Solving customer insurance coverage sales plan problem using a multi-stage data mining approach

Kybernetes ◽  
2018 ◽  
Vol 47 (1) ◽  
pp. 2-19 ◽  
Author(s):  
Farshid Abdi ◽  
Kaveh Khalili-Damghani ◽  
Shaghayegh Abolmakarem
Keyword(s):  
Data Mining ◽  
Insurance Coverage ◽  
High Potential ◽  
Insurance Companies ◽  
Feature Subset ◽  
Insurance Company ◽  
Contract Term ◽  
Content Type ◽  
Data Mining Approach ◽  
Loyal Customers

Purpose Customer insurance coverage sales plan problem, in which the loyal customers are recognized and offered some special plans, is an essential problem facing insurance companies. On the other hand, the loyal customers who have enough potential to renew their insurance contracts at the end of the contract term should be persuaded to repurchase or renew their contracts. The aim of this paper is to propose a three-stage data-mining approach to recognize high-potential loyal insurance customers and to predict/plan special insurance coverage sales. Design/methodology/approach The first stage addresses data cleansing. In the second stage, several filter and wrapper methods are implemented to select proper features. In the third stage, K-nearest neighbor algorithm is used to cluster the customers. The approach aims to select a compact feature subset with the maximal prediction capability. The proposed approach can detect the customers who are more likely to buy a specific insurance coverage at the end of a contract term. Findings The proposed approach has been applied in a real case study of insurance company in Iran. On the basis of the findings, the proposed approach is capable of recognizing the customer clusters and planning a suitable insurance coverage sales plans for loyal customers with proper accuracy level. Therefore, the proposed approach can be useful for the insurance company which helps them to identify their potential clients. Consequently, insurance managers can consider appropriate marketing tactics and appropriate resource allocation of the insurance company to their high-potential loyal customers and prevent switching them to competitors. Originality/value Despite the importance of recognizing high-potential loyal insurance customers, little study has been done in this area. In this paper, data-mining techniques were developed for the prediction of special insurance coverage sales on the basis of customers’ characteristics. The method allows the insurance company to prioritize their customers and focus their attention on high-potential loyal customers. Using the outputs of the proposed approach, the insurance companies can offer the most productive/economic insurance coverage contracts to their customers. The approach proposed by this study be customized and may be used in other service companies.

scholarly journals INNOVATIVE MANAGEMENT STRATEGIES BY ACTIVITIES OF INSURANCE COMPANIES: INTERNATIONAL AND NATIONAL CONTEXT

2021 ◽  
pp. 65-70
Author(s):  
Vilenchuk O.
Keyword(s):  
Business Processes ◽  
Insurance Coverage ◽  
Management Strategies ◽  
Insurance Market ◽  
Insurance Companies ◽  
Insurance Company ◽  
Investment Activity ◽  
Innovative Strategies ◽  
Innovative Management

This review article is devoted to the study of innovative strategies for managing the activities of insurance companies. In the condi-tions of risky environment, rather high probability occurrence of threats of natural and technogenic character, problems concerning the necessity of a scientific substantiation of the process of the insurance market stakeholders’ interaction aggravate. It is established that insurance in the world economic space is an indispensable financial and economic tool for neutralizing a variety of risks, especially given today’s the socio-economic, financial, epidemiological dangers. It is proved that despite the key parameters’ positive dynamics of the Ukrainian insurance market development for 2009-2019, there is a need to intensify business and investment activity of its participants. The competitive environment of the insurance market requires insurance companies to implement innovative management strategies focused primarily on solving two interrelated problems: firstly, the expansion of property risks’ insurance coverage, as well as risks related to citizens’ life, health and ability to work, secondly, the formation of the insurers’ solvency and ensuring an acceptable level of insurance operations’ profitability in terms of certain types of insurance. It is determined that one of the primary tasks of the insurance company’s management is the management of its business processes aimed at forming a model of customer-oriented behaviour in relation to potential customers. The article emphasizes the need to use a variety of innovative management strategies to achieve medium-term and long-term goals of the company in the insurance market. In particular, the expediency of diversification and the use of alternative pricing strategies for insurance services for long-term and general types of insurance is argued. Proposals aimed at digitalization of the insurance market and wide application of FinTech technologies in the field of insurance services are formulated: automated underwriting, IOT-technologies; blockchain in insurance. Summarizing the results of the study, the author’s vision of the further insurance relations’ functioning of in society is specified. Keywords: risks, insurance company, insurers, insurance protection, insurance market stakeholders, management. Статтю присвячено дослідженню інноваційних стратегій управління діяльністю страхових компаній. В умовах ризикогенного середовища, досить високої ймовірності виникнення загроз природного та техногенного характеру загострюються проблеми щодо необхідності наукового обґрунтування процесу взаємодії стейкхолдерів страхового ринку. Аргументовано, що страхування у світовому економічному просторі є незамінним фінансово-економічним інструментом нейтралізації найрізноманітніших ризиків, особливо зважаючи на соціально-економічні, фінансові та епідеміологічні небезпеки сучасності. Визначено, що одним із першочергових завдань менеджменту страхової компанії є управління її бізнес-процесами, спрямованими на формування мо-делі клієнтоорієнтованості відносно потенційних клієнтів. Наголошено на необхідності використання різноманітних інноваційних стратегій управління для досягнення середньострокових та довгострокових цілей компанії на страховому ринку. Зокрема, аргу-ментовано доцільність здійснення диверсифікації та використання альтернативних стратегій ціноутворення на страхові послуги з довгострокових та загальних видів страхування. Сформульовано пропозиції, спрямовані на цифровізацію страхового ринку та широке застосування FinTech-технологій у сфері страхових послуг: автоматизований андерайтинг, ІОТ-технології; блокчейн у страхуванні. Узагальнюючи результати проведеного дослідження, конкретизовано авторські підходи до подальшого функціонування страхових відносин у суспільстві.Ключові слова: ризики, страхова компанія, страхувальники, страховий захист, стейкхолдери страхового ринку, управління.

The cyber-insurance market in Norway

2019 ◽  
Vol 28 (1) ◽  
pp. 54-67 ◽  
Author(s):  
Hayretdin Bahşi ◽  
Ulrik Franke ◽  
Even Langfeldt Friberg
Keyword(s):  
Insurance Market ◽  
Insurance Companies ◽  
Supply Side ◽  
Insurance Company ◽  
Marine Insurance ◽  
Structured Interviews ◽  
Content Type ◽  
General Data Protection Regulation ◽  
Cyber Insurance ◽  
General Data

Purpose This paper aims to describe the cyber-insurance market in Norway but offers conclusions that are interesting to a wider audience. Design/methodology/approach The study is based on semi-structured interviews with supply-side actors: six general insurance companies, one marine insurance company and two insurance intermediaries. Findings The Norwegian cyber-insurance market supply-side has grown significantly in the past two years. The General Data Protection Regulation (GDPR) is found to have had a modest effect on the market so far but has been used by the supply-side as an icebreaker to discuss cyber-insurance with customers. The NIS Directive has had little or no impact on the Norwegian cyber-insurance market until now. Informants also indicate that Norway is still the least mature of the four Nordic markets. Practical implications Some policy lessons for different stakeholders are identified. Originality/value Empirical investigation of cyber-insurance is still rare, and the paper offers original insights on market composition and actor motivations, ambiguity of coverage, the NIS Directive and GDPR.

scholarly journals Institutional forms and their competitive analysis of organization of insurance activities in Ukraine

2018 ◽  
Vol 2 (1) ◽  
pp. 99-110
Author(s):  
Olga Slobodianiuk ◽  
Galina Tolkacheva
Keyword(s):  
Development Strategy ◽  
Competitive Analysis ◽  
Swot Analysis ◽  
Objective Assessment ◽  
Insurance Coverage ◽  
Insurance Market ◽  
Insurance Companies ◽  
Insurance Company ◽  
Strategic Development ◽  
Ability To Act

Introduction. In the insurance market of Ukraine, there is a need for non-commercial insurance, which does not carry out business activities in favor of shareholders, but implements the principle of collective mutual assistance of insurance participants. Its necessity is determined, first of all, by the possibility at affordable prices to provide real insurance coverage to the general population, agricultural producers, small business representatives, and others like that. The development of the strategic development of the work of insurance companies prompts them to rational use of all their resources, in order to increase their efficiency at all stages of development. For this purpose, special methods of strategic management are used, which include the search for economic criteria that provide an objective assessment of the company's activities. Thus, conducting this analysis will enable each company to make it competitive on the insurance market. Aim and tasks. The purpose of the article is to reveal and substantiate the institutional forms and perform SWOT analysis to identify the weak and strong points of the organization of the insurance company and the strategic development of insurance activities in general. Research results. The article reveals the concept of institutional forms of organization of insurance activity in Ukraine. Conducted competitive analysis (SWOT-analysis) of strategic development of insurance companies and organization of insurance activity in Ukraine. The proposed ways of strategic development, weak and strong forms of organization of insurance activity are revealed. Conclusion. Insurance companies have financial institutionalization of their forms of activity. Under this concept is understood the process of consolidation of norms, roles, statuses, rules for bringing and combining them into the insurance system for the ability to act for insurance protection. The conducted analysis of the activity of the insurance company, its development strategy provides an opportunity to identify the most promising directions for its development and organization of work. Thus, the identification of weak and strong sides of the company, based on SWOT analysis, positively influences the development of the company's future strategy, its organizational structure, as well as the quality and price of insurance services.

scholarly journals A multi-attribute data mining model for rule extraction and service operations benchmarking

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hannan Amoozad Mahdiraji ◽  
Madjid Tavana ◽  
Pouya Mahdiani ◽  
Ali Asghar Abbasi Kamardi
Keyword(s):  
Data Mining ◽  
Service Operations ◽  
Rule Extraction ◽  
Service Industries ◽  
Quality Analysis ◽  
Behavior Patterns ◽  
Content Type ◽  
Attribute Data ◽  
Data Mining Approach ◽  
Combining Data

PurposeCustomer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.Design/methodology/approachThe authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.FindingsAs a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.Originality/valueThe authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.

scholarly journals Network-based pricing for 3D printing services in two-sided manufacturing-as-a-service marketplace

2020 ◽  
Vol 26 (1) ◽  
pp. 82-88 ◽  
Author(s):  
Deepak Pahwa ◽  
Binil Starly
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
3D Printing ◽  
Ad Hoc ◽  
Activity Based Costing ◽  
Content Type ◽  
Data Mining Approach ◽  
Price Range ◽  
Competitive Prices ◽  
Profile Characteristics

Purpose This paper presents approaches to determine a network-based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The purpose of this study is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad hoc and subjective prices. Design/methodology/approach A data mining approach with machine learning methods is used to estimate a price range based on the profile characteristics of 3D printing service suppliers. The model considers factors such as supplier experience, supplier capabilities, customer reviews and ratings from past orders and scale of operations, among others, to estimate a price range for suppliers’ services. Data were gathered from existing marketplace websites, which were then used to train and test the model. Findings The model demonstrates an accuracy of 65 per cent for US-based suppliers and 59 per cent for Europe-based suppliers to classify a supplier’s 3D printer listing in one of the seven price categories. The improvement over baseline accuracy of 25 per cent demonstrates that machine learning-based methods are promising for network-based pricing in manufacturing marketplaces Originality/value Conventional methodologies for pricing services through activity-based costing are inefficient in strategically priced 3-D printing service offering in a connected marketplace. As opposed to arbitrarily determining prices, this work proposes an approach to determine prices through data mining methods to estimate competitive prices. Such tools can be built into online marketplaces to help independent service bureaus to determine service price rates.

Using Twitter to engage with customers: a data mining approach

2015 ◽  
Vol 25 (3) ◽  
pp. 416-434 ◽  
Author(s):  
Shintaro Okazaki ◽  
Ana M. Díaz-Martín ◽  
Mercedes Rozano ◽  
Héctor David Menéndez-Benito
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Web 2.0 ◽  
Knowledge Sharing ◽  
Customer Engagement ◽  
Service Development ◽  
Content Type ◽  
Customer Complaints ◽  
Data Mining Approach ◽  
Robust Model

Purpose – The purpose of this paper is to explore customer engagement in Twitter via data mining. Design/methodology/approach – This study’s intended contributions are twofold: to find a clear connection among customer engagement, presumption, and Web 2.0 in a context of service-dominant (S-D) logic; and to identify social networks created by prosumers. To this end, the study employed data mining techniques. Tweets about IKEA were used as a sample. The resulting algorithm based on 300 tweets was applied to 4,000 tweets to identify the patterns of electronic word-of-mouth (eWOM). Findings – Social networks created in IKEA’s tweets consist of three forms of eWOM: objective statements, subjective statements, and knowledge sharing. Most objective statements are disseminated from satisfied or neutral customers, while subjective statements are disseminated from dissatisfied or neutral customers. Satisfied customers mainly carry out knowledge sharing, which seems to reflect presumption behavior. Research limitations/implications – This study provides partial evidence of customer engagement and presumption in IKEA’s tweets. The results indicate that there are three forms of eWOM in the networks: objective statements, subjective statements, and knowledge sharing. It seems that IKEA successfully engaged customers in knowledge sharing, while negative opinions were mainly disseminated in a limited circle. Practical implications – Firms should make more of an effort to identify prosumers via data mining, since these networks are hidden behind “self-proclaimed” followers. Prosumers differ from opinion leaders, since they actively participate in product development. Thus, firms should seek prosumers in order to more closely fit their products to consumer needs. As a practical strategy, firms could employ celebrities for promotional purposes and use them as a platform to convert their followers to prosumers. In addition, firms are encouraged to make public how they resolve problematic customer complaints so that customers can feel they are a part of firms’ service development. Originality/value – Theoretically, the study makes unique contributions by offering a synergic framework of S-D logic and Web 2.0. The conceptual framework collectively relates customer engagement, presumption, and Web 2.0 to social networks. In addition, the idea of examining social networks based on different forms of eWOM has seldom been touched in the literature. Methodologically, the study employed seven algorithms to choose the most robust model, which was later applied to 4,000 tweets.

scholarly journals Measuring the effectiveness of intermediary loyalty programmes in the motor insurance industry: loyal versus non-loyal customers

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Manuel Leiria ◽  
Efigénio Rebelo ◽  
Nelson deMatos
Keyword(s):  
Insurance Industry ◽  
Binary Logistic Regression ◽  
Data Access ◽  
Insurance Companies ◽  
Insurance Company ◽  
Content Type ◽  
Customer Data ◽  
Insurance Policies ◽  
Motor Insurance ◽  
Value Propositions

PurposeThe insurance industry has not been able to effectively retain its customers and struggles to establish and maintain long-lasting relationships with them. The purpose of this paper is thus to identify the main factors that explain the cancellation of motor insurance policies by individual customers, considering the influence of intermediaries on their decisions.Design/methodology/approachThe data used in this research is based on a sample of 3,500 insurance policies that lapsed during the period of analysis between January and July 2017, against another sample of 3,500 policies that did not lapse, from a major insurance company in Portugal. Binary logistic regression was used for data analysis, using IBM SPSS software.FindingsAggressive tactics by insurance companies for customer acquisition may induce the cancellation of insurance policies. More valuable customers, the policies with higher premiums and recent claims, as well as the ancillary intermediaries and agents, are determinants of insurance cancellation. Conversely, the payment of policies by direct debit and without instalments reduces the probability of cancellations.Research limitations/implicationsThe main limitation of this study is the restriction on data access. Insurance companies are significantly resistant to sharing their customer data – including with academic researchers – even in an anonymised form.Practical implicationsThe paper highlights internal and external practices of insurance companies that should be reformulated to significantly improve their performance regarding product cancellation, related to customer information management, mistrust behaviours related to stakeholders and new value propositions that deepen the relationships with intermediaries.Originality/valueThis research developed a framework with which to identify the factors that are mainly associated with motor insurance cancellation and to predict its likelihood.

Implementing a Data Mining Solution for an Automobile Insurance Company

2011 ◽  
pp. 63-73 ◽  
Author(s):  
Ai Cheo Yeo ◽  
Kate A. Smith
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Competitive Environment ◽  
Automobile Insurance ◽  
Insurance Company ◽  
Data Mining Approach ◽  
Business Data

The insurance company in this case study operates in a highly competitive environment. In recent years it has explored data mining as a means of extracting valuable information from its huge databases in order to improve decision making and capitalise on the investment in business data. This case study describes an investigation into the benefits of data mining for an anonymous Australian automobile insurance company.1 Although the investigation was able to demonstrate quantitative benefits of adopting a data mining approach, there are many practical issues that need to be resolved before the data mining approach can be implemented.

Implementing a Data Mining Solution for an Automobile Insurance Company

2006 ◽  
pp. 189-201
Author(s):  
Ai Cheo Yeo ◽  
Kate A. Smith
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Competitive Environment ◽  
Automobile Insurance ◽  
Insurance Company ◽  
Data Mining Approach ◽  
Business Data

The insurance company in this case study operates in a highly competitive environment. In recent years it has explored data mining as a means of extracting valuable information from its huge databases in order to improve decision making and capitalise on the investment in business data. This case study describes an investigation into the benefits of data mining for an anonymous Australian automobile insurance company.1 Although the investigation was able to demonstrate quantitative benefits of adopting a data mining approach, there are many practical issues that need to be resolved before the data mining approach can be implemented.

scholarly journals Going digital: case study of an Italian insurance company

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Galena Pisoni
Keyword(s):  
Value Chain ◽  
Design Thinking ◽  
New Technologies ◽  
Lessons Learned ◽  
Insurance Companies ◽  
Insurance Company ◽  
Content Type ◽  
Cyber Insurance ◽  
Insurance Policies ◽  
Cyber Risk

Purpose This paper aims to present the case of an Italian SME in the domain of insurance and how it approached its own digital transformation. Together with the founders of the SME, the author investigated the digital trends the company should adopt and identified where to intervene in the value chain of the company with new technologies available in the market. The research was focused on the following three sub-domains: a strategy for adoption of innovative digital solutions to improve the everyday operations of the company, platform connecting the company with the customers and analysis of cyber insurance policies to include in the portfolio of the company. Design/methodology/approach For the part on strategy for adoption of innovative digital solutions, the author performed literature review; for the part in which the study ideates new solution to better connect the company with the customers, the author relied on design thinking, creative facilitation and prototyping; and for the part on cyber insurance policies to include the portfolio, the author relied on data available from other insurance companies the SME collaborates with. Findings This paper presented the analysis on how an insurance SME can embrace digital innovation (via internal innovation, buying from startups, partnering with startups or investing in startups), how an SME can do internal innovation and come up with a simple tool to bring closer the insurers and their customers and types of new cyber risk policies to include in the portfolio to respond to the growing demand for cyber risk insurance. This paper provides useful insights and lessons learned from companies of similar size in the domain of insurance and discusses future extensions of inquiry. Originality/value Big insurance companies and incumbent for their digitization efforts rely on the freshly created InsurTechs wave of companies. In this paper, the author analyzes what small- and medium-sized insurance enterprises can do in this respect and showcases the approach an Italian SME took in this direction.

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