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Constructing Predictive Default Models Based on Lattices of Closed Interval Patterns for Borrower's Stats

Student: Anastasiya Demidova

Supervisor: Sergei Kuznetsov

Faculty: Faculty of Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Final Grade: 7

Year of Graduation: 2017

In the past few years, due to the need to process large amounts of data in banks, there is a growing demand for more efficient methods of credit scoring that would give interpretable results. In this regard, we propose a new method of lazy classification, based on lattices of closed interval pattern structures. The main idea of the method is to use methods of formal concepts analysis for constructing interval pattern structures for good and bad groups of borrowers.After that, based on these descriptions, we can build a classification rule for each test object, which determines to which group of borrowers refers a new candidate for a loan. As a result, the quality of the proposed method is compared with the main methods of credit scoring. Keywords: machine learning, classification methods, credit scoring, formal concept analysis, pattern structures.

Full text (added May 29, 2017)

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