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.

隨著我國壽險業資產比率快速增加，投資績效不但影響眾多保戶的權益，更影響整體經濟之安定，但面臨現今全球金融和經濟環境劇烈動盪，加上壽險同業間激烈競爭的情況，我國壽險公司如何訂定投資策略做好資產配置，對壽險公司的營運健全而言相當重要。現行保險法第一百四十六條限制壽險公司的投資上限，但法令限制對壽險公司資產配置的影響為何，本研究將透過Markowitz的平均數－變異數投資組合模式對我國整體壽險業及國泰人壽、南山人壽、新光人壽及富邦人壽探討之；並以夏普指數、崔納指數及詹森指數，評估上述四家壽險公司之資金運用績效；另藉由分析壽險業之資金成本是否低於實際投資率，以達成研究壽險業長期資產配置之穩健度。 本研究主要結論如下：1.運用Markowitz 投資組合模型所推導出的效率前緣，以最大Sharpe Measure評估，不受法令限制下所建立之最佳投資組合，較有受法令限制下所建立之最佳投資組合的期望報酬率高，且分散風險的效果較佳。2.整體壽險業及前四大壽險公司之實際投資報酬率皆低於其實際投資組合之期望報酬，顯示壽險業於資金運用的靈活度及績效性有改善的空間。3.以績效評估指標求出前四大壽險公司的資金運用績效，發現新光人壽在此三項評估指標皆位居最後；國泰人壽在評估中皆名列前茅。4.新光人壽的平均實際報酬率低於平均資金成本，應控管好資金成本並加強投資組合之績效；富邦人壽平均實際報酬率高過平均資金成本最多，顯示富邦人壽在資金成本控管及投資績效有良好之表現。整體壽險業的實際報酬率亦高於平均資金成本，顯示我國壽險業於營運狀況正常。 / With the life insurance companies’ assets ratio rapidly increasing, the investment performances affect not only the right of a number of policy holders, but also affect the economic stability. However, facing the dynamic global financial and economic environment and the keen competition in the domestic life insurance industry, the life insurance companies need to adopt the proper investment strategy. Law of Insurance 146th p restricts the investment upper limit of the life insurance company. This research will use Markowitz MV model to discuss the influence of this investment restriction on life insurance companies’ asset allocation by the samples of Life industry, Cathay Life Insurance, Nan Shan Life Insurance, Shin Kong Life Insurance, and Fubon Life Insurance, and evaluate the performances of these four life insurance companies by Sharpe ratio, Treynor ratio, and Jensen’s measure. This research also analyze the cost of capital and real rate of return of these companies to examine the stability of life insurance industry’s long term asset allocation. The conclusions of this research are as follows: 1.Evaluated by the Markowitz efficient frontier and the Sharpe measure, there is the higher expected rate of return and better diversification with no investment restriction. 2.The actual rates of return of the life insurance industry and the above four life insurance companies are below the expected rates of returns of their portfolio evaluated be the Sharpe measure, which means the life insurance industry need to prove their capital allocation. 3. Comparing the performance of the life insurance companies by the performance indicator, we find the then Shin Kong Life Insurance is the last, while Cathay Life Insurance has a good score. 4. We also find the real rate of return of Shin Kong Life Insurance is lower than its cost of capital, which means Shin Kong Life Insurance need to adjust its cost of capital and the investment performance. Meanwhile, Fubon Life Insurance is the excellent in controlling the cost of capital and investment. The real rate of return of the Life insurance industry is higher than its cost of capital, and that shows the Life insurance industry has normal operation.

The primary objective of this study was to develop an Enterprise Risk Management Maturity Framework (ERMMF) for use in the assessment of Enterprise Risk Management (ERM) maturity levels of the insurance industry in Botswana. The ERMMF incorporated elements from the Committee of Sponsoring Organisations of the Treadway Commission (COSO)’s ERM framework and the AON risk maturity model. Five criteria were utilised to define each of the eight components of ERM used to measure ERM maturity levels. The framework was developed qualitatively through literature review. The ERMMF was tested empirically to evaluate the ERM maturity levels of the insurance industry in Botswana. Data was collected from 12 respondents from long-term insurance companies, 15 from short-term insurance companies, 4 from reinsurers and 59 from brokerages. The findings revealed that the whole insurance industry is at the Defined stage of ERM maturity level as the responses bordered around 3 on the developed scale of measurement. The findings implied that the insurance sector in Botswana has generally implemented ERM but not enough follow-ups had been made to ensure that ERM became a continuous process. Results further indicated that although the whole sector was at the defined stage of ERM, the responses in each component differed per stratum. Literature indicates that insurance organisations, regardless of stratum within which they are, are faced with similar risks generally. The differing responses could be due to the magnitude of risks that could differ according to unique characteristics of each stratum. The study further recommended an enterprise risk management implementation procedure for the insurance industry in Botswana. / Business Management / D. Admin (Business Management)

和顧客保持長期關係，對於企業經營是有利的，然而，由於資訊科技的進步與物流效率的提升，顧客越來越來越聰明，要尋找並取得替代產品與服務也越來越容易。在這樣的環境前提下，如何使顧客願意與企業維持長期關係而不轉向競爭者，是企業的一大挑戰。在文獻探討中本文發現，其關鍵是在於要能夠持續與顧客互動並了解顧客，並且將這些了解應用於產品設計、流程設計與組織設計。對於顧客的了解，本文定義其為顧客知識，就成為了顧客關係的基石。不過，由於與顧客的接觸點越多，了解顧客的管道就越多，其整合上有一定的難度，而資訊科技與傳統行銷方法都能夠產生對於顧客的了解，再加上產生並應用對於顧客的了解，以塑造顧客在消費與使用產品與服務時能得到高度的滿意，已經企業所有部門的責任，這樣跨部門的作業也不是一件簡單的事，這些議題都是企業在顧客知識管理上所需面對的。 在過往的文獻中所談的顧客知識管理，很多都混淆顧客資訊與知識，較無探討企業如何加工與顧客互動所得到的資訊，因此無法清楚了解企業在顧客知識管理上所作的貢獻為何。本文就以流程觀點，並以目前市場需求飽和且競爭激烈的人壽保險產業中的一家企業：保誠人壽為例，探討企業內的顧客知識流程，並且探討主要的影響因素，以了解企業在顧客知識管理上所作的努力。 而在本文個案的探訪後，本文還嘗試以服務業作業系統的角度，提出一種顧客知識的新分類：前台作業所需的顧客知識、後台作業所需的顧客知識，並且根據個案中保誠人壽的實際做法，修正文獻探討所提出的顧客知識管理架構，並且提出三種顧客知識管理的類型以及提出其管理重點，以作為實務界與後人研究的參考。 / It is beneficial for an enterprise to keep long-term relationship with its customers. However, owing to advances of information technology and the improvement of logistics efficiency, it is not difficult to search and acquire better products and services to substitute for what they are using now. Under these circumstances, it is a challenge for enterprises to retain their customers. How to do this? The answer in literatures is: to gain more customer insights (defined as customer knowledge in this study) through interactions with customers and apply them in product design, process design and organizational structure design. Therefore customer knowledge is the basics of long-term relationships with customers. But still, there are issues. First, an enterprise may have many contact points with its customers. Then, there are traditional ways versus information technology ways to generate customer knowledge. Finally, an enterprise has to think how to address appropriate customer knowledge to right departments and use them to enhance the total experience when interacting with customers. These are all issues an enterprise has to face with when trying to do “Customer Knowledge Management” (CKM). Most of past studies on CKM did not tell the difference between information and knowledge, thus can not clearly define what contributions an enterprise makes when implementing CKM. This study attempts to investigate practices of CKM in an enterprise in life insurance industry, in which the demand is saturated and the competition is fierce, from a process perspective to manifest how customer knowledge is generated and applied, and what main factors are to affect CKM in an enterprise. After investigation of the case, this study proposes a new framework, from the operation system in service industry, to classify customer knowledge into two categories: customer knowledge needed for front-office operations, and customer knowledge needed for back-office operations. In addition, according to practices in this case, this study will revise the CKM process framework proposed right after the literature review, and will raise keys to successful CKM in CKM processes, for the reference for following studies.

1.Summary - review in English The above-mentioned work comprises of two basic issues that are connected together, namely commentary on Insurance Policy Act as general introduction into the field of insurance policies and consequent and special issue of legal expenses insurance interpretation. The basic meaning of it was to introduce not only the provisons concerning insurance policies, but also to show one of the insurance policies type in details, what could be hardly possible or satisfactory without preceding description of the Insurance Policy Act. Thus, complete summary of this work will be divided into two parts; first would be the part concerning Insurance Policy Act and second would be the part concerning legal expenses insurance. 1.1.Insurance Policy Act - Summary The first part of this work concerned, as mentioned above, the Insurance Policy Act, whereas my primary aim was to interpret provisions of this act that were worth it and point out namely on imperfections of the existing legislature. It wasn't and wouldn't be of any significant value just to copy and paste individual provisions of the act, therefore, this work aimed primarily on introduction of new ideas and as a basis for that served a confrontation with the actual wording and its interpretation, whereas plain consent with what was...