Application of Big Data Unbalanced Classification Algorithm in Credit Risk Analysis of Insurance Companies

Abstract

The 2008 global financial crisis triggered by subprime mortgage crisis in the United States and the ongoing European debt crisis have urged governments and academics to pay high attention to financial industry risk supervision. The financial industry has actively implemented comprehensive risk management. As an important component of the financial industry, the insurance industry implements comprehensive risk management to control the risks of insurance companies. Propose an integrated learning model based on imbalanced dataset resampling and apply it to UCI dataset (University of California Irvine). First, resampling technology is used to preprocess the unbalanced dataset to obtain a relatively balanced training set. Then, use the classic backpropagation neural network, classic k-nearest neighbor, and classic Naive Bayes three algorithms as the base classifier and use the Bagging strategy to get the ensemble learning model. In order to verify its effectiveness, F-measure and G-mean methods are used to measure the performance of the classifier. The subject mainly focuses on the classification of relevance vector machine (RVM) in two types of large-scale datasets, imbalanced and balanced, and proposes solutions for these two types of data. This explains the effectiveness of the disequilibrium classification algorithm used in the risk analysis of insurance companies.