Research on the Relationship between Cooperation Innovation Expenditure and Economic Output of China’s Pharmaceutical Industry

Objective To empirically analyze the relationship between cooperation innovation expenditure and economic output of China’s pharmaceutical industry,and provide a reference for improving its economic benefits and the capability of cooperation innovation in the future.Methods The relevant data of China’s pharmaceutical industry from 2000 to 2016 was selected as a sample.Based on the co-integration theory,an error correction model was established to conduct Granger test of causality to study the relationship between cooperation innovation expenditure and economic output of China’s pharmaceutical industry.Results and Conclusion The cooperation innovation expenditure of China’s pharmaceutical industry has a significant positive impact on economic output.If cooperation innovation expenditure increases 1%,its economic output will go up by 1.7%.At the same time,the long-term promotion effect of cooperation innovation expenditure on economic output is more significant than the short-term effect.

Online payment fraud:from anomaly detection to risk management

Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.