Locating online loan applicants for an insurance company

Abstract

Purpose -This study aims to investigate insurance policy loan applicant characteristics. Additionally, it reveals the behaviour patterns of heavy users who have applied for at least two loans. A policy loan prediction model is established which is designed to increase loan application rates. Design/methodology/approach -The proposed model is implemented using data-mining techniques and comprises two mechanisms: a business rule generator and a recommendation mechanism. Two analytical approaches, the C.5 and Apriori algorithm, are employed to analyse the profile and browsing log DBs of insured individuals. The prediction model is verified by actual data from a Taiwanese insurance company. Findings -The data-mining results reveal that five attributes are ultimately used to establish the prediction model, namely: gender, marketing channel, insurance type, area of policy owner, and assumed interest rate. Additionally, the analytical results also indicate that insured individuals apply for loans as a result of arbitrage inducement. The accuracy of loan applicant prediction can exceed 70 per cent. Finally, some interesting patterns emerge for heavy users, such as the finding that loan applicants are used to applying for loans continuously (loan application repetition is on average two to three times). Research limitations/implications -Some policy owners who are unfamiliar with the web interface prefer to contact insurance personnel directly to discuss their insurance needs, and thus no browsing records are available for such users. In such cases only the profile could be collected and analysed. Practical implications -The proposed model enables insurance firms to locate potential loan applicants according to the data-mining results. As in the illustration scenario in the paper, insurance personnel can contact these potential loan applicants before they submit loan applications to the bank. Additionally, loan-related information is provided for online insurance users based on their browsing logs. The loan application rate is thus expected to increase, along with interest revenue. Originality/value -As long as the policy proceeds, the interest income from the policy loan seems to be a good option for extending insurance company operational earnings. Understanding the characteristics of loan applicants will provide helpful information. Besides, the proposed mechanism will be more appropriate to online users, who are unwilling to deal with unwanted information.

Antecedents to Indian customers attitude towards online insurance services

“…As with online banking, customers may be willing to adopt e-banking technologies if they have the assurance that their problems will be appropriately handled and resolved, promising confidentiality in the transaction (Goldfarb, 2001); similar behaviour may be expected in case of online insurance. During the past decade, the ubiquity of the internet has made it possible for customers to increasingly access insurance services online, specifically using it for gathering information about products, financial consultancy and indemnity planning (Yang and Wang, 2008). However, the inhibitions of the customers need to be tackled for making the online insurance model successful.…”

A framework for identifying the linkability between Web servers for enhanced internet computing and E-commerce

“…deducing Web relations and number of visits through analysis of patterns [19]. e) Products browsing: allowing for the automation of shopping and trading by inferring the ranks of different products and services.…”

Customer behavior toward online insurance services in India

“…During the past decade, the ubiquity of Internet has made it possible for customers to access insurance services online for gathering information about products, fi nancial consultancy and indemnity planning. 60 An important attribute of online insurance service satisfaction is the trust customer envisages while using the service. Research states that online trust is important in online environments 61,62 and customers ' satisfaction and loyalty is governed by Web sites ' security features.…”