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,wit...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.展开更多
本文根据损失分布法的基本原理,采用聚合风险模型度量新农合医疗保险的欺诈风险,并运用蒙特卡洛模拟对新农合欺诈风险的经验数据进行了实证分析。研究表明,社会专门欺诈团伙、定点医疗机构和参合农民是三大主要欺诈主体,其中社会专门欺...本文根据损失分布法的基本原理,采用聚合风险模型度量新农合医疗保险的欺诈风险,并运用蒙特卡洛模拟对新农合欺诈风险的经验数据进行了实证分析。研究表明,社会专门欺诈团伙、定点医疗机构和参合农民是三大主要欺诈主体,其中社会专门欺诈团伙和定点医院的欺诈对新农合基金造成的损失最大(占94%);欺诈风险损失频率服从正态分布,其损失强度(损失金额对数值)服从we ibu ll分布,在99%的置信水平下,我国每年需计提约2897万元的欺诈风险准备金。展开更多
基金from any funding agency in the public,commercial,or not-for-profit sectors.
文摘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.
文摘本文根据损失分布法的基本原理,采用聚合风险模型度量新农合医疗保险的欺诈风险,并运用蒙特卡洛模拟对新农合欺诈风险的经验数据进行了实证分析。研究表明,社会专门欺诈团伙、定点医疗机构和参合农民是三大主要欺诈主体,其中社会专门欺诈团伙和定点医院的欺诈对新农合基金造成的损失最大(占94%);欺诈风险损失频率服从正态分布,其损失强度(损失金额对数值)服从we ibu ll分布,在99%的置信水平下,我国每年需计提约2897万元的欺诈风险准备金。
