Financial Risks Identification Model based on Reinforcement Learning

At present, financial risks have become an important problem for companies due to the large amount of transcation data and the uncontrolled transcation behaviours. Therefore, identifying the risks is essential for companies to know the possible crisis and provide corrsponding measures, which also plays an irreplaceable component for marketing and economic development. However, existing identification methods are concentrated on using mathematical models and analyzing the correlations among risk factors, which can not satisfy large amount of financial data and complex risks relationships. In this work, we initally attempt to establish a learning model to extract the features of financial risks. Subsequently, a reinforcement learning model is trained to classify risks and non-risks data with enough training iterations. Finally, we evaluate the proposed model with real-life data-set and compare with existing risk identification models to measure the performance of proposed model with multi-levels metrics. From our experimental results, we can observe that our model can achieve the identification of risks data with acceptable accuracy and reasonable computation costs.