构建我国机动车保险欺诈识别的指标体系——基于江、浙、沪机动车保险索赔样本数据

保险欺诈影响保险业的偿付能力,严重的保险欺诈甚至会导致保险市场失效。本文基于江、浙、沪调研采集的机动车保险索赔样本数据,通过Logistic分布下二元选择模型的实证分析,得到关于机动车保险欺诈识别的指标特征及相关解释;同时指出目前我国保险欺诈识别体系中尚存的不足之处,并就保险诚信的构建提出相关对策建议。     

大数据背景下的农险反欺诈检测:国际经验与技术选择

中国农业保险市场中欺诈骗保等违法行为不容忽视,亟需运用数据挖掘技术提高农业保险发展质量。首先,本文对反欺诈检测的常用方法进行了梳理,包括异常值检测、聚类法、线性回归法、社会关系网络分析法等方法。其次,本文总结美国运用数据挖掘技术开展农业保险反欺诈检测的基本经验。美国利用以政府主导、研究机构参与的模式,开发出多种欺诈检测项目,为美国农业保险节约巨额资金。再者,基于国际经验,本文提出适用于中国农业保险反欺诈检测的相关性异常值检测法、合谋关系检测法和机器学习法。最后,为进一步推动数据挖掘技术在中国农业保险反欺诈检测中的运用,本文提出建立农业保险大数据库、建立数据挖掘合作平台、建立常态化的数据利用机制、以及培育和激励数据挖掘人才等建议。     

基于BP神经网络的保险欺诈识别研究——以中国机动车保险索赔为例

当前保险欺诈在国内外呈现蔓延态势,尤其体现在机动车保险领域,欺诈识别已成为保险欺诈研究的核心内容.目前保险欺诈识别有统计回归和神经网络两大类方法,这两种方法在指导思想和识别流程上各有优缺.本文基于我国财产保险公司车险索赔样本数据,检验BP神经网络在我国保险欺诈识别中的有效性;同时为了尝试统计回归和神经网络的有效融合,本...     

Can alert models for fraud protect the elderly clients of a financial institution?

ABSTRACT

Using account-level transaction data at a major financial institution, we predict the incidence of suspicious activity that can be related to the external financial fraud of its elderly clients. The data consists of over 5 million accounts of clients aged 70 years and older, and over 250 million transactions extending from January 2015 to August 2016. Our main focus is to improve the detection of alerts within a proprietorial transaction monitoring system. Using logistic regression, random forest and support vector machine learning techniques, together with corrections for imbalanced alert samples, we provide a new alert model for the protection of elderly clients at a financial institution, with out-of-sample predictive accuracy. Our findings show the relative influence of client traits and account activity in our select external fraud alert models.     

保险欺诈防范研究与思考

在我国保险欺诈问题日趋严重的背景下,本文通过博弈论理论以及matlab等软件进行回归预测模型的构建,对国际上在反保险欺诈领域领先的国家采取的措施及其经验进行了定量分析。同时,结合我国国情,提出解决我国保险欺诈问题的建议。     

论《消费者保护法》中的惩罚性赔偿在保险领域的适用

在保险人欺诈的情况下,保险消费者可以依据《消费者保护法》第五十五条第一款请求惩罚性赔偿。在保险领域适用惩罚性赔偿并不会与损失填补原则发生冲突,也不会动摇保险的射幸性。在我国《消费者保护法》的体系内,惩罚性赔偿中的欺诈限于故意。与传统民法不同的是,《消费者保护法》中欺诈的认定不需要保险消费者因受欺诈而作出违背真实意志的意思表示。保险人恶意拒赔的行为可以直接认定为欺诈,从而适用惩罚性赔偿以解决保险理赔难的困境。惩罚性赔偿以保险费作为计算基数。     

Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China

The current research aims to launch effective accounting fraud detection models using imbalanced ensemble learning algorithms for China A-Share listed firms. Based on a sample of 33,544 Chinese firm-year instances from 1998 to 2017, this research respectively established one logistic regression and four ensemble learning classifiers (AdaBoost, XGBoost, CUSBoost, and RUSBoost) by 12 financial ratios and 28 raw financial data. Additionally, we divided the sample into the train and test observations to evaluate the classifiers' out-of-sample performance. In detail, we applied two metrics, namely, Area under the ROC (receiver operating characteristic) curve (AUC) and Area under the Precision-Recall curve (AUPR), to evaluate classifiers' discriminability. In the supplement test, this study put forward an algebraic fused model on the basis of the four ensemble learning classifiers and introduced the sliding window technique. The empirical results showed that the ensemble learning classifiers can detect accounting fraud for the imbalanced China A-listed firms far more effectively than the logistic regression model. Moreover, imbalanced ensemble learning classifiers (CUSBoost and RUSBoost) effectively performed better than the common ensemble learning models (AdaBoost and XGBoost) in average. The algebraic fused model in the supplement test also obtained the highest average AUC and AUPR among all the employed algorithms. Our results offer firm support for the potential role of Machine Learning (ML)-based Artificial Intelligence (AI) approaches in reliably predicting accounting fraud with high accuracy. Similarly, for the Chinese settings, our ML-based AI offers utmost advantage in forecasting accounting fraud. Finally, this paper fills the research gap on the applications of imbalanced ensemble learning in accounting fraud detection for Chinese listed firms.     

Bagging集成方法在保险欺诈识别中的应用研究

保险欺诈不仅危及保险公司的正常经营,增加投保人的负担,甚至有可能影响到国家的金融稳定。随着大数据时代的到来,保险反欺诈亟需引入革命性技术。Bagging集成方法以其可调节模型结构、易于部署、参数空间可控、支持并行运算等特点成为保险公司进行保险反欺诈一个好的选择。Bagging方法主要包括Bagging算法、Random Subspace算法、Random Patches算法,它们又能与不同基学习器结合构成新的分支算法及算法特例。本文基于这些算法对保险欺诈问题进行了实证检验,分析了各算法及与基学习器的适用性问题,以及基学习器个数对算法表现的影响。分析发现:针对保险欺诈识别问题,在Bagging、Random Subspace、Random Patches三者之中,Random Patches算法的表现最好,Bagging的运行时间最短;不同算法适用的基学习器不同,但总体来说最适合Bagging集成方法的是决策树;基于决策树的方法都一致选择是否委托律师代理作为最重要的特征;基学习器个数对不同Bagging算法表现的影响并不一致。     

论惩罚性赔偿适用保险业的法律规制路径

《消费者权益保护法》已将保险消费者纳入调整范畴,但第五十五条规定的惩罚性赔偿制度,在适用保险合同纠纷的司法实践时,法院却存在承认与不承认两种分歧.惩罚性赔偿可适用于保险业,从适用原则、适用要件与适用目的的角度分析,均存在理论上的正当性.未来惩罚性赔偿在保险业适用的规制路径应从遵循欺诈行为主观故意要件的实践场域、重构保险人欺诈的证明责任分配规则、量定惩罚性赔偿在保险业中的金额基数及倍数、建立分立式赔偿制度四个方面进行,从而进一步完善我国保险消费者救济权保护的规范体系.     

Do Sentiments Matter in Fraud Detection? Estimating Semantic Orientation of Annual Reports

We present a novel approach for analysing the qualitative content of annual reports. Using natural language processing techniques we determine if sentiment expressed in the text matters in fraud detection. We focus on the Management Discussion and Analysis (MD&A) section of annual reports because of the nonfactual content present in this section, unlike other components of the annual reports. We measure the sentiment expressed in the text on the dimensions of polarity, subjectivity, and intensity and investigate in depth whether truthful and fraudulent MD&As differ in terms of sentiment polarity, sentiment subjectivity and sentiment intensity. Our results show that fraudulent MD&As on average contain three times more positive sentiment and four times more negative sentiment compared with truthful MD&As. This suggests that use of both positive and negative sentiment is more pronounced in fraudulent MD&As. We further find that, compared with truthful MD&As, fraudulent MD&As contain a greater proportion of subjective content than objective content. This suggests that the use of subjectivity clues such as presence of too many adjectives and adverbs could be an indicator of fraud. Clear cases of fraud show a higher intensity of sentiment exhibited by more use of adverbs in the “adverb modifying adjective” pattern. Based on the results of this study, frequent use of intensifiers, particularly in this pattern, could be another indicator of fraud. Moreover, the dimensions of subjectivity and intensity help in accurately classifying borderline examples of MD&As (that are equal in sentiment polarity) into fraudulent and truthful categories. When taken together, these findings suggest that fraudulent MD&As in contrast to truthful MD&As contain higher sentiment content. Copyright © 2016 John Wiley & Sons, Ltd.     

Full Insurance, Bayesian Updated Premiums, and Adverse Selection

In the classic Rothschild-Stiglitz model of adverse selection in a competitive environment, we analyse a no-claims bonus type contract (bonus-malus). We show that, under full insurance coverage, if the insurance company applies Bayes's rule to learn about client probability types over time and uses this information in premium calculations for contract renewals, then there exist conditions under which all client types strictly prefer the Bayesian updating contract to the classic Rothschild-Stiglitz separating equilibrium.     

Understanding the police response to fraud: the challenges in configuring a response to a low-priority crime on the rise

ABSTRACT

Previous research has demonstrated the comparative lack of priority fraud receives from government and law enforcement in the UK compared to other serious offences, as well as shortcomings in the overall approach to investigation. This paper examines the current state of affairs in the light of changes aimed at addressing these limitations. It incorporates findings from a national survey of police forces, as well as a local survey of police personnel in three forces supplemented by interviews. The findings suggest that the situation has become more complicated. Many police officers interviewed did not feel that the police response in their own area was effective, and that their colleagues often lacked the appropriate skill sets needed. Moreover, forces were not confident they were recruiting the right people to tackle fraud. The paper has important lessons for policing internationally.