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 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. However, data retrieval, collection, preprocessing and feature selection plays an important role in this field; thus implementation of these techniques also becomes important. This review focuses on available patents from credit risk domain which deal mainly with intelligent techniques, with both systematic and implementation (engineering) aspects from intelligent learning, summarizes and identifies main trends.
Credit risk, financial risk, loan processing, machine learning, patents, intelligent systems, decision support, evaluation, artificial intelligence
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