Patent ID: 8799150

Claim:
A method of determining an individual's unemployment risk based credit score, comprising: generating by a computer, an unemployment risk probability for the individual using the individual's personal data including age, education, demographic data and employment history, and using historical employment, unemployment, and economic data; generating by the computer, an income loss risk for the individual using said unemployment risk probability; generating by the computer, an income reduction risk for the individual; generating the individual's unemployment risk based credit score based on the unemployment risk probability, income loss risk, income reduction risk, personal data, and national employment data, national unemployment data, and national economic data, wherein said individual's income risk based credit score provides an indication of a probability of the individual defaulting on one or more of the individual's payment obligations; and wherein the step of using the unemployment risk probability, income loss risk, income reduction risk, personal data, national employment data, and national economic data to generate the individual's unemployment risk based credit score further comprises the steps of: segmenting a national workforce population into homogenous risk categories, with each risk category comprising a plurality of homogenous sub risk subcategories; segmenting dependent, unemployed and non-working individuals into risk categories and sub categories; assigning a risk factor weight to each of the risk categories and sub risk subcategories; predicting an unemployment rate for a finite time frame, for each risk category and subcategory; predicting an income loss probability for a finite time frame, for each risk category and subcategory; transforming the said unemployment rate predictions and income loss risk predictions, or any mathematical combinations of these, into a mathematical score on a scale of zero to one thousand or any other similar scale, which may be developed using linear or non-linear mathematical equations; transforming the said unemployment rate score and income loss risk score into an unemployment risk based credit score by correlating them with individuals' ability to pay and credit data; predicting an ability to pay risk for a finite time frame, for each risk category and sub category and converting it into an ability to pay score; predicting credit default risk for a time frame, for each risk category and sub category and converting it into an unemployment risk based credit score; and providing a quantitative and qualitative explanation and narrative of the contributing risk factors, relative ranking of said unemployment risk based credit scores by comparing it with other scores and score groups including, but not limited to, national and regional risk scores, industry and sub-industry scores, education scores, and scores grouped based on economic, credit and payment behavior, and demographic similarities and other common attributes.