The business intelligence in the 21. century

Bábel Ottó (2016) The business intelligence in the 21. century. Külkereskedelmi Kar.

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Absztrakt (kivonat)

First of all I would like to rationalize that I decide at the topic of business intelligence on the insurance market, because I was being trained at the Generali Insurance Company. It was the first time when I worked with professionals. I truly enjoyed my task which mostly meant providing assistance for key account management, in particular on small and medium enterprises (SMEs) segments. I have always felt an allegiance for this segment, because the SMEs have unique characteristics at very basic levels of economy. The second reason, why I choose this topic is, because I am interested in the modern society in which we are. It operates as a very complex organism as a total—built up from markedly distinct areas, such as social sciences: political sector, cultural sector and economy. Economy is also a complex system where the financial sector is clearly distinguished. The financial world has not been developing in a way that it could work without the development of other social subsystems. The opposite meaning of the statement is also true. One of the most important groups among the components of the financial system is made up of information. The types of information or data are produced by accounting, financial statistics, from different market prices, exchange rates as well as official and unofficial transactions. Information is a starting point and an indicator of the decisions of the players that influences their expectations. There is very important role of stakeholders earned information in business life. Today companies have grown up in an era of massively abundant data. Digital computers have made information readable for sixty-five years. The Internet has made it accessible. Then the first search engine crawlers have made it a single database for fifteen years. Thirdly, even it is not undoubtedly negligible point that the position of Generali Insurance Co. Ltd. has high impacts on the insurance market. It was established in Triest on the day of Christmas in 1831, and at that time it was one of the most important commercial centers located in Central Europe. It soon appeared in most of the European cities from Marseille to St. Petersburg, and continued the expansion outside the continent. Today the GC&C is one of the biggest insurance service providers in Europe, employing 77 thousand employees worldwide, and it serves 65 million customers in more than 60 countries. The Generali Group is a market leader in Western Europe and also an important market player in Asia or Eastern Europe. The service provider of the Group is the Generali CEE Holding which the region’s leader in services operates in 10 countries in Central East Europe. Finally, there are many science fiction movies predict that enterprises will take control of the world in the future. There are some possible outcomes that are not so optimistic. Enterprises may not ruin our society. Instead of exploiting the consumers, they would use their power for creating opportunities in terms of jobs, protection, and renewable energies. Let us suppose for a moment that people are planning the future and they have the right philosophy of mind. Serious technologists and futurologists predict that enormous amounts of computing power will be available in the future. It could be the case that companies are only capable of financing the most high-tech power to predict future economic growth. Furthermore, they will have opportunities to invest more in information technology. In the vast majority of the situations, technology companies were always enforced by governments’ support to improve the economy. It is then possible to argue that if this were the case, we would be rational to think that we are likely to live in a future that is ruled by these companies. This basic idea is taking the following into account: the insurance companies may maintain the equilibrium between the participants of statutory institutions and enterprises. Economic studies have perceptions for analysing the flow of information with theoretical costs models from information gathering. The model of the perfect market assumes that market participants are equally well-informed and receive information needed to make decisions with negligible costs. Thus, the participants are capable of making rational decisions. However the economic players' chance to access information is not the same in real life; an asymmetry of information exists in most of the situations. This Graduation research is intended to provide an overview of current thinking about big data. Explanations of big data tend to focus much more on its underlying determinants. That is, they are much more focused on the untapped possibilities in client services and risk mitigation. As an example, data mining in a few insurance companies are successfully determined in their marketing strategy. A key feature of this research is that explanations are separated into two main branches. These are: • Big data • Business Intelligence Big data can potentially generate more accurate insights for companies then traditionally craft hypothesis and research strategies. In other words, speculations and hypothesis are not needed in the future, simply let the machines lead us. (Anderson, 2008) For example, companies like Google, Facebook, LinkedIn have been able to collect extraordinary information about of their platforms users. For instance, no semantic or casual analytics is required to translate unknown language into the mother languages of the users. “That's why Google can translate languages without actually "knowing" them (given equal corpus data, Google can translate Klingon into Farsi as easily as it can translate French into German)” (Anderson, The end of theory, 2008) Business Intelligence has several other names like Competitive Intelligence, Reporting and Analysis, Business Analytics, or decision support. However the matter is more important than its name. Business Intelligence allows people inside of an organization to access the data, interact and make analysis to the management about development, performance, operations and opportunities. The research stars with theoretical explanations of insurance industry, in particular, the theory of information economics. The information is distinct from data or knowledge which is extremely important to make clean. The information is the most important commodity for any kind of business. The insurance is a very good service for companies. The benefit of insurance is given further innovations in methodology and findings of those companies which have sought to avoid risks. I would like to collect all the related theories of the issue that I can. As we move on page…to explanations of big data and on page….review the business intelligence and on page…the insurance industry for further conclusions. Particular emphasis is given to data mining and customer relation management as the most widely used methodologies of the Hungarian insurance companies. Finally, I will demonstrate the application of big data to avoid risk in Hungarian insurance market. As it turns out, customer analytics was developing in different ways in the beginning of the 21st century. Hungarian banks and insurance companies invested intensively in their own CRM systems, which strongly relied on the results of data mining. After a promising start in the insurance sector, no signs of data mining analysis could be traced in the second half of the first decade of the 2000s. I will reflect on what reasons caused the different ways of data mining development in the sector as final conclusion and try to mention further recommendations.By listing the possibilities of data mining we have described the situation of the insurance industry. In this section, I summarize the highlights as conclusion and identify the issues to be resolved. The market revenues have become stagnant as customer retention has been gaining more importance than forecasting model development support in the data mining system. The old logistic regression and decision tree models have turned out to be appropriate. The customer retention strategy to which the insurance market pays attention should not only include contracts which are the most vulnerable, but also the value of certain clients as well. A new good starting point is needed for the actuarial models, but these should be supplemented by unique, characteristic parameters of clients activities which are reliable output for data mining analyses. The effectiveness of the sales can be improved by targeted selling. Past customer behavior analysis and the content of each campaign project can supported by the conversion rates of distribution channels, which could provide a basis for analysis of data mining. Examining past fraud patterns when exploring the clients’ claims can serve as prevention of reoccurrence. All the insurable possibilities based on the usage of existing databases. The range of available data about customers needs to be changed and better integrated with the compliance and underwriting systems. If there is more available data, then the insurer can more accurately determine insurance premiums and risks on the level of the customer contract analysis. In case even more detailed data are available, then the management can be identified on the top, which means further differentiated premiums and customer-based (friendly) fees. This action leads to the rethinking of customer and regulatory decisions. It is also a good example to show that there is significantly untapped potential in customer-level analysis. A short review of the above: why could the expectations of insurance sector remain unaccomplished in the development of data mining? • The market was faced with the importance of the sector’s stock protection problem too late. • The acquisition promised greater results than focusing on customer retention and servicing strategy on the rapidly growing insurance market. • The data mining methodology has been (just) addressed for the analysis of customers. It used to be difficult for the industry to find the harmony between sales of unique products and risk communities. • A significant proportion of „two-product sales” specifications stood in the way of real cross-selling opportunities. • The industry actually applies the wrong data results in production difficulties. Any of the above arguments can be sufficient for the management at rational decision making that There are not significant resources allocated for data mining . If a change occurs in any of the above conditions, data mining can provide support in many points of implementation to achieve business goals for the industry.

Magyar cím

Üzleti intelligencia a 21. században

Angol cím

The business intelligence in the 21. century

Intézmény

Budapesti Gazdasági Főiskola

Kar

Külkereskedelmi Kar

Tanszék

Világgazdaság és Nemzetközi Kereskedelem Intézeti Tanszék

Tudományterület/tudományág

NEM RÉSZLETEZETT

Szak

Nemzetközi Gazdálkodás (angol nyelvű)

Mű típusa:	diplomadolgozat (NEM RÉSZLETEZETT)
Kulcsszavak:	Üzleti intelligencia, 21. század , Biztosítás, Adatbányászat, Big Data , Esettanulmány
Felhasználói azonosító szám (ID):	Turányi Nóra
Rekord készítés dátuma:	2016. Máj. 09. 13:30
Utolsó módosítás:	2017. Már. 28. 14:45

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