壽險業作為經營風險的特許行業，承擔著穩定社會、資金的聚集和供給，以及促進經濟成長等重要功能。近年來由於低利率、匯率變動及國內外經濟環境日益惡化，保險業的本業利潤逐年降低，壽險業的經營面臨重大的挑戰。 保險公司倒閉在國外並不是新鮮事，根據統計，1978～1994年，不到20年間，全球共有648家保險公司破產。導致保險公司破產的原因很多，其中美國在2000～2001年破產的公司大部份是準備金不足；日本保險公司的破產原因則多為投資損失。此外，一些無法意料的天災及人禍之理賠，例如美國911恐怖事件以及卡翠那風災，使得各國保險公司對於企業風險管理更加重視。標準普爾信用評等公司於2006年起，正式將企業風險管理（ERM）制度列入保險業的信用評等項目，更加速保險公司執行企業風險管理的速度。目前國內保險業風險管理制度之實施，則仍屬於起步階段。 保險業屬於高度競爭及高度監理之行業，因此，如何在保持領先的競爭地位及提供優質的客戶服務的同時，亦能恪遵法令、落實內部控制制度及企業風險管理制度，進而創造出屬於自身的藍海策略，以期追求穩定持續成長的獲利，實為壽險業者必須重視的議題。 本研究探討目前保險業所面臨的風險及挑戰，分析金管會對保險公司重大裁罰之種類，並描述及探討個案公司實施內部控制、沙賓法案及企業風險管理制度之狀況與歷程，以及其可能遇到的瓶頸與障礙。本研究結果對於國內保險業實施內部控制、沙賓法案及企業風險管理的過程，可提供助益。 / Life insurance industry conducts business that requires approval from its regulatory authority. The industry provides an array of functions, such as managing risks, maintaining social stability, pooling and supplying funds, etc. In recent years, life insurance industry has faced with serious challenges due to its decreasing operating profit margin caused by low interest rates, volatility of foreign exchange rates, and deteriorating economic environment around the globe. The bankruptcy among insurance companies is nothing new over the world. There were as many as 648 companies going bankrupt during 1978 to 1994. The causes of the aforementioned bankruptcy were many. For example, Japan companies became insolvent mostly due to investment losses; those in the U.S. were caused by insufficient reserves. Huge insurance claims for unpredictable natural or man-made disasters, such as 9/11 terrorist attack, hurricane Katrina, and the subprime mortgage crisis in the U.S., have attracted most companies’ attention worldwide to “enterprise risk management (ERM)”. Hence, Standard & Poor’s started in 2006 to apply the ERM analysis to its credit rating for the insurance industry, and this measure propels insurance companies into the implementation of ERM. Nonetheless, Taiwanese insurance companies are just at the inception of ERM. Insurance industry must comply with stringent regulations while confronting keen competition. An insurance company must take a lead in competition, provide quality services to its customers, abide by regulations, and implement internal controls and ERM in an integrated fashion. In doing so, an insurance company can develop its own blue ocean strategy and maintain sustainable growth and profit. This research probes into the risks and challenges faced by Taiwan insurance industry in dealing with various risks and challenges, including the serious sanctions by the Financial Supervisory Commission. We present a case study of local life insurance company with respect to its process of integrating internal control systems, compliance to Sarbanes-Oxley Act and implementation of ERM. A number of deficiencies, bottlenecks and obstacles were identified and analyzed, followed by related suggestions. Our results provide insights and helpful suggestions to the integration of internal controls, Sarbanes-Oxley compliance, and ERM for Taiwan insurance industry.

兩岸在2001年底加入世界貿易組織（WTO），使得兩岸壽險市場受到經濟自由化及國際化的衝擊。因此，要如何提高自身的經營績效及競爭能力，便成為兩岸壽險公司最重要的目標。本研究以資料包絡分析法為基礎，並結合共同邊界（metafrontier）分析法，針對兩岸地區在2004年至2007年共59家壽險公司，進行經營效率與Malmquist生產力指數的實證研究。在生產力變動來源的拆解上，本文延伸Pastor and Lovell（2005）的固定規模報酬模型，利用變動規模報酬的生產邊界來衡量各公司的技術變動及技術差距比率變動，使生產力變動的來源上獲得更明確的意涵。最後本文利用Tobit迴歸模型，探討影響兩岸壽險公司經營效率的因素。 / After joining the WTO in December 2001, there is the advent of economic liberalization and internationalization on the life insurance market of Taiwan and Mainland China. Therefore, how to improve the operating performance and the industrial competitiveness in the present economic circumstance is the critical and important goal of the life insurance industry in Taiwan and Mainland China. This study applies data envelopment analysis with metafrontier model to measure the managerial efficiency and Malmquist productivity index of 59 firms of life insurance industry in Taiwan and Mainland China from 2004 to 2007. On decomposing the sources of productivity change, we extend Pastor and Lovell’ s CRS model (2005) to a VRS frontier benchmark to measure technical change and technical gap ratio change, which apparently provides us a more meaningful decomposition of productivity change. Finally, this study uses Tobit regression model to examine the factors which influence the managerial efficiency of the life insurance industry in Taiwan and Mainland China.

我國在金融監督管理委員會2009年5月14日宣告全面採用國際會計準則後，所有公開發行公司即受到全面性的衝擊;而在眾多的IFRS中，IAS 40投資性不動產會計準則，是目前國內會計準則所沒有的規定，且此號公報最特別的規定，是允許投資性不動產的續後評價，企業可以擁有選用公允價值法與歷史成本法的彈性，這樣的特殊規定觸發筆者想瞭解，企業未來適用此號公報，其續後評價方法之選擇及影響選擇原因的動機。 相對於歐美，由於我國缺乏具有長期收益性與安全性的資金投資管道，造成我國保險業長期偏好將可運用資金投資於實體不動產，故IAS 40對我國保險業影響重大，因此筆者以我國保險業為研究對象，以問卷、個案分析及訪談來探討保險業者對未來適用IAS 40之看法與期待。 經過研究分析，本研究發現60.61%的保險業在未來實施IAS 40後會繼續延用歷史成本模式，對影響選擇的因素方面，受訪者認為「對盈餘數字高低的影響」、「所得稅金額大小的考慮」、「對不同年度的損益造成波動的關係」等三項因素最為重要，另外，受訪者最認同新會計準則方法規定允許公司選用歷史成本法或公允價值法「會增加公司間財務資訊比較的複雜性」，且60.6%受訪公司傾向不同意提前適用此號公報。 / After the Financial Supervisory Commission Executive Yuan, R .O.C. declaring on May 14, 2009 that adopt International Accounting Standard in an all-round way in our country, all the public company were totally impacted promptly; Among the IFRSs, IAS 40 investment property is not exist at our present domestic accounting regulations and the most special of this regulation is allowing the enterprises can have elasticity of selecting the cost method or fair value method for measurement after recognition. The special treatments induce me to understand the drivers of the reason in its choice. As to America and Europe, because our country lacks have long-term rentability and security fund investment channel, cause the insurance of our country prefer to invest in real estate for a long time, so the implemental of IAS 40 will influence our insurance industry very much, so the study use questionnaires, case analysis and interview to researching the determinants of insurance industry choice to use the cost or fair value model to account for their real estate. Researched and analyzed, originally discover that 60.61% of the insurances will continue to use the cost method after implementing IAS 40 in the future, to influencing the factor chosen, interviewees think ”the amount of earnings”, “the tax amount” and “the volatility of income in different years” three factors are most important. In addition, interviewees admit that IAS 40 permits enterprises to choose cost method or fair value method will increase the complexity of comparisons among the companies’ report, and 60.6% interviewed disagree earlier application with this criterion.

Changes and challenges that have occurred in the past two decades have forced a radical shift in the basic foundations of how business is conducted. Internal, as well as external forces have forced organisations to constantly monitor their surrounding environment in order to create an awareness of opportunities and threats to allow them to survive in their competitive environment. Organisations need to gather all the information at their disposal, and turn the raw data into intelligence through a process of analysis and an exercise of human judgement. By utilising the potential offered by information systems in the process of generating intelligence and creating a corporate knowledge base to be used in strategic decision-making will lead to competitive advantage and constant innovation. Strategic Intelligence has information as its foundation. This research proposes that through its ability to absorb sources of information, the synergy of Business Intelligence, Competitive Intelligence, and Knowledge Management combined to form Strategic Intelligence, will allow organisations to incorporate all of their information and intellectual capital into a single database or system which will meet the intelligence requirements of management. The purpose of this study is to identify the current use of Strategic Intelligence in the Long-term Insurance Industry in the South African environment, and through the use of a survey questioned the benefits or problems experienced by executive management who have not yet implemented and used Strategic Intelligence as an input to the Strategic Management process, and identified the perceived value Strategic Intelligence could add in the decision-making process. The research study shows that organisations have not yet fully embraced a model for a cooperative global internal corporate Strategic Intelligence System or Portal that will incorporate all aspects of Strategic Intelligence into a single, easily manageable resource for management’s strategic planning and decision-making process, even though it could enhance their ability to withstand the onslaught of global competitors and expand their business into new markets, protect their local market or identify potential merger or acquisition targets, and increase innovation within the organisations. / Business Management / M. Com. (Business Management

Technical provisions representthe most important item of the liabilities of insurance companies. First chapter of this thesis deals with the characteristics and the sense of provisions in any accounting entity. The next chapters are concerned with the regulation of provisions according to the current czech account and tax law and to the IAS/IFRS. The next chapter is the most extensive. This chapter describes technical provisions and rules for their creation, accountig, measurement according to the current valid law in the Czech republic. The subchapters describe the particular technical provisions and their differenties and calculation. The related topics of the reinsurance and the financial placing are presented here too. The examplas for accounting of the technical provisions int the life and the non-life insurance company are shown in the practical part of this thesis.

台灣壽險業近年來面對利差損、國內低利環境，與國內長天期投資工具不足的窘境，因而保險法第146-4條修正後，壽險業者擴大持有國外投資部位。本研究以海外債券投資作為國外投資代理變數，探討海外債券投資與壽險公司之營運風險、經營績效的關係。採用2008年至2016年，共25家壽險公司不平衡追蹤資料，以OLS、Panel Data兩種統計方法作實證分析，再以金融海嘯到歐債危機的時間點，劃分成前期(2008年-2011年)、後期(2012年-2016年)作比較分析。 根據全期資料之分析：以線性模型來看，海外債券投資與營運風險為顯著負相關；和經營績效則是顯著正相關。若是非線性模型，海外債券投資與營運風險為U型相關，臨界點在18.83%。分期資料之分析則顯示：前期資料結果與全期資料一致。但後期資料的研究結果出現反轉，在線性模型下，海外債券投資與營運風險呈現顯著正相關，和經營績效則呈現顯著負相關；而非線性模型下，海外債券和營運風險的U型相關，臨界點下降至13.56%。 / In recent years, life insurers in Taiwan face the spread loss of interest rate, low interest rate environment and shortage of long-term instruments. Life insurers started to expand their foreign investments after the amendment of Article 146-4 of Insurance Act in 1992. This study aims to investigate the relations among overseas bonds investment, operational risk and business performance of life insurers. Data is composed of 25 life insurers from 2008 to 2016. With consideration of the global financial crisis and European debt crisis, this study then divides the data into two subsets (before and after the crises). The results shows that under the linear model there is a significant negative relation between overseas bonds and operational risk, but a significant positive relation between overseas bonds and business performance. Under non-linear model there is a significant U-shaped relationship between overseas bonds and operational risk, with the critical point at 18.83%. The results for the subset data before the crises (2008-2011) is consistent with the based on the whole data (2008-2016). However, the analysis for the subset data after the crises (2012-2016) display different result. Under the linear model, the relation between overseas bonds and operational risk is positive, but it is negative between overseas bonds and business performance. Additionally, the critical point of that U-shaped relationship between overseas bonds and operational risk under the non-linear model drops to 13.56%.

This master's thesis assesses the actuel situation of the insurance of Miroslavské strojírny vehicles and contains suggestions for its optimalization. It deals with analysis of the insurance products in motor vehicles insurance in the Czech market.

This thesis explores the application of machine learning 8ml9 techniques in customer classification and their intergration into customer relationship management (CRM) systems within the corporate insurance industry. The research aims to address the gap in the use of AI-CRM for the corporate insurance industry. It was conducted as a case study at a Swedish insurance broker company. The study leveraged external data sources to create a data seet on customer information. The feature selection process included Variance Influence Factor (VIF) to remove collinearity and then Mutual Class Info and Random Forest, which are methods used to find which independent variables affect the dependent variable the most. Also, Recursive Feature Testing was applied to find the best feature combinations. Four different binary classification models were implemented and compared - Decision Tree, Random Forest, Support Vector Machine, and Artificial Neural Network. Note that Random Forest can be used both for feature selection and classification. The models were tested on four different feature combinations and evaluated using Accuracy, Recall, Precision, F1-score, and ROC-AUC. The study further conducted interviews at the partner company to evaluate their current CRM system. The findings show that ML-based customer classification can be leveraged to effectivize the customer acquisition process for corporate insurance. The Support Vector Machine model achieved the highest accuracy, at 80%. Depending on the avaliable data and the use of metrics, different classifiers had the best performance. The study also found that when implementing classification into AI-CRM, the specific requirements at the company need to be examined. This study found it important to conersider the data procurement process, the current customer acquisition process, the risks associated with misclassification, and present bias. The findings of this study have theoretical implications for the implementation of AI-CRM for customer acqusition. It demonstrates the practical benefits of intergrating machine learning techniques into CRM systems, emphasizing the effectiveness of AI-CRM for customer classification. Further, by comparing different classification models and evaluating their performance, the study enhances the theoretical understanding of model selection for customer classification tasks in this specific domain. Additionally, the research provides insights into effective feature selection methods, aiding researchers and practitioners in extracting relevant variables for customer classification. / Denna studie utforskar tillämpningen av maskininlärning (ML) inom kundklassificering och dess intergration i kundrelationssystem (CRM) inom företagsförsäkringsbranschen. Forskningen syftar till att fylla kunskapsluckan inom användningen av AI-CRM inom företagsförsäkringsbranschen. Studien genomfördes som en fallstudie på ett svenskt försäkringsmäklarföretag. Studien utnyttjade externa datakällor för att skapa en dataset av kundinformation. Processen för val av variabler inkluderade Variance Influence Factor (VIF) för att ta bort kollinearitet och sedan Mutual Class Info och Random Forest, som är metoder som användsför att hitta vilka oberoende variabler som påverkar den beroende variabeln mest. Dessutom användes Recursive Feature Testing för att hitta de bästa kombinationerna av funktioner. Fyra olika binära klassificeringsmodeller implementerades och jämfördes- Decision Tree, Random Forest, Support Vector Machine och Artificial Neural Network. Observera att Random Forest kan användas både för val av funktioner och klassificering. Modellerna testades med fyra olika kombinationer av variabler och utvärderades med hjälp av Accuracy Recall, Precision, F1-score och ROC-AUC. Studien genomförde även intervjuer på partnerföretaget för att utvärdera deras nuvarande CRM-system. Resultaten visar att ML-baserad kundklassificering kan användas för att effektivisera processen för kundanskaffning inom företagsförsäkring.  Support Vector Machine-modellen uppnådde högst accuracy, 80%. Beroende på tillgängliga data och användning av evalueringsmått hade olika klassificerade bäst prestanda. Studien fann också att vid implementering av klassificering i AI-CRM måste de specifika kraven på företaget undersökas. Denna studie fann det viktigt att beakta processen för dataanskaffning, den nuvarande processen för kundanskaffning, riskerna med felklassificering och nuvarande partiskhet. Resultaten av denna studie har teoretiska implikationer för implementeringen av AI-CRM för kundanskaffning. Den visar på de praktiska fördelarna med att integrera maskininlärningstekniker i CRM-system och betonar effektiviteten hos AI-CRM för kundklassificering. Dessutom förbättrar studien den teoretiska förståelsen för val av modeller för kundklassificeringsuppgifter i det specifika domänet genom att jämföra olika klassificeringsmodeller och utvärdera deras prestanda. Studien ger också insikter om effektiva metoder för val av variabler och hjälper forskare och utövare att extrahera relevanta variabler för kundklassificering.

Churn refers to the discontinuation of a contract; consequently, customer churn occurs when existing customers stop being customers. Predicting customer churn is a challenging task in customer retention, but with the advancements made in the field of artificial intelligence and machine learning, the feasibility to predict customer churn has increased. Prior studies have demonstrated that machine learning can be utilized to forecast customer churn. The aim of this thesis was to develop and implement a machine learning model to predict customer churn and identify the customer features that have a significant impact on churn. This Study has been conducted in cooperation with the Swedish insurance company Bliwa, who expressed interest in gaining an increased understanding of why customers choose to leave.  Three models, Logistic Regression, Random Forest, and Gradient Boosting, were used and evaluated. Bayesian optimization was used to optimize the models. After obtaining an indication of their predictive performance during evaluation using Cross-Validation, it was concluded that LightGBM provided the best result in terms of PR-AUC, making it the most effective approach for the problem at hand. Subsequently, a SHAP-analysis was carried out to gain insights into which customer features that have an impact on whether or not a customer churn. The outcome of the SHAP-analysis revealed specific customer features that had a significant influence on churn. This knowledge can be utilized to proactively implement measures aimed at reducing the probability of churn. / Att förutsäga kundbortfall är en utmanande uppgift inom kundbehållning, men med de framsteg som gjorts inom artificiell intelligens och maskininlärning har möjligheten att förutsäga kundbortfall ökat. Tidigare studier har visat att maskinlärning kan användas för att prognostisera kundbortfall. Syftet med denna studie var att utveckla och implementera en maskininlärningsmodell för att förutsäga kundbortfall och identifiera kundegenskaper som har en betydande inverkan på varför en kund väljer att lämna eller inte. Denna studie har genomförts i samarbete med det svenska försäkringsbolaget Bliwa, som uttryckte sitt intresse över att få en ökad förståelse för varför kunder väljer att lämna. Tre modeller, Logistisk Regression, Random Forest och Gradient Boosting användes och utvärderades. Bayesiansk optimering användes för att optimera dessa modeller. Efter att ha utvärderat prediktiv noggrannhet i samband med krossvalidering drogs slutsatsen att LightGBM gav det bästa resultatet i termer av PR-AUC och ansågs därför vara den mest effektiva metoden för det aktuella problemet. Därefter genomfördes en SHAP-analys för att ge insikter om vilka kundegenskaper som påverkar varför en kund riskerar, eller inte riskerar att lämna. Resultatet av SHAP-analysen visade att vissa kundegenskaper stack ut och verkade ha en betydande påverkan på kundbortfall. Denna kunskap kan användas för att vidta proaktiva åtgärder för att minska sannolikheten för kundbortfall.

InsurTech är en ny gren inom försäkringsbranschen som använder innovativa informationsteknologier för att effektivisera processer. Trots att försäkringsbranschen historiskt sett varit trögrörlig och inte förändrats mycket, har intresset för InsurTech ökat markant de senaste åren, vilket har skapat en betydande hype. På grund av detta syftar denna uppsats till att analysera och värdera om InsurTech kan få någon större effekt på den svenska försäkringsbranschen. Genom att fokusera på teknologierna Big data, Artificiell Intelligens, Telematik och Internet of Things undersöker uppsatsen om InsurTech kan effektivisera försäkringsprocessen och påverka marknaden och dess aktörer. Studien tillämpar en kvalitativ metod och inleds med en litteraturstudie i syfte att ge en helhetsbild av forskarvärldens bild av InsurTechs potential i dagsläget. Därefter följer en intervjustudie med experter inom försäkringsbranschen i syfte att framhäva deras bild av potentialen och vad som i dagsläget går att implementera i branschen. Studien tar avstamp i en teoretisk analysmodell baserad på ett transaktionskostnadsperspektiv. Modellen byggs upp i tre faser; Ex-ante, Avtal och Ex-post och belyser de transaktionskostnader som uppstår under försäkringsprocessen. Litteraturstudien visar på en stark hype från forskningsvärlden där potentialen framhävs starkt. Även intervjustudien visar att det finns mycket potential för InsurTech, samtidigt som den även lyfter flera hinder och utmaningar för InsurTech i dagsläget. Sammantaget kommer uppsatsen fram till att InsurTechs potential i dagsläget främst finns i AI och Big data för att effektivisera datahantering och skadereglering. Samt att utvecklingen förmodligen kommer att ske från samarbete mellan etablerade försäkringsbolag och flera nya underleverantörer. / InsurTech is a new branch within the insurance industry that leverages innovative information technologies to streamline processes. Although the insurance industry has historically been slow-moving and resistant to change, interest in InsurTech has significantly increased in recent years, creating a considerable hype. Consequently, this thesis aims to analyse and assess whether InsurTech can have a substantial impact on the Swedish insurance market. By focusing on technologies such as Big Data, Artificial Intelligence, Telematics, and the Internet of Things, this thesis explores whether InsurTech can increase the efficiency of insurance processes and influence the market and its players. The study employs a qualitative methodology, beginning with a literature review to provide a comprehensive overview of the current academic perspective on Insurtech’s potential. This is followed by an interview study with industry experts to highlight their views on the potential and current implementable aspects of InsurTech within the industry. This study is grounded in a theoretical analysis model based on a transaction cost perspective. The model is structured into three phases: Ex-ante, Contract, and Ex- post, highlighting the transaction costs that arise during the insurance process. The literature review reveals significant hype from the academic community, emphasising the strong potential of InsurTech. While the interview study indicates considerable potential for InsurTech it also points out several current obstacles and challenges. Overall, the thesis concluded that the greatest potential currently lies in AI and Big Data to enhance data management and claims processing. Furthermore, the development is likely to occur through collaborations between established insurance companies and various new suppliers.