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How artificial intelligence incidents affect banks and financial services firms? A study of five firms
16 references·0 citing·49 second hop
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References

  • 1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY14,869 cited
  • 2010Consumer credit-risk models via machine-learning algorithms861 cited
  • 2021Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis723 cited
  • 2021Consumer-lending discrimination in the FinTech Era655 cited
  • 2016A survey of the applications of text mining in financial domain265 cited
  • 2015Risk and Risk Management in the Credit Card Industry260 cited
  • 2015Size, leverage, and risk-taking of financial institutions241 cited
  • 2014The effect of feature selection on financial distress prediction234 cited
  • 2018Artificial intelligence and augmented intelligence collaboration: regaining trust and confidence in the financial sector228 cited
  • 2018Predicting mortgage default using convolutional neural networks181 cited
  • 2016The impact of non-interest income on bank risk in Australia171 cited
  • 2020Do Banks Price Firms' Data Breaches?147 cited
  • 2016Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics1140 cited
  • 2006Estimating the probability of informed trading—does trade misclassification matter?113 cited

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2019
Grabit: Gradient tree-boosted Tobit models for default prediction
98 cited
  • 2015The perennial challenge to counter Too-Big-to-Fail in banking: Empirical evidence from the new international regulation dealing with Global Systemically Important Banks84 cited
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