Mitic, Peter;
Cooper, James;
(2020)
Enhanced Credit Prediction Using Artificial Data.
In: Analide, Cesar and Novais, Paulo and Camacho, David and Yin, Hujun, (eds.)
Intelligent Data Engineering and Automated Learning – IDEAL 2020.
(pp. pp. 44-53).
Springer Nature: Cham, Switzerland.
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Abstract
Analysing credit data using a neural network has hitherto proved to be very resilient to attempts to improve success rates in prediction. We present a technique using simulated data which results in a marginal improvement in success rate. The empirical probability distribution for each feature of the training data is determined, and random samples are drawn from those distributions. The result is termed ‘artificial’ data. It is then possible to generate equal volumes of data for each of the binary outcomes (default or not), thereby alleviating a class imbalance classification problem. The simulation method uses a copula (to preserve the correlation structure of the original data) and optimal feature weighting to give acceptable results. The results indicate that overall percentage success rates for the more common outcome only are improved, but there is a more significant improvement in the AUC metric. The significance of this result in the context of assessing credit worthiness is discussed.
| Type: | Proceedings paper |
|---|---|
| Title: | Enhanced Credit Prediction Using Artificial Data |
| Event: | 21st International Conference. Intelligent Data Engineering and Automated Learning – IDEAL 2020 |
| ISBN-13: | 9783030623647 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-62365-4_5 |
| Publisher version: | https://doi.org/10.1007/978-3-030-62365-4_5 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Artificial data, Copula, Importance weight, Neural network, Lorenz curve |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10163341 |
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