Algorithmic Decision Making Methods for Fair Credit Scoring

Credit Scoring Methods

Credit Scoring using Semiparametric Methods

Credit scoring methods aim to assess credit worthiness of potential borrowers to keep the risk of credit loss low and to minimize the costs of failure over risk groups. Standard parametric approaches as logistic discrimination analysis assume that the probability of belonging to the group of ”bad” clients is given by P (Y = 1|X) = F (βX), with Y = 1 indicating a ”bad” client and X denoting the ...

Trade Credit Decision Making

The basic financial purpose of an enterprise is maximization of its value. Trade credit management should also contribute to realization of this fundamental aim. Many of the current asset management models that are found in financial management literature assume book profit maximization as the basic financial purpose. These book profitbased models could be lacking in what relates to another aim...

Using DEA for Classification in Credit Scoring

Credit scoring is a kind of binary classification problem that contains important information for manager to make a decision in particularly in banking authorities. Obtained scores provide a practical credit decision for a loan officer to classify clients to reject or accept for payment loan. For this sake, in this paper a data envelopment analysis- discriminant analysis (DEA-DA) approach is us...

Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning

With widespread use of machine learning methods in numerous domains involving humans, several studies have raised questions about the potential for unfairness towards certain individuals or groups. A number of recent works have proposed methods to measure and eliminate unfairness from machine learning models. However, most of this work has focused on only one dimension of fair decision making: ...