本文選題：保險(xiǎn)欺詐 + 矩陣排序��； 參考：《保險(xiǎn)研究》2017年02期

【摘要】：隨著我國保險(xiǎn)業(yè)的蓬勃發(fā)展,車險(xiǎn)領(lǐng)域的保險(xiǎn)欺詐問題日益嚴(yán)峻。鑒于我國在機(jī)動車輛保險(xiǎn)欺詐檢測技術(shù)方面較為滯后,本文針對車險(xiǎn)反欺詐檢測方法進(jìn)行研究,首次將團(tuán)伙微觀建模應(yīng)用于機(jī)動車輛保險(xiǎn)欺詐檢測。通過引入廣義團(tuán)伙概念,采用基于矩陣的相似度計(jì)算、秩排序和變換算法,對極小概率發(fā)生但又高度可疑的團(tuán)伙實(shí)現(xiàn)有效識別。相較于傳統(tǒng)方法具有更準(zhǔn)確和高效的實(shí)際應(yīng)用價(jià)值:引入廣義團(tuán)伙對車險(xiǎn)欺詐進(jìn)行全方位識別;將可疑欺詐團(tuán)伙的車輛碰撞關(guān)系映射為人網(wǎng)絡(luò)關(guān)系,從而避免各種人為規(guī)避行為對識別和檢測的影響;不需要確定的欺詐樣本,也不需要進(jìn)行模型訓(xùn)練就可以直接應(yīng)用;采用矩陣數(shù)值運(yùn)算完成全部過程,有效提高計(jì)算效率。
[Abstract]:With the rapid development of insurance industry in China, insurance fraud in auto insurance field is becoming more and more serious. In view of the lag in the detection technology of motor vehicle insurance fraud in China, the anti-fraud detection method of vehicle insurance is studied in this paper, and the group microscopic modeling is applied to the detection of fraud in motor vehicle insurance for the first time. By introducing the concept of generalized group, using the similarity calculation based on matrix, rank ranking and transformation algorithm, we can effectively identify the group with minimal probability but highly suspicious. Compared with the traditional method, it has more accurate and efficient practical application value: introducing the generalized group to identify the vehicle insurance fraud in all directions, mapping the vehicle collision relation of the suspected fraud group to the network relationship, In order to avoid the impact of various human evading behavior on identification and detection; do not need to determine the sample fraud, and do not need model training to be directly applied; the use of matrix numerical operation to complete the whole process, effectively improve the efficiency of the calculation.
【作者單位】： 西安交通大學(xué)經(jīng)濟(jì)與金融學(xué)院;深圳般若計(jì)算機(jī)系統(tǒng)股份有限公司;太平財(cái)產(chǎn)保險(xiǎn)有限公司;
【分類號】：F842.634;D924.35
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本文編號：2107619