Practical Directions for the Integration of Financial Big Data and Quantitative Risk Control

Xun Hong

doi:10.62051/ijphmr.v6n6.08

Authors

Xun Hong Faculty of Business, Macau University of Science and Technology, Macao, 999078, China

DOI:

https://doi.org/10.62051/ijphmr.v6n6.08

Keywords:

Financial big data, Quantitative risk control, Machine learning, Alternative data, Credit scoring, Algorithmic integration

Abstract

With the all-round development of digital technology in recent years, so too have people's risk-bearing habits changed. Systematically explore the practical directions of applying large-scale financial big data and high-end quantitative risk control frameworks in this paper. Utilise a large number of organised and unorganised alternative data to improve the accuracy of prediction and operation. Introduce new kinds of high-performance, frequently updated quantitative algorithms and move away from the old credit-scoring system. The first few practical applications are shown below: real-time fraud detection, dynamic credit assessment and comprehensive market risk monitoring. This paper will also discuss the issues that have arisen due to such a combination, such as data privacy laws, algorithmic transparency, and a large-scale computational infrastructure. In short, now that large-scale big data analysis and high-end quantitative methods have been introduced, all-encompassing risk management systems for large banks can be constructed to face the turbulence of the economy, credit losses due to defaults can be reduced, and continuous profit generation in the era of deep globalisation can be achieved.

References

[1] Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: A review. Journal of the Royal Statistical Society: Series A (Statistics in Society), 160(3), 523–541. https://doi.org/10.1111/1467-985X.00078

[2] Thomas, L. C. (2000). A survey of credit and behavioural scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, 16(2), 149–172. https://doi.org/10.1016/S0169-2070(99)00045-4

[3] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324

[4] Witten, I. H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques (2nd ed.). Morgan Kaufmann.

[5] Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.

[6] Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787. https://doi.org/10.1016/j.jbankfin.2010.06.009

[7] Einav, L., Jenkins, M., & Levin, J. (2013). The impact of credit scoring on consumer lending. The RAND Journal of Economics, 44(2), 249–274. https://doi.org/10.1111/1756-2171.12020

[8] Baesens, B., Roesch, D., & Scheule, H. (2016). Credit risk analytics: Measurement techniques, applications, and examples in SAS. John Wiley & Sons.

[9] Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136. https://doi.org/10.1016/j.ejor.2015.05.030

[10] Giudici, P. (2018). Fintech risk management: A research challenge for artificial intelligence in finance. Frontiers in Artificial Intelligence, 1, 1. https://doi.org/10.3389/frai.2018.00001