The Application of Recurrent Neural Networks on Early Detection of Credit Default Risk

@inproceedings{Owe2018TheAO,
  title={The Application of Recurrent Neural Networks on Early Detection of Credit Default Risk},
  author={Andreas Owe},
  year={2018}
}
Credit card companies lend money to borrowers on the presumption of the borrowers being able to pay back the borrowed amount plus a fee in the form of interest. There is an inherent risk in lending money: that the borrower will not be able to pay back the borrowed amount, and therefore default on the payment; causing the lender to lose their invested money. This thesis aims to determine if we can use recurrent neural networks in order to identify defaulted customers based on a customers account… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 21 REFERENCES

A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers

L. C. Thomas
  • International Journal of Forecasting, 16(2):149–172.
  • 2000
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Consumer credit scoring models with limited data

  • Expert Syst. Appl.
  • 2009
VIEW 1 EXCERPT
HIGHLY INFLUENTIAL

Noregs Bank Memo, Kunderetta betalingsformidling 2016

NoregsBank
  • (2).
  • 2017
VIEW 1 EXCERPT

Understanding the Impact of Heteroscedasticity on the Predictive Ability of Modern Regression Methods by

S. J. Gelfand, R. Lockhart
  • 2015