ClaimChain:用于处理保险索赔处理的安全区块链平台

Naga Ramya Bhamidipati, Varsha Vakkavanthula, George Stafford, Masrik A. Dahir, R. Neupane, Ernest Bonnah, Songjie Wang, J. Murthy, K. A. Hoque, P. Calyam
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引用次数: 4

摘要

保险索赔处理涉及多领域实体和多源数据,以及许多人与代理的交互。因此,这种处理传统上是人工密集且耗时的。基于区块链技术的智能自动化平台可以显著提高理赔处理的规模和响应时间。然而,有必要保护这些平台免受欺诈(例如,重复索赔)和由于网络攻击(例如,Sybil攻击)造成的数据完整性损失。在本文中,我们提出了一种新颖的“ClaimChain”,这是一个联盟区块链平台,通过增加共享智能和保险公司的参与,改变了最先进的NICB/ISO数据库架构方法。ClaimChain的功能包括:(a)通过实施区块链基础设施实现保险索赔处理的自动化,(b)通过数据完整性攻击的攻击树形式化进行基础设施级威胁建模,以及(c)通过机器学习模型和基于风险严重性的风险评分对识别出的突出危险信号进行应用级欺诈建模。我们通过模拟实际的大量索赔处理的区块链交易来评估ClaimChain的可扩展性。此外,我们表明,通过实施安全设计原则,可以减轻基础设施级别的数据完整性攻击(如减少24%的损失概率)。我们还在ClaimChain中的开放数据集上执行欺诈检测,以展示机器学习模型如何以98%的准确率检测欺诈活动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ClaimChain: Secure Blockchain Platform for Handling Insurance Claims Processing
Insurance claims processing involves multi-domain entities and multi-source data, along with a number of human-agent interactions. Consequently, this processing is traditionally manually-intensive and time-consuming. Blockchain technology-based platforms for intelligent automation can significantly improve the scale and response time of claims processing. However, there is a need to secure such platforms against fraud (e.g., duplicate claims) and the loss of data integrity caused due to cyber-attacks (e.g., Sybil attack). In this paper, we propose a novel “ClaimChain”, a consortium Blockchain platform that transforms the state-of-the-art NICB/ISO database architecture approach through increased shared intelligence and participation of insurance companies. ClaimChain features include: (a) automation of insurance claim processing via implementation of a Blockchain infrastructure, (b) infrastructure-level threat modeling via attack tree formalism for data integrity attacks, and (c) application-level fraud modeling for identified prominent red flags through machine learning models and risk scoring on the basis of risk severity. We evaluate the scalability of ClaimChain by simulating realistically large number of Blockchain transactions of claim processing. Further, we show that data integrity attacks at the infrastructure-level can be mitigated (as seen in reduction of 24% probability in loss) through implementation of security design principles. We also perform fraud-detection over an open dataset in ClaimChain to show how machine learning models can detect fraudulent activity with 98% accuracy.
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