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

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Artificial Intelligence, Evolutionary Computing and Metaheuristics

Part of the book series: Studies in Computational Intelligence ((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.

The views expressed here are those of the authors and do not represent the views of Banco de Mexico

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Authors and Affiliations

  1. Banco de Mxico, Mexico City, Mexico

    Alma Lilia Garcia-Almanza, Biliana Alexandrova-Kabadjova & Serafin Martinez-Jaramillo

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  1. Alma Lilia Garcia-Almanza
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  2. Biliana Alexandrova-Kabadjova
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  3. Serafin Martinez-Jaramillo
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Correspondence to Alma Lilia Garcia-Almanza .

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Editors and Affiliations

  1. , Mathematics and Scientific Computing, National Physical Laboratory, Hampton Road, Teddington, TW11 0LW, United Kingdom

    Xin-She Yang

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Garcia-Almanza, A.L., Alexandrova-Kabadjova, B., Martinez-Jaramillo, S. (2013). Bankruptcy Prediction for Banks: An Artificial Intelligence Approach to Improve Understandability. In: Yang, XS. (eds) Artificial Intelligence, Evolutionary Computing and Metaheuristics. Studies in Computational Intelligence, vol 427. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29694-9_24

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  • DOI: https://doi.org/10.1007/978-3-642-29694-9_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29693-2

  • Online ISBN: 978-3-642-29694-9

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