Intelligent Techniques and Systems in Credit Risk Analysis and Forecasting: A Review of Patents
Paulius Danenas and Gintautas GarsvaAffiliation:
Center of Information Systems Design Technologies, Kaunas University of Technology, Lithuania.
AbstractCredit risk evaluation and bankruptcy analysis is essential for various financial institutions which must minimize their possible loss, as well as banking sector, investors, governing authorities, as it helps to identify possible financial problems or even predict future financial crises. Various artificial intelligence, soft computing and machine learning techniques often prove to overcome limitations of previously applied techniques or tend to show competitive results in terms of accuracy or precision. These techniques are widely developed, researched and applied to solve problems in credit risk domain. Data retrieval, collection, preprocessing and feature selection play an important role in this field; thus proper implementation of these techniques is adequately important. This review is focused on available patents from credit risk domain which involve intelligent techniques, with both systematic and implementation (engineering) aspects, as well as identification of future trends in this field.
Artificial intelligence, credit risk, decision support, evaluation, financial risk, intelligent systems, loan processing, machine learning, patents.
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