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.

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.

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.

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.

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.

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.