【摘要】：從全球范圍來看,保險欺詐案件呈逐年遞增態(tài)勢,而且每年都會造成數(shù)千億美元損失。保險欺詐不僅扭曲了保險定價機制,損害保險經(jīng)營的最大誠信原則,而且還嚴重威脅醫(yī)�；鸢踩�,妨礙醫(yī)保政策的有效實施。因此,反欺詐研究尤其是欺詐識別研究已成為學(xué)術(shù)界和實務(wù)界研究的熱點領(lǐng)域。關(guān)于保險欺詐的理論研究已較為深入,研究人員從信息經(jīng)濟學(xué)、社會心理學(xué)等角度對欺詐的形式、成因及反欺詐措施等方面進行了系統(tǒng)研究；機動車保險欺詐的實證研究從最初的統(tǒng)計分析方法發(fā)展到人工智能識別技術(shù)以及兩者的有機結(jié)合,并從概念模型構(gòu)建向?qū)嵶C分析逐步過渡。然而,受健康保險信息技術(shù)和數(shù)據(jù)儲備的限制,以及復(fù)雜醫(yī)療環(huán)境的阻礙,相對于國際上健康保險欺詐實證研究的熱絡(luò),國內(nèi)健康險欺詐識別與度量的研究還寥寥無幾。 基于此,本文在參考國內(nèi)機動車輛險欺詐識別研究成果的基礎(chǔ)上,嘗試運用統(tǒng)計回歸和神經(jīng)網(wǎng)絡(luò)相結(jié)合的方法對健康服務(wù)需求方道德風(fēng)險引致的欺詐進行識別研究。首先結(jié)合國內(nèi)外保險欺詐的相關(guān)研究對我國健康險欺詐問題進行理論分析；以住院醫(yī)療保險具體險種為例搜集索賠案件樣本數(shù)據(jù),在理論分析的基礎(chǔ)上結(jié)合專家意見確定欺詐識別因子；運用logistic回歸分析提取具有模型顯著性的識別因子,作為輸入數(shù)據(jù)對構(gòu)建的BP神經(jīng)網(wǎng)絡(luò)模型進行訓(xùn)練,并選取檢驗樣本對模型的有效性進行預(yù)測檢驗；最后根據(jù)研究結(jié)果提出針對性的反欺詐措施和政策建議。研究結(jié)果顯示,嵌入logistic回歸分析的BP神經(jīng)網(wǎng)絡(luò)模型在特定條件下和一定范圍內(nèi)可以作為健康險欺詐識別的有效工具。本研究在一定程度上揭示了我國健康險欺詐的特征與規(guī)律,對改進保險機構(gòu)欺詐識別技術(shù)、提高反欺詐能力具有一定的現(xiàn)實意義。
[Abstract]:Globally, insurance fraud cases are increasing year by year and cost hundreds of billions of dollars a year. Insurance fraud not only distorts the insurance pricing mechanism and damages the principle of the greatest integrity of insurance management, but also seriously threatens the security of medical insurance fund and hinders the effective implementation of medical insurance policy. Therefore, the research on anti-fraud, especially fraud identification, has become a hot area in academia and practice. The theoretical study on insurance fraud has been more in-depth, the researchers from the information economics, social psychology and other aspects of the form of fraud, causes and anti-fraud measures were systematically studied; The empirical study of motor vehicle insurance fraud developed from the initial statistical analysis method to artificial intelligence identification technology and the organic combination of the two, and gradually transition from conceptual model construction to empirical analysis. However, due to the limitation of information technology and data reserve of health insurance, and the hindrance of complex medical environment, there are few researches on identification and measurement of health insurance fraud in China compared with the international research on health insurance fraud. Based on this, this paper tries to use the method of combining statistical regression and neural network to identify the fraud caused by moral hazard on the demand side of health service based on the research results of domestic motor vehicle insurance fraud identification. Firstly, the theoretical analysis of health insurance fraud in China is made based on the related research of insurance fraud at home and abroad. Taking the inpatient medical insurance as an example to collect the sample data of the claim cases and determine the fraud identification factor on the basis of theoretical analysis combined with the expert opinion. The logistic regression analysis is used to extract the significant recognition factors of the model, and the BP neural network model is trained as input data, and the validity of the model is predicted by selecting the test samples. Finally, according to the results of the study, targeted anti-fraud measures and policy recommendations are put forward. The results show that the BP neural network model embedded in logistic regression analysis can be used as an effective tool for the identification of health insurance fraud under certain conditions and within a certain range. To a certain extent, this study reveals the characteristics and laws of health insurance fraud in China, which has a certain practical significance to improve the fraud identification technology of insurance institutions and improve the ability of anti-fraud.
【學(xué)位授予單位】：青島大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP183;F842.684

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本文編號：2324003