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Hashing for Financial Credit Risk Analysis

  • Bernardete Ribeiro
  • Ning Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8835)

Abstract

Hashing techniques have recently become the trend for accessing complex content over large data sets. With the overwhelming financial data produced today, binary embeddings are efficient tools of indexing big datasets for financial credit risk analysis. The rationale is to find a good hash function such that similar data points in Euclidean space preserve their similarities in the Hamming space for fast data retrieval. In this paper, first we use a semi-supervised hashing method to take into account the pairwise supervised information for constructing the weight adjacency graph matrix needed to learn the binarised Laplacian EigenMap. Second, we train a generalised regression neural network (GRNN) to learn the k-bits hash code. Third, the k-bits code for the test data is efficiently found in the recall phase. The results of hashing financial data show the applicability and advantages of the approach to credit risk assessment.

Keywords

hashing method financial credit risk generalised regression neural network k-bits hash code 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bernardete Ribeiro
    • 1
  • Ning Chen
    • 2
  1. 1.CISUC - Department of Informatics EngineeringUniversity of CoimbraPortugal
  2. 2.GECADInstituto Superior de Engenharia do PortoPortugal

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