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Title:	*Extension Of Bank Application Scoring Model With Big Data Analysis*
Authors:	Laszlo Madar
Published in:	(2017).ECMS 2017 Proceedings Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics European Council for Modeling and Simulation. doi:10.7148/2017 ISBN: 978-0-9932440-4-9/ ISBN: 978-0-9932440-5-6 (CD) 31st European Conference on Modelling and Simulation, Budapest, Hungary, May 23^rd – May 26^th, 2017
Citation format:	Laszlo Madar (2017).Extension Of Bank Application Scoring Model With Big Data Analysis, ECMS 2017 Proceedings Edited by: Zita Zoltay Paprika, Péter Horák, Kata Váradi, Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics European Council for Modeling and Simulation. doi: 10.7148/2017-0550
DOI:	https://doi.org/10.7148/2017-0550
Abstract:	Application scoring models of credit institutions has been subject to research since the 1960s. Micro and SME lending has also been improving, however current application scoring models for smaller firms have still lower power statistics as models for private individuals or larger firms. This portfolio has not so strong financials and have less bureau collected behavioral information that makes individual assessment of these firms a hard job for credit institutions. This paper presents an actual example on how external unstructured information can be collected, assessed and used in order to increase performance of this portfolio segment.
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