Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey

By Meryem Duygun Fethi, Fotios Pasiouras. 2010

862 citations recorded by the source corpus.

Subfields: Accounting, Finance, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Sailesh Tanna
2. M. Nnadi
3. Nicolas Huck
4. M. Gomes
5. Sanaa Belasri
6. Guillaume Pijourlet
7. Roman Matousek
8. Hirofumi Fukuyama

In Finance

1. Sailesh Tanna
2. Roman Matousek
3. Fotios Pasiouras
4. M. Nnadi
5. Nicolas Huck
6. Guillaume Pijourlet
7. Sanaa Belasri
8. M. Gomes

In Management Science and Operations Research

1. Roman Matousek
2. Sailesh Tanna
3. Fotios Pasiouras
4. Nicolas Huck
5. M. Nnadi
6. Guillaume Pijourlet
7. Sanaa Belasri
8. M. Gomes

Citations in the corpus

• Deep Learning for Financial Applications : A Survey (2020, neutral): “[86] reviewed the bank performance assessment studies based on Artificial Intelligence (AI) and ML models.”
• Pairs trading and outranking: The multi-step-ahead forecasting case (2010, methodology): “Multi-criteria decision methods (Figueira et al., 2005a; Wallenius et al., 2008) are now used in various fields and are becoming increasingly popular in finance as underlined in different surveys (Zopounidis, 1999; Steuer and Na, 2003; Spronk et al., 2005; Xidonas and Psarras, 2009; Duygun Fethi and”
• Monitoring bank performance in the presence of risk (2014, neutral): “Banking efficiency literature identifies two main approaches for evaluating financial institutions (see the surveys of Berger and Humphrey 1997; Goddard et al. 2001; Fethi and Pasiouras 2010).”
• Does corporate governance affect Australian banks' performance? (2016, background): “to the survey of Fethi and Pasiouras (2010), which reviewed 196 bank performance studies, DEA has become a popular tool in this area around the world.”
• Interval data without sign restrictions in DEA (2014, background): “In many recent studies assessing bank performance, DEA is recognised as one of the main tools providing information both on the efficiency and productivity of banks (see, [38,39]).”
• Correlated discrete data generation using adversarial training (2018, background): “Recent advances in computational power have offered a significant boost to learning techniques, fueling applications of Artificial Intelligence(AI) in varied domains – disease diagnoses [1], predicting bank failures [2] and evaluating faults in sports movements [3], etc.”
• Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases (2019, background): “AI applications can improve banks ’ operation ef fi ciency, fraud detection, and asset management (Fethi and Pasiouras 2010).”
• Efficiency of Turkish banking: Two-stage network system. Variable returns to scale model (2011, background): “Furthermore, we expect that size, profitability, capitalization, net interest margin among other variables will be the main factors having the impact on bank efficiency levels as reported by Mester (1996), Isik and Hasan (2002, 2003), Athanasoglou et al., (2008), Fethi and Pasiouras (2010).”
• The Effect of Board Size and Composition on the Efficiency of UK Banks (2011, methodology): “Second, there are difficulties associated with the estimation of profit efficiency measures using DEA, such as collecting reliable and transparent information for output prices (see Fethi and Pasiouras, 2010 ) and disaggregating profit efficiency into technical and allocative efficiency (Coelli et”