CC BY
2121. Dunning, J.H. Location and the Multinational Enterprise: a Neglected Factor? // Journal of International Business Studies, vol. 29, 1998. P. 45 - 66.
22. Blonigen B.A. FDI in Space: Spatial Autoregressive Relationships in Foreign Direct Investment // European Economic Review, vol. 51, 2007. P. 1303 - 1325.
23. Forum for kaerlig-opdragelse.com. - kaerlig-opdragelse-forum.com/Forum/
24. Foreign Direct Investment Theories: An Overview of the Main FDI Theories. - http://ejist.ro/files/pdf/357.pdf
25. Rakhmatullayeva D. Zh. Social effects of direct foreign investments in regions of Kazakhstan. Dissertation of the doctor of PhD, Almaty, 2015. - 180 p.
26. Chowdhury A., Mavrotas G. FD land Growth: What Causes What?. - United States: Nations University, 2006.
27. Trapeznikov P. The methodological principles of creation of the integrated theory of direct foreign investments as forms of foreign business activity of firm // The Belarusian magazine of international law and the international relations, №4, 1999.
ВИКОРИСТАННЯ BIG DATA У СТРАХОВ1Й Д1ЯЛЬНОСТ1
Ерастов B.I.
астрант
Кшвський нацюнальний yHieepcumem iMeHi Тараса Шевченка,
м. Кшв, Украша
BIG DATA UTILIZATION IN INSURANCE INDUSTRY Erastov V., PhD student, Taras Shevchenko Kiev National University, Kiev, Ukraine АНОТАЦ1Я
У статп розглядаються основш пвдходи до використання Великих Даних у страховш дшльностг Розкрито основш пози-тивш аспекти впровадження BIG DATA та специфiчнi обмеження, накладет страховою галуззю. ABSTRACT
The paper examines the approaches to Big Data utilization in insurance business. It outlines the main positive aspects of Big Data implementation and the specific restrictions imposed by insurance industry.
Ключовi слова: Велиш Дат, Big Data, страховий ринок, страховий маркетинг, поведшковий аналiз. Keywords: Big Data, insurance market, insurance marketing, behavioral analysis.
Insurance industry is based on risk principle. Clients are choosing insurance policies concerning on their personal assessment of loss probability and amount, insurance company at the same time are using assessment of probable refund as the base.
Both parties of insurance interaction will get significant benefits in case of increase accuracy of probable risk assessment. This opportunity of insurance industry is gained by Big Data implementation. Big Data is a novel trend, which is concerned to increased amount of digital data that are generated and stored for using during different data mining algorithm that could bring some insights of different phenomenon and behavioral patterns. Predictive and statistical modeling could be used to find probable future events by analyzing and assessing different indicators that characterize same events from the past. Such models could highlight only events, which are quite probable basing on correlation and interrelationships of variables, chosen after analysis of previous periods. These models are the main instrument of the data scientist and insurance is the industry that will gain maximum benefits of Big Data implementation.
The significant part of researches about insurance market and it's tendencies were held by such Ukrainian researches as T. Motashko[1], N. Piykaziuk[3], developing ideas of existing on Ukrainian market examples of novel technologies and systems, also they are looking for examples of similar systems in foreign markets for comparison.
The overwhelming majority of published researches in this field belongs to foreign experts, who are a part of different research organizations[4,5]. Their achievements are aimed to understand the basic principles of novel system functioning in different countries.
The aim of this research is to highlight the main achievements of insurance industry in Big Data implementation, which became
possible due to opportunities in storing and analyzing different information.
The research object is theoretical and methodological approaches to Big Data usage in insurance.
That main Big Data using concern is premium calculation. Efficiency is the key factor of insurance industry. Insurers should set premium rate at the level, which will be able to cover risks, provide profit and will be suitable for clients' budget, because in the other case they will go to the other insurance company.
The great example could be motor insurance. Clients, especially younger ones, often complain on high prices, so the industry is becoming a highly competitive market that is characterized by different price comparison services usage. In such circumstances, insurance industry is pushed to ignore accurate risk assessment for each client and to form prices in concern of competition and profitability.
Many insurance companies provide telemetry based products, which can provide personified and accurate information about driving by creating clients' behavioral profiles. Utilization of predictive models could provide information about probability of insurance case that involve certain client or even vehicle theft by comparing current behavioral profile and existing behavioral database created basing on behavioral information of thousands of insured clients. Thus information could be collected and sent by a specialized device, that is inbuilt or plagued in customers car or increasingly by drivers smartphone application.
«Progressive» insurance company could be an example of Big Data utilization for quality of service improvement. They created «Business Innovation Garage» where technicians and insurance experts are offering some innovations and opportunities for insurance industry and assessing their efficiency and appropriateness. One of such innovative approach was 3D modeling implementation in car accidents investigation. Photos and some other data are uploading to specialized
soft- and hardware environment that are used to create three dimensional computer models for deeply analyzing current state and impairment of insured vehicle.
The same situation could be observed on life and health insurance market due to increasing spread of wearables such as Apple Watch and Fitbit activity tracker, that could be used to monitor individuals habits and to provide current assessment of activity level and even lifestyle. According to research, held by «Accenture», nearly one-third insurers propose insurance products that are involving data collection by different wearable devises. «John Hancock» insurance organization could be the remarkable example of such cooperation. They are providing insurance premium discounts and free Fitbit tracker for each engaged customer. Clients will be able to get progressive discount rate by increasing their healthy activities and decreasing negative and unhealthy lifestyle aspects. Brooks Tingle companies vice-president of insurance marketing and strategy concludes, «Clients will not contradict to share some personified information in case and circumstances where transparent mechanism and ways of utilization of such information appears. Also they should get a real value benefits for it»[4].
Both cases have some ethical and privacy problems. Automotive telemetry provides an opportunity to monitor every customer's route assessing the possible danger of every its part, basing on statistical information about road accidents that took place on this road or on similar ones. This should not mean that every journey of every client would be logged and stored somewhere in insurance company, although the current lack of transparency in this issue causes situations when insurers act so. In respect to improve this issue, collected data could be unpersonified, geolocation data will be sent in encrypted form directly to analytical systems of insurer that will not corrupt privacy of insured clients. Insurance companies does not need to examine accurate data about every road segment that is used for client's journey because this will be unstructured set of information that will not be able to provide any useful insights for insurance data scientists. The most efficient way of such data utilization is collecting abstract information, for example, driver is using grade E roads for 32% of his journey, grade A for 51% and grade C for 15%. In such case unstructured data is be-coming structured that is able to provide some meaningful insights to analyze correlation and relationship between variables.
Ethical concerns and data privacy problems are even sharpened in health insurance. Most of respondents are clear in concern that premiums should be increased for people who choose unhealthy lifestyle, such as smoking, drinking alcohol or just never exercising as an efficient method of risk distribution and covering. On the other hand current scientific researches in the field of human genome has shown that main chooser in human life is inborn genetic profile. In the era of deep human genome profiling and mapping the assumption that lifestyle is determining diseases and health problems is opposed to assumption that any health deviation is caused by some markers in genome. In such circumstances, insurers are not only able but even pushed to increase premiums for clients whose genetic markers demonstrate higher risk in some diseases, this will highly increase efficiency of insurance activity but would it be fair? Historical data can show us that health care for individuals who had inborn diseases or a genetically caused deviation from common determination of "health" was signifi-cantly more expensive than for "normal" people. In the time of Big Data utilization for genetic and behavioral profiling appears an
opportunity to create quite accurate empirical model that will highlight reasons of any disease, was it caused by improper lifestyle or by genetic aspects that could not be influenced by individual. Current problem and ways of its improvement should be regulated via legislation because market forces would not be able to resolve it in a proper way.
In addition to setting fairer and efficient insurance rates, Big Data established itself as a useful tool to overcome insurance fraud. According to FBI data insurance fraud caused increasing of average American families insurance premium for nearly 400-700$ per year[6].
Insurance companies are using Big Data to sift different fraud scenario by utilizing profiling and predictive models. Some variables of each insurance request are compared with the same indicator from the previous settlement processes that was claimed as fraudulent. In a case of specific markers appear, the fraud is highly probable and the system readdresses this insurance claim for further deep investigation. Such markers could be even behavioral pattern of insured, who settled the claim because computer system could reveal some behavioral peculiarities of cheater that could be not revealed by human eye. Furthermore, the data about client's milieu could be deeply examined by collecting information from open sources, such as social networks, credit history and other demographical information repositories. The last main aspect of investigation is counterparties analysis, where all contractors that are involved into insurance claim, such as repairing shops, vendors etc. are examined to compare their behavioral profile with known frauds to exclude their fraudulent activities, which are held with or without customers participation.
The third field of Big Data implication in insurance industry is marketing. Deep analysis of all available information about certain client could be used for accurate understanding of his needs for increasing efficiency of acquisition activity by supplying products and services that will better fit customers' preferences. Increased level of understanding could be gained by application of behavioral and psychological methods of clients' feedback analysis even in social media. Different algorithms are used for digging some useful insights about dislikes and preferences from unstructured data such as phone calls, e-mail, opened social media that could form individual marketing strategy for every separate customer. Client's behavior while being online is also remarkable for highlighting customer's interests and demands. Justin Cruse "American Family Insurance" strategic data analysis vice president states that insurers are aimed on clients and marginal profits of provided services, but the aim should be infor-mation and technologies utilization for positive feedback monitoring while monitoring implementation of different new services. This insurance institute had licensed specific application named "Talk & Learn" created to deepen understanding level between customer and service employee for predicting some products and services grows and development.
Marketing departments of many insurance organizations utilize Big Data to find out clients who are more likely to cancel insurance agreement or just change the insurer. The algorithm is same to underwriting and fraud detection, information about behavioral aspects of certain client is matched against similar records that characterize insured that had left insurance company. Marking clients by amount of service line application criteria could be an appropriate example for the tendency of trying to change customer's opinion to more positive one. Such
steps could be some additional benefits, price reduction or just more attentive interaction while maintaining client's application.
Big Data will undoubtedly become a great tool to bring positive shift of insurance industry that could be characterized by increased quality of service, pricing accuracy and reduction of costs in fraudulent cases. In addition, insurance industry has a set of specific challenges of Big Data implementation that are concerned with ethical and privacy problems. This will become a remarkable trend of insurance industry and certain technology will widen mostly in health insurance sector. Insurance industry will show all the carried opportunities of Big Data and no carried threats in case of proper utilization for providing higher quality and efficient services, client services and marketing.
References
1. Pikus, R., Prykazyuk, N., Lobova, O., 2015. Mizhdystsyplinarnyi slovnyk zi strakhuvannia ta ryzyk-menedzhmentu. Kyiv: Logos
2. Prykazyuk, N., Motashko, T., 2015. New vectors of the motor insurance development in Ukraine. Bulletin of Taras Shevchenko National University of Kyiv. Economics. №3 (168)
3. Prykazyuk, N., Motashko, T., 2014. Role of internet in insurance services realizations Bulletin of Taras Shevchenko National University of Kyiv. Economics 156.
4. A.T. Kearney Analysis Report, 2014 [Electronic resource]. Access mode: https://www.atkearney.com/ documents/10192/4572735/ReadyforTakeoff-FDICI2014.pdf/
5. Capgemini Financial Services Analysis, 2015 [Electronic resource]. Access mode: https://www.capgemini. com/insurance/archive/2015/
6. Insurance Fraud [Electronic resource]. Access mode: https://www.fbi.gov/stats-services/publications/insurance-fraud
PERSPECTIVES ON THE CONCORD-BASED MANAGEMENT IN THE AGE OF INNOVATION
Kovalevskaya E.A.
PJSC "Blitz-Inform" Kiev branch Printing house "Blitz-Print"
Chief of the Finance Department
ABSTRACT
In this paper, the author investigates the influence of the interests of the participants of economic relations on the innovative readiness of ukrainian machine-building holding companies. She briefly analyzes the socio-economic state of the machine-building industry of Ukraine, and then compares strategic plans of the holding companies VAT "AvtoKraz ", VAT "Kh "Luhanskteplovoz", DAKHK "Chornomorskyi sudnobudivnyi zavod". The author finds the facts of neglect of workers' contribution in financial results and presence of a social tension in the holding companies. She makes accents on the necessity of raising innovative readiness of the holding companies. To overcome mentioned difficulties the author represents the concord-based management.
Keywords: innovative readiness; participant's interest; the concord-based management; the concord budgeting; impact factor; the concord coefficient.
Introduction
Now innovation has become a new imperative of management in the world. Integration processes with the CIS and highly developed countries stimulate using of different methods and tools of management to raise innovative activity of companies. But the difficult socio-economic state of Ukrainian companies, increased by the physical and moral aging of industrial capacities, reduces the competitiveness of national products, needs searching for the strategic directions of its improvement. This question becomes keen while studying a large business and acquires a special actuality for machine-building companies, especially, holding companies.
In scientific national and foreign literature strategic management directs in a greater degree on strengthening the interests of business owners, but the interests of the other participants of economic relations - customers, suppliers, production workers, administrations of corporate enterprises and a managing company, transporters, creditors and et cetera -haven't been studied enough. Namely, concord of marked above participants we might examine in the strategic development of a holding company, because their interests directly or indirectly influence on the financial result. This question acquires the special actuality within the research of a holding structure in the context of contradiction interests of corporate (daughter, subsidiary) enterprises and a managing (mother) company according allocation resources and the profit.
The results of studying theory and practice of strategic management have shown the attempts of concord of participants' interests (Momot, T.V et al., 2010; Gaponenko, A.L. et al., 2010; Nusinov, V.Ya. et al., 2006; Pavluk, A.P. et al., 2012), but no accents on creating the concord-based management as the component of a managerial system. Too many networks have studied theory and practical implementation of concord (Makashina, O.V; Huei, T.H.; Blagov, Yu.E.; Post, J.E. et al.). As a result, many conceptions have appeared one of which is the conception of interested participants by J. E. Post, L. E. Preston and S. Sachs. Nevertheless, the problem of concord of participants' interests exists. Up to now there are no effective tools of managing this process. Every company tries to do this in their own way, sometimes intuitively, do not understanding to the end the impact of interests on raising innovative activity. This is caused the objective of the study - improving on strategic management on the core of concord of participants' interests towards raising innovative activity of a holding company.
Results and discussion
Machine-building holding companies of Ukraine are in the conditions of variable environment which has been caused by the unstable economic situa-tion in the industry. During the period from 2010 to 2015 the long absence of the stably positive dynamics of the indexes of machine-building production points to it [see figure 1]. The world financial crisis has become the principal cause of worsening financial and economic state of the industry, which conse-quences influences on companies
