Progress on Machine Learning for Regional Financial Risk Prevention

Abstract

The regional financial risks prevention (RFRP) is indispensable in managing the regional traditional financial risks (TFR) or preventing the regional financial systemic risks (FSR). With the growing of big data and the uncertainty of financial risk types, traditional econometrics methods are facing insurmountable difficulties in terms of the efficiency, accuracy, and application of financial risk prevention modeling. Today, increasing future methods and technologies for machine learning (ML) for RFRP prevention have been paid much attention by researchers. A new scientific classification of RFRP prevention and conceptual basis of ML methods are firstly put forward.Secondly, this paper summarizes the ML methods and application of regional TFR prevention, compares their key logic, model algorithm and learning effect of the representative literature, and categorizes the advantages, limitations and traditional scenarios of ML methods. Thirdly, it combs the ML methods and application of regional FSR prevention, analyzes the key context, ML algorithm and learning effect of the seminal documents, and compares the benefits, disadvantages and financial risk contexts of ML methods. Finally, six promising technologies and emerging directions of ML methods for RFRP prevention are proposed.

Keywords

• |machine learning (ml)|regional financial risk prevention (rfrp)|traditional financial risk (tfr)|financial systemic risk (fsr)