Credit Card Anti-Fraud Prediction Based on Ensemble earning

Abstract

In recent years, due to the development of Internet technology in the financial industry, cardless and cashless payments have become increasingly popular. At present, people only need to bind their cards to their cell phones to scan the code for payment. In the meanwhile, as the use of credit cards is vigorously promoted nationwide, younger generations relied more on the credit card debt for consumption. However, although these developments have brought a lot of convenience to people's travel and their life, the virtual nature of the Internet and the "Enjoy First, Pay Later" feature of credit cards have led to a significant increase in potential credit risk in the entire transaction, triggering more and more new credit card fraud incidents. While the overall financial environment in China is relatively healthy, the fact that fraudulent behaviors such as credit card overdrafts, counterfeit cards, and credit card frauds are all personal conduct, making it difficult to detect and prevent these individuals from acting illegally in a timely manner. Although fraud represents a small percentage of the overall transaction size, its bad debt losses to merchants and banks can be significant. In order to make a timely prediction of fraud, there are already many single-model machine learning methods such as decision trees and logistic regression. However, the generalization ability of these models is not good enough in the face of complex user behavior features. In addition, since the probability of fraudulent behavior is very small, resulting in few samples that can be trained, even if a model has a high accuracy rate, there is no guarantee that it can accurately predict fraudulent behavior. Therefore, this paper proposes the Adaboost method based on ensemble learning and uses SMOTE oversampling based on a few classes of fraudulent samples of user features to solve the above existing problems.