Discussion on Knowledge Management in Insurance Industry

KnowledgeManagement inthe InsuranceIndustry andtheroleof BusinessPerformanceManagementin an organizationspotential business strategy.

Abstract

Purpose: This report describes the information about the use of knowledge management and data mining techniques in the Insurance industry to manage the huge amount of data because the insurance industry holds a huge amount of customer data. To establish and expand business every business organization needs a business strategy. This report describes the business strategy of the Tesco Group and Role of Business performance management to meet that strategic objective.

Method: The research methodology used for writing this report is a literature review. Data is collected from the secondary resources which are available at the Wintec library, science direct, google scholar and other online websites.

Findings: The first finding is related to the use of knowledge management and its technique used in the insurance industry. Data mining is the technique of knowledge management which is used to manage and extract meaningful data from the huge datasets. The second finding is related to the potential business strategy used by the Tesco group for business growth and the role of business performance management in achieving that strategic objective.

Conclusion: Insurance industry is customer based industry. Thus it is very rich in the database. Data mining is an appropriate technique for knowledge management in the insurance industry. Business strategy is much needed by the organization to establish good business. Tesco uses Corporate steering wheel as an approach of Business performance management to measure and manage the performance of its business across the stores.

Recommendation: knowledge management and its technique data mining is the appropriate way to manage and handle the data in a more useful manner but it is not a stand-alone tool. For its successful application, a skilled user and specialist are required to provide accurate data and interpreting the output for decision making.Introduction

Table of contents

1. Introduction.6

2. Findings of the use of Knowledge management in the insurance industry…6

2.1. What are the parts of the topic?….6

2.2. How are the parts connected?..6

2.2.1.Knowledge Management.7

2.2.2. Data Mining..7

2.2.3 Use of Data Mining in the Insurance Industry in general…7

2.3 What are the implications of data mining for profitability.8

2.4.What are the different angles of perspectives?8

2.5 Possible consequences That can be faced by the insurance industry if the data Mining technique is not used ..8

3. Finding of Potential business strategy in TESCO and role Business performance management in meeting that strategy..9

3.1.Business Performance Management..9

3.2.TESCO and its business strategy9

3.3.Role of business performance management in meeting the strategic objective9

4. Conclusion..10

5. Recommendations..10

6. Appendix.12

7. References .13

1. Introduction

This report is about how knowledge management techniques are used in the insurance industry.The purpose of this report is to investigatethe use of knowledge management in insurance organization and potential business strategies and the role of business performance management to meet with the existing strategy.

The significance of this reportis that in the insurance industry it is very important to determine risks, set price, improve customer service and control costs. The profitability of the companies crucially depends upon the capability to assess these factors efficiently. So, Knowledge management provides some methods and techniques to determine the factors stated above.

The research methodology used for writing this report is a literature review. Data is collected from the secondary resources which are available at the Wintec library, science direct, google scholar and other online websites. While finding the information for this report the only difficulty occurs is the lack of enough information material related to some topics.

This report organizes the two parts.Firstpart analyses and evaluate the use of Knowledge management in the insurance industry and other part analyses the role of business performance management in meeting the objective of potential business strategy.

2. Findings of the use of Knowledge management in the insurance industry.

2.1. What are the parts of the topic?

• Knowledge management
• Data Mining
• Role of Data mining in the insurance industry

2.2. How are the parts connected?

2.2.1.Knowledge Management: knowledge management is the process of capturing the information and distributing it to wherever it can produce the biggest payoff

Knowledge management is the identification and management of information and knowledge residing in an organizations expertise (Sharda, Delen, & Turban, 2014).

According to (Silwattananusarn & Tuamsuk, 2012) In the present era of information, knowledge has become an important resource of any organization that gives rise to the concept of Knowledge Management (KM). According to them, data mining is an important part of knowledge management as it is a process of extracting knowledge from large datasets. According to (Silwattananusarn & Tuamsuk, 2012) data mining is a tool of knowledge management that focuses on capture and creation of knowledge

2.2.2 Data mining:

Data mining the main part of the findings is how data mining is useful in the insurance sector for knowledge management. According to (Nguyen, 2018), Data mining is the process of analyzing data from different angles of perspective and extracting it into valuable information. According to (Umamaheswari & Janakiraman, 2014) from a large amount of data when information is extracted it is known as data mining. Data mining is also known as Knowledge discovery in database (KDD). But according to (Nguyen, 2018) Some people consider data mining as entire knowledge discovery from data. Data mining is an IT-based technique thus use of computers or devices is required for the use of data mining.

2.2.3. Use of Data Mining in the Insurance Industry in general

All these topics are connected to eachother because it is the study of how knowledge management and its technique data mining is helpful in the insurance industry. The insurance industry is a customer based industry. Insurance industry carries a large amount of data of its customers. It is very important that the customer data should be handled effectively that is possible with the help of data mining. In other words, data mining is a technique of knowledge management that helps the insurance industry to manage the customers data effectively (Umamaheswari & Janakiraman, 2014). According to (Umamaheswari & Janakiraman, 2014) various techniques such as classification, clustering, association, and summarization are used for Data mining in the insurance industry for development.

According to (Sharda, Delen, & Turban, 2014) Data mining is used in the insurance industry to forecast the claim amounts for better business planning. Data mining is also used to determine the optimal rate plans in the insurance industry by analyzing claims and customer data, they said. Data mining also helps in predicting profitable customers to the companies who are more likely to buy new policies in the future. According to (Umamaheswari & Janakiraman, 2014) insurance industry use cluster analysis for acquiring new customers and retaining existing who are profitable to the companies. Data mining is also used to detect fraudulent activities for preventing incorrect claim payments (Sharda, Delen, & Turban, 2014).

2.3. What are the implications of data mining for profitability

According to (Umamaheswari & Janakiraman, 2014) in order to compete successfully in market, the insurance companies use data mining to investigating whether people are going to buy the policy and policy design. Data mining also can be used in the analysis of the trend. Trend analysis is a study of change in social patterns, Technologies, consumer behavior, etc. Data mining is used to identify new customers or to reduced portfolio risk in the insurance industry (Umamaheswari & Janakiraman, 2014). They said that data mining is also used to detect policies based on fraudulent information. Fraudulent activities lead to excess or incorrect payments of claims that negatively affect the profitability of the companies. Besides this data mining is also used risk analysis. Customer level analysis, acquiring new customers, developing new product lines etc.

2.4. What are the different angles of perspectives?

On the use of data mining in the insurance industry, there are different angles of perspectives. As discussed above some authors has a view that data mining is a beneficial tool for the insurance industry as it helps in analysis future market trends, detecting frauds, customer segmentation. Risk analysis etc. on the other hand According to (Dereak, 2011) data mining cannot be considered as a stand-alone tool. For its successful application, a skilled user and specialist are required to supply correct data and obtained an objective conclusion. If the data supplied is not correct then the expected outcome cannot be achieved and the forecast will not be profitable. If the incorrect information is used for decision making then it will result in serious consequences. According to (Umamaheswari & Janakiraman, 2014) data mining faces a lot of difficulties while handling the huge amount of data with high complexity, noisy data, corrupt values or data with missing attributes.

2.5. Possible consequences That can be faced by the insurance industry if the data Mining technique is not used

• Insurance industry collects and stores the vast amount of customer data. It will be difficult for companies to extract valuable information from the huge amount of data.
• Insurers will face difficulties in identifying the risk factors that will affect the policy price, profits, etc.
• Difficulty in fraud detection
• Unable to do customer level analysis, trend analysis.
• Analysis of claims and customer policies will be more complex.

3. Finding of Potential business strategy in TESCO and role Business performance management in meeting that strategy

3.1. Business Performance Management

According to (Sharda, Delen, & Turban, 2014) Business Performance Management refers to the set of business processes, methods, and technologies used to increase the manage the business performance. According to (Frolick & Ariyachandra, 2006)it is a process of addressing financial and operational activities of the companies with the approach to the and analytic processes.

3.2. TESCO and its business strategy

Tesco is a British leading groceries and merchandise retailers. Tesco is working at a large scale and its organizational structure was complex. According to (Riley, 2013) Tesco has followed its growth strategy. According to the strategy TESCO decided to jump into the family restaurant markets for the purpose of growth. Tesco acquired a group of restaurants known as Giraffe. The Tescos strategy was to create a space in some of its large stores for creating a family-friendly environment where people can meet, eat drink while shopping. Tescos main focus was to create the shopping stores a leisure destination for the customers and increasing the sales and growth record from a different but related market (Riley, 2013).

3.3. Role of business performance management in meeting the strategic objective.

According to (Marr, n.d) in order to achieve the strategy of growth the company needs a clear direction, map, and compass. For describing the strategic objective of the company and provide a map the management decided to develop a performance management framework and name it as corporate steer wheel. The management also developed some Key performance indicators that act as a compass that will help the organistaion that whether it was on track or not.

Source (Marr, n.d)Fig 1. Corporate Steering wheel

This is the corporate steering wheel with 20 corporate objectives with 5 different perspectives.

According to (Marr, n.d) Tesco uses this steering wheel to measure the performance across its stores. Sir Terry Leahy said Objectives across these different perspective allows the Tescos to become more balanced toward performance. This simple image of steering wheel communicate the companies strategy and believe to everyone very easily. By focusing only on the data that provide insight into the Tesco instead of measuring everything will enable the company to manage what really matters.

4. Conclusion

From the study of this report, it is concluded that the insurance industry holds a large amount of customer data. It is very difficult to manage the data with traditional ways. Knowledge management and its techniques of data mining help the insurer to manage the customers information more efficiently. Besides this data mining helps in improving the profitability of the companies by trend analysis and fraud detection, customer level investigation, acquiring customers, risk analysis. From the second part of the findings, it is concluded that Tesco a well known retail group develop a business strategy of growth. The organization entered into the family restaurant market and acquire Girrafe restaurant to make its shopping stores a leisure destination for the people where they can meet eat and drink while shopping that will result in the increase in the sales and income record of the company from the different but related market. The company uses Corporate steering wheel as an approach of Business Performance Management that helps the company to measure and manage the performance across the stores in a simplified manner by not measuring everything and focussing only on that data that really matters.

5. Recommendations

In my opinion, knowledge management and its technique data mining is the appropriate way to manage and handle the data in a more useful manner but it is not a stand-alone tool. For its successful application, a skilled user and specialist are required to provide accurate data and interpreting the output for decision making. If the data provided is incorrect then the outcome will not be as per the expectations and will not be profitable for the companies.

6. Appendix

• “>https://www.scribd.com/document/290196988/Case-Study-TESCO

7. References

• Dereak. (2011, october 11).Data Mining Benefits, Limitations, and Challenges. Retrieved from Dereak Newsletter: https://dereak.wordpress.com/2011/10/11/data-mining-benefits-limitations-and-challenges/
• Frolick, M., & Ariyachandra, T. (2006). Business Performance Management: One Truth.Information Systems Management, 41-48.
• Marr, B. (n.d).Tesco: Creating a Corporate Steering Wheel. Retrieved from Bernard Marr & Co.: https://www.bernardmarr.com/default.asp?contentID=1052
• Nguyen, N. (2018).Data Mining in Knowledge Management Processes: Developing an Implementing Framework.n.d: UMEA University.
• Riley, J. (2013, 3 15).Growth strategy Tescos Related-diversification into Family Restaurants. Retrieved from tutor2u: https://www.tutor2u.net/business/blog/growth-strategy-tescos-related-diversification-into-family-restaurants
• Sharda, R., Delen, D., & Turban, E. (2014).Business Intelligence and Analytics: Systems for Decision Support.Boston: Pearson education limited.
• Silwattananusarn, T., & Tuamsuk, K. (2012). Data Mining and Its Applications for Knowledge Management : A Literature Review from 2007 to 2012.International Journal of Data Mining & Knowledge Management Process, 13-24.
• TESCO. (2018, january 22).Tesco to . Retrieved from TESCO PLC: https://www.tescoplc.com/news/news-releases/2018/tesco-to-simplify-operational-structures/
• Umamaheswari, K., & Janakiraman, S. (2014). Role of Data mining in Insurance Industry.An international journal of advanced computer technology, 961-966.