E-Risk Insurance Product Design: A Copula Based Bayesian Belief Network Model

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Author(s): Arunabha Mukhopadhyay (Indian Institute of Management Lucknow, India), Samir Chatterjee (Claremont Graduate University, India), Debashis Saha (Indian Institute of Management Calcutta, India), Ambuj Mahanti (Indian Institute of Management Calcutta, India)and Samir K. Sadhukhan (Indian Institute of Management Calcutta, India)
Copyright: 2009
Pages: 9
Source title: Handbook of Research on Social and Organizational Liabilities in Information Security
Source Author(s)/Editor(s): Manish Gupta (State University of New York, USA)and Raj Sharman (State University of New York, USA)
DOI: 10.4018/978-1-60566-132-2.ch004

Keywords: Information Science Reference / Information Security & Privacy / IT Security and Ethics / Security & Forensics

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Abstract

An online business organization spends millions of dollars on firewalls, anti-virus, intrusion detection systems, digital signature, and encryption, to ensure minimal security breach. Nonetheless, a new virus or a clever hacker can easily compromise these deterrents, resulting in losses to the tune of millions of dollars annually. To minimize the financial loss, we propose that online businesses should invest in e-risk insurance products as a complementary alternative, above the network security appliances. In this work, we develop a Copula aided Bayesian Belief Network (CBBN) model, to assist insurance companies to design e-insurance products. The CBBN model does an e-vulnerability assessment (e-VA) and e-risk quantification (e-RQ). We first draw a casual diagram (BBN) stating the probable reason for security failure in an organization. We assume the marginal distributions for each of the nodes of the diagram. Using the CBBN model we compute the joint probability of the constituent nodes of the BBN. Next the conditional probability of each of the occurrences of the malicious event is arrived at. We then assume a loss distribution, and using the principles of collective risk modeling, we arrive at the expected severity of the attack. The e-risk insurance companies compute the premium, by charging an extra (i.e., overloading and contingency loading), over the expected severity of attack.

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E-Risk Insurance Product Design: A Copula Based Bayesian Belief Network Model

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