Credit scoring for billions of financing decisions

When you apply for credit, whether it's for a new credit card, a car, a student loan, a mortgage, or financing, about forty pieces of information from your credit card report are fed into a statistical model. That model provides a numerical score designed to predict your risk as a borrower. In this presentation, we will introduce fuzzy query and ranking as an alternative to predict the risk in an ever-changing world and an imprecise environment which includes subjective considerations for credit scoring. Fuzzy query and ranking is robust, provides better insight and a bigger picture, contains more intelligence about an underlying pattern in data and provides the ability of flexible querying and intelligent searching. This greater insight makes it easy for users to evaluate the results related to the stated criterion and make a decision faster with improved confidence. Fuzzy query is very useful for multiple criteria and when users want to vary each criterion independently with different degrees of confidence or weighting factor.