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Bankruptcy Prediction for Banks: An Artificial Intelligence Approach to Improve Understandability

  • Alma Lilia Garcia-Almanza
  • Biliana Alexandrova-Kabadjova
  • Serafin Martinez-Jaramillo
Part of the Studies in Computational Intelligence book series (SCI, volume 427)

Abstract

Artificial Intelligence (AI) is a prominent field within Computer Science whose main goal is automatic problem solving. Some of the foundations of this area were established by Alan M. Turing in his two seminal papers about machine intelligence [39] and [40]. Machine Learning (ML) is an important branch within the AI field which currently is on an intensive stage of development due to its wide range of applications. In particular, ML techniques have recently gained recognition in finance, since they are capable to produce useful models. However, the difficulty, and even the impossibility, to interpret these models, has limited the use of ML techniques in some problems where the interpretability is an important issue. Bankruptcy prediction for banks is a task which demands understandability of the solution. Furthermore, the analysis of the features (input variables), to create prediction models, provides better knowledge about the conditions which may trigger bank defaults. The selection of meaningful features before executing the learning process is beneficial since it reduces the dimensionality of the data by decreasing the size of the hypothesis space. As a result, a compact representation is obtained which is easier to interpret. The main contributions of this work are: first, the use of the evolutionary technique called Multi-Population Evolving Decision Rules MP-EDR to determine the relevance of some features from Federal Deposit Insurance Corporation (FDIC) data to predict bank bankruptcy. The second contribution is the representation of the features’ relevance by means of a network which has been built by using the rules and conditions produced by MP-EDR. Such representation is useful to disentangle the relationships between features in the model, this representation is aided by metrics which are used to measure the relevance of such features.

Keywords

Feature Selection Decision Rule Genetic Programming Bank Failure Intelligence Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Alma Lilia Garcia-Almanza
    • 1
  • Biliana Alexandrova-Kabadjova
    • 1
  • Serafin Martinez-Jaramillo
    • 1
  1. 1.Banco de MxicoMexico CityMexico

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