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Machine Learning Techniques and Risk Management: Application to the Banking Sector During Crisis
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Author(s): Christos Floros (Hellenic Mediterranean University, Greece)and Panagiotis Ballas (Hellenic Mediterranean University, Greece)
Copyright: 2021
Pages: 16
Source title:
Machine Learning Applications for Accounting Disclosure and Fraud Detection
Source Author(s)/Editor(s): Stylianos Papadakis (Hellenic Mediterranean University, Greece), Alexandros Garefalakis (Hellenic Mediterranean University, Greece), Christos Lemonakis (Hellenic Mediterranean University, Greece), Christiana Chimonaki (University οf Portsmouth, UK)and Constantin Zopounidis (School of Production Engineering and Management, Technical University of Crete, Greece & Audencia Business School, France)
DOI: 10.4018/978-1-7998-4805-9.ch013
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Abstract
Crises around the world reveal a generally unstable environment in the last decades within which banks and financial institutions operate. Risk is an inherent characteristic of financial institutions and is a multifaceted phenomenon. Everyday business practice involves decisions, which requires the use of information regarding various types of threats involved together with an evaluation of their impact on future performance, concluding to combinations of types of risks and projected returns for decision makers to choose from. Moreover, financial institutions process a massive amount of data, collected either internally or externally, in an effort to continuously analyse trends of the economy they operate in and decode global economic conditions. Even though research has been performed in the field of accounting and finance, the authors explore the application of machine learning techniques to facilitate decision making by top management of contemporary financial institutions improving the quality of their accounting disclosure.
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