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In the mortgage lending business of a bank, a key focus area is risk analysis, which supports the mortgage awarding process and the prediction of the risk of defaulting (repayment issues). The standard risk assessment method at most banks is a scorecard calculation. A new way of predicting the defaulting is proposed using Defeasible Reasoning (DR) and computational Argumentation Theory (AT), areas of interdisciplinary research, in the discipline of Articial Intelligence (AI). Argumentation is formalised by reasoning models which are inspired by human reasoning. For a more realistic representation AT employs DR which is a non-monotonic reasoning process, meaning that in case of new evidence a previous conclusion might change. The computational AT approach is predominantly knowledge driven and it includes building and evaluating arguments, their relationships, the resolution of their inconsistencies and the generation of defeasible conclusions, on which the experiment conducted in this thesis is based upon. It is demonstrated how it is possible to reasoning in a defeasible way to predict the risk of defaulting. Results demonstrated that in 75% of the test cases AT predicted better than the scorecard method. The prediction accuracy was compared using a so-called confusion-matrix where the two axes being Predicted (Yes; No) and Actual (Yes;No).
Szucs, H. (2017) Modeling mortgage assessment with computational argumentation theory and defeasible reasoning, Dissertation, Dublin Institute of Technology.