Credit analysis determines a numerical score that represents an amount of credit-worthiness (or credit risk) associated with an individual or a group. Businesses and financial institutions, among others, use this credit score to determine whether credit should be offered or granted, and at what terms, to the individual or group in question.
A credit score is determined based on several types of information, which is collectively called “credit data.” Credit data can include, for example, personal information (such as a value of a major asset), credit information (such as account balance), public record information (such as bankruptcy), and inquiry information (such as a request for a credit report). Each piece of credit data has a value, and this value can affect the credit score.
Since credit scores are important, it makes sense that a person would want to know how taking a particular action (such as increasing or decreasing an account balance) could affect her credit score. Unfortunately, it is very difficult to determine this by merely analyzing a set of credit data and its resulting credit score. Credit data is fed into an algorithm (called a risk model or “scorecard”), which analyzes the data and determines a credit score. Scorecards are generally kept secret, and it is nearly impossible to reverse-engineer them because they are so complex.
What is needed are a method and a system that can generate a first credit score, enable the credit data to be modified, and generate a second credit score. This will demonstrate how changes in credit data affect the credit score.