarala - Schenectady NY, US
Jason Wayne Black - Clifton Park NY, US
Kete Chalermkraivuth - Niskayuna NY, US
John Andrew Ellis - Niskayuna NY, US
Radu Neagu - Niskayuna NY, US
Rajesh Venkat Subbu - Clifton Park NY, US
Jingjiao Zhang - Seattle WA, US
David Chienju Li - Forest Hills NY, US

Assignee:

GE Corporate Financial Services, Inc. - Norwalk CT

International Classification:

G06Q 40/00

US Classification:

705 38, 705 36 R, 705 35

Abstract:

A method for generating an optimized transition probability matrix (OTPM) is provided. The method is performed using a computer system coupled to a database. The method includes storing in the database financial data including obligor credit ratings, generating multi-period empirical transition probability matrices (ETPMs) for a selected time horizon using the financial data stored within the database, generating a mathematical expression to minimize a difference between target ETPM values and candidate OTPM values, and calculating the OTPM from the generated mathematical expression and the financial data stored within the database, wherein the calculated OTPM includes a first set of optimized transition probability values for predicting a likelihood that a credit rating of an obligor will migrate from one credit state to another credit state during a first time interval in the future.

US Patent:

20030034995, Feb 20, 2003

Filed:

Jul 3, 2001

Appl. No.:

09/897556

Inventors:

Brock Osborn - Niskayuna NY, US
Radu Neagu - Albany NY, US
Alissa Krupar - Erie PA, US
John Hershey - Ballston Lake NY, US
Kati Illouz - Schenectady NY, US
Carl Hansen - Latham NY, US
Melvin Edgar - Cincinnati OH, US
James Dockendorff - Cincinnati OH, US
Donald Gardner - West Chester OH, US

International Classification:

G09G005/00

US Classification:

345/713000

Abstract:

An interactive graphics-based analysis tool for performing reliability analysis of a system formed from a variety of subsystems and components within each subsystem. The tool uses a hierarchical representation component to organize the system, subsystems and components into a hierarchical representation. An interactive selection component provides different options for analyzing the hierarchical representation. A reliability analysis component, responsive to the hierarchical representation component and the interactive selection component, allows a user to perform a reliability analysis at any level of the hierarchical representation.

US Patent:

20030229556, Dec 11, 2003

Filed:

Jun 5, 2002

Appl. No.:

10/162217

Inventors:

Radu Neagu - Schenectady NY, US
Kannan Ramanathan - Niskayuna NY, US
Roger Hoerl - Niskayuna NY, US
Jason Weisman - Stamford CT, US
Chandrasekhar Pisupati - Schenectady NY, US
Sung-Ho Ahn - Clifton Park NY, US
Michael Shaw - Roslyn Heights NY, US

International Classification:

G06F017/60

US Classification:

705/036000

Abstract:

The invention provides for processing of data to determine the likelihood of default of an entity. The processing may comprise obtaining a data set relating to an entity, the data set including at least a first likelihood of default indicator (LDI) value and a second LDI value; determining a LDI rate of change, based on the first LDI value and the second LDI value, to provide a LDI slope value; and determining the likelihood of default of the entity based on the LDI slope value and the first LDI value.

US Patent:

20060031150, Feb 9, 2006

Filed:

Dec 27, 2004

Appl. No.:

11/022402

Inventors:

Deniz Senturk - Niskayuna NY, US
Murat Doganaksoy - Niskayuna NY, US
Christina LaComb - Schenectady NY, US
Bethany Hoogs - Niskayuna NY, US
Radu Neagu - Schenectady NY, US

International Classification:

G06Q 40/00

US Classification:

705035000

Abstract:

A technique for detecting anomalous values in a small set of financial metrics makes use of context data that is determined based upon the characteristics of the target company being evaluated. Context data is selected to represent the historical values of the financial metric for the target company or the simultaneous performance of peer companies. Using the context data, an anomaly score for the financial metric is calculated representing the degree to which the value of the financial metric is an outlier among the context data. This can be done using an exceptional statistical technique. The anomaly score can be used to evaluate the risks associated with business transactions related to the target company.

US Patent:

20060059063, Mar 16, 2006

Filed:

Jan 5, 2005

Appl. No.:

11/028685

Inventors:

Christina LaComb - Schenectady NY, US
Bethany Hoogs - Niskayuna NY, US
Jason Miele - Falls Church VA, US
Deniz Doganaksoy - Niskayuna NY, US
Radu Neagu - Schenectady NY, US
Corey Bufi - Troy NY, US
Abha Moitra - Scotia NY, US
Andrew Deitsch - Clifton Park NY, US
Richard Arthur - Ballston Spa NY, US

International Classification:

G06Q 40/00

US Classification:

705035000

Abstract:

A visualization technique for directing the attention of analysts to anomalous values of performance measures associated with a target entity is described. A grid of cells is created where each row represents a particular performance metric, and each column a particular time period. For each cell, an anomaly score is calculated associated with the performance metric and time period corresponding to the row and column of the cell. The anomaly score is based on the value of the performance metric for that particular entity for that time period, as well as context data. The context data is selected to represent the historical values of the performance metric for the target entity or the simultaneous performance of peer entities. The anomaly score is calculated using an exceptional statistical technique, and a display characteristic is associated with the value of the anomaly score based upon the range into which the anomaly score falls. The display characteristic is displayed within the cell on the grid, forming an anomaly map that allows identification of patterns among the performance metrics.