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service. Promotion The insurance company advertises logo aggressively. The company uses different events to promote utilizes my personal contact (phone, email, mail…) to inform updated services. The company in person to pursue to deliver agents have sufficient knowledge about company products. The call-center is availa... |
-5 years. 56 customers (13.6%) are new vehicle owners with less than 1 year duration with a company whereas, 154 (37.4%) and 89 customers (21.6%) have been with their companies for periods of 5 – 10 years and more than 10 years respectively. Measurement Adequacy The sampling adequacy of each variable was measured using... |
What Should A Car Insurance company focus on?Ziwen Liao*†Xinxin Zhong‡Di Ma§April, 2024AbstractFollowing the pandemic’s economic impact, auto insurance companies require recovery.To assist companies in understanding their customers better and creating successful strategies,relevant data was collected. This data reveale... |
throughout the duration of their relationship with the company. Kumar, Ra-mani, and Bohling (2004) explained that businesses intend to calculate the lifetime value of eachcustomer and use this data to develop distinctive marketing campaigns tailored to each individual.Therefore, the first research question pertains to ... |
the predictedpremium also increases. When the risk of needing to make claims is reduced, the lifetime value ofthe client increases due to the higher premium that their car commands. To support our hypothesisand identify more potential variables related to lifetime value, we analyzed a dataset containinginformation for ... |
divided into three factions predicated on the Coverage level:Basic, Extended, and Premium. As shown in Figure 2, results from the t-test divulge a statisticallysignificant disparity in Lifetime Value between both Extended and Basic Coverage, and Premiumand Extended Coverage, with p-values falling below 0.01. This outco... |
it becomes apparent that "Marital.Status" does not significantlysway variations in Lifetime Values. To perform an exhaustive analysis, multiple t-tests is executed,comparing Lifetime Values among the different Marital Status cohorts. As shown in Figure 5, the results of the t-tests offer intriguing insights: the juxtap... |
explores the effect of vehicle classes (Two-Door Car, Four-Door Car, SportsCar, SUV , Luxury Car, and Luxury SUV) on the CLV , using boxplot visualizations generatedthrough ggplot2.As shown in Figure 7, upon visual inspection, a distinct correlation emerges between vehicle class and CLV variance. To validate this obser... |
= mean(Y|Luxury Car) −6.61∗∗∗mean(Y|SUV) = mean(Y|Luxury SUV) −7.01∗∗∗mean(Y|Luxury Car) = mean(Y|Luxury SUV) -0.05 Note: * represent 10% significance level, ** represent 5% significance level; *** represent 1%significance level. Y is the ‘Custome Lifetime Value’. When t statistic > 0, it means thatmean(Y|situation 1) ... |
as significant pre-dictors, whereas Location Code and Sales Channel failed to achieve any significance in their re-spective regressions. The regression involving Marital Status suggested that the Single categorywas significantly associated with the dependent variable. Similarly, Vehicle Class was discerned as a signifi... |
9,134 9,134 9,134 9,134Adjusted R20.159 0.159 0.159 0.159 0.159 0.159From the Table 4, the preliminary regression embraced all variables, yielding an AdjustedR-squared value of 0.159. This metric offers an estimate of the model’s capability to replicateobserved outcomes, where a value ranging from 0 to 1 denotes the fr... |
The result is reported in Table 5.Univariate Logistic Regression: Prior to progressing with a comprehensive model, each predictor was individually evaluatedagainst the dependent variable "LifeT" to comprehend their individual significance. The result isreported in Table 5.Table 5: Univariate Logistic Regression resultL... |
-0.0001 -0.00002(0.0002) (0.0002) (0.0002)Vehicle.Class(Luxury Car)13.891 13.887(172.954) (173.026)Vehicle.Class(Luxury SUV)13.809 13.806(162.969) (162.969)Vehicle.Class(Sports Car)0.224 0.222(0.205) (0.205)Vehicle.Class(SUV)0.111 0.111(0.182) (0.182)Vehicle.Class(Two-Door Car)0.019 0.020(0.058) (0.058)Constant−2.336∗∗... |
and not randomlychosen, as the company investigated may be more focused on young clients.Vehicle class can be a reflection of lifestyle, financial status, and even risky behavior. Moredirectly, it is an indicator of the value of the vehicle, which can impact insurance quotes, as men-tioned in the introduction. The sign... |
10.243620.8080.02.20Conditional likelihood based inference onsingle-index models for motor insurance claimseverityCatalina Bolance ´1, Ricardo Cao2and Montserrat Guillen1Abstract Prediction of a traffc accident cost is one of the major problems in motor insurance.To identify the factors that infuence costs is one of th... |
risky events that do not always lead to an accident (see Guillen et al., 2019, 2020 andGuillen, Nielsen and P ´ ın, 2021). Risk scores such as the ones obtained with erez-Mar ´index-models can be combined with the evaluation of near-miss information to improvethe performance of predictive modelling in motor insurance p... |
and Cao (2013) investigated maximum likelihoodalternatives based on the kernel estimation of the conditional distribution and showedthat previous methods for censored data could be improved.Nonparametric regression is more general than the single-index model specifed in(1). Indeed, it emanates from a more general speci... |
with onlyclassical non-telematic variables. The data set is available at SORT-BCG/. We observe how the mean yearly claim cost per policy does not changewith a linear index; however, the shape of the distribution depends on a linear index,something that could be considered when calculating the premium. 4Conditional like... |
directly.Let(X1,Y1),...,(Xn,Yn) be a random sample of the dependent variable and the co-variates, where Xi= (Xi1,..., Xid)⊤ and it is assumed that at least one covariate is con-tinuous. Let Kbe a nonnegative kernel and h1,h2two positive bandwidths. In line withBashtannyk and Hyndman (2001), the kernel conditional densi... |
the fnal maximum conditional likelihood estimator is defned asθˆn= argmax ℓˆn(θ). θ The estimation procedure including the two smoothing parameters h1andh2will bedescribed in sub-section 2.3. A similar procedure based on the leave-one-out estimatorof the hazard rate model was proposed by van den Berg et al. (2021). We ... |
h i−1Σ2= ℓ(θ0) and Σ1= E∇θ log( fθ (Y|θ ⊤X))θ )(∇θ log( fθ (Y|θ ⊤X))θ )⊤ =θ0 =θ0Z = (∇θ log( fθ (y|θ⊤x))θ )(∇θ log( fθ (y|θ⊤x))θ )⊤ f(x,y)dxdy. =θ0 =θ0All the proofs can be found in the Supplementary Material.The asymptotic variance-covariance matrix in (8) is different from the one obtainedby Delecroix et al. (2003). ... |
the variance-covariance matrix in (8)we calculate the correspondingderivatives of the leave-one-out kernel estimation of conditional log-likelihood defnedin(6). Asymptotic normality inference, based on (7),is carried out using the esti-mated variance-covariance matrix, replacing theoretical derivatives by estimated one... |
values, i.e., the mean and the variance, as followsnh i2 ˆ2PMCC = − 1∑ Yi− mˆθ ⊤Xi− σ θ ⊤Xi, (10)ni=1� � where mˆθ ⊤Xiis the kernel estimator of the conditional expectation E Y i|θ ⊤Xiand� ˆ2σ θ⊤Xiis estimated with the kernel estimates of both expectations as follows: h i2σˆ2θ ⊤Xi= EˆYi2|θ⊤Xi− EˆYi|θ⊤Xi, where ∑n t−θ ⊤... |
⊤x,σ = |θ ⊤x|) x)21 (y− θ⊤p exp− 2π|θ ⊤x|2 2|θ ⊤x|2 (y− θ ⊤x)exp1 |θ ⊤x| |θ ⊤x| (y− θ⊤x)1+ exp|θ⊤x| PositivelognormalWeibullChampernowne(µ = θ ⊤x,σ = |θ ⊤x|) (α = 1, σ = |θ ⊤x|) (α = 1,M= |θ ⊤x|) (α = 2,M= |θ ⊤x|) 1 (ln(y) − θ⊤x)2p exp− 2π|θ ⊤x|2 2|θ⊤x|2y 1 yexp− |θ ⊤x| |θ ⊤x| |θ ⊤x| 2(y+ |θ ⊤x|) 2|θ ⊤x|2y2(y2+ |θ ⊤x|2... |
isH0:θk= 0, k= 1,..., dand as an alternative hypothesis we assume that the sign of theparameter is known, i.e., H1:θk> 0, k= 1,..., d. The statistic test is Z= θˆjse(θˆ2− θˆ3). 13 Catalina Bolanc ´e, Ricardo Cao and Montserrat Guillen Table 2. Power of the test for skewed distributions. The values are calculated using ... |
distribution should be suitable. 14Conditional likelihood based inference on single-index models for motor insurance claim severity 4. Data analysis and model estimations of automobile claim costs In this section we analyse the effect of risk factors on the distribution of the cost perautomobile claim in a real case st... |
www.vtpi.org Info@vtpi.org 250-508-5150 © 2001 -2023 Todd Alexander Litman All Rights Reserved Distance -Based Vehicle Insurance Feasibility, Costs and Benefits Comprehensive Technical Report 10 March 2023 By Todd Litman Victoria Transport Policy Institute Abstract Vehicle insurance is a significant portion of total ve... |
................................ ................................ .............................. 34 1. Mileage Rate Factor (KRF) ................................ ................................ ................................ .............. 34 2. Pay-at-the-Pump (PATP) ................................ .................. |
................................ ................................ ... 83 2. Transaction Costs ................................ ................................ ................................ ........................... 83 3. Financial Risks ................................ ................................ .............. |
their current mileage would be no worse off on average then they are now (excepting any additional transaction costs), while those who reduce their mileage save money. Distance -based pricing can help achieve several public policy goals including actuarial accuracy, e quity, affordability, road safety, consumer savings... |
vehicle travel, allowing insurance prices to reflect when and where a vehicle is driven in addition to existing rating factors. It is predicted t o cost $150 or more per vehicle -year and raises privacy concerns. Installation costs may decline somewhat in the future as more vehicles have factory -equipped GPS transpond... |
the implementation costs and effectiveness at achieving various objectives for the seven distance -based pricing options considered in this study. Summary of Distance -Based Pricing Options Implementation Costs Effectiveness Mileage Rate Factor Low Low Pay-At-The-Pump High Medium Per-Mile Premiums, Mandatory Low High P... |
equal levels support and opposition, with responses affected by the concept is described. For example, if described as a reward to consumers who use alternative modes, it tends to have a positive response, but if presented as a surcharge on higher -mileage motorists, it tends to have a more negative response. This stud... |
feasibility of implementing distance -based motor vehicle insurance. Distance -based pricing converts insurance from a fixed cost into a variable cost with respect to vehicle travel. Thus, the more you drive the more you pay, and the l ess you drive the more you save. Distance -based insurance is justified on actuarial... |
risk to themselves and to other road users. It is progressive with respect to income. Most lower income motorists should save money, since they tend to drive their vehicles less than average and are relatively price sensitive. It reduces the need to rely on cross -subsidies from low -risk motorists to provide “affo... |
per insured vehicle average approximately $ 850 per vehicle -year in the U.S., or about $1, 360 annually per household. Registration and license fees average about $ 250 per vehicle -year. Figure 1 Typical Costs for Intermediat e Size Car1 Depreciation31%Short-Term Parking & Tolls4%Insurance21%Financing6%Fuel & Oil19%T... |
higher premiums in lower -income communities.4 Higher -income motorists sometimes drive uninsured if they own a vehicle that is only used occasionally. Alth ough most jurisdictions mandate minimum levels of coverage, these requirements are often ignored.5 But enforcement strategies can be effective. In British Columbia... |
higher -income consumers who are likely to purchase other types of insurance, such as household coverage.7 As a result, premiums often overcharge lower -risk motorists (what actuaries call “cream”) and undercharge higher -risk motorists. This results in ex tremely high premiums in lower -income areas, since a greater p... |
EXPLORING THE IMPACT OF E-INSURANCE AS A DISTRIBUTION PLAT FORM TO IMPROVE SALES OF THIRD-PARTY MOTOR INSURANCE IN NIGE RIA. BY OMOTAYO BALOGUN IN FULFILMENT FOR THE AWARD: MSc. INTERNATIONAL MANAGEMEN T (DIGITAL BUSINESS) APPLIED. TEESSIDE UNIVERSITY May, 2024. 1 ABSTRACT The global expansion of the automobile industr... |
insurance sales. By exploring the impact of e-insurance on the sales of third-party moto r insurance in Nigeria, this study seeks to uncover possibilities that can inform strategic interventions and drive the industry toward greater efficiency, accessibility, and customer sati sfaction. Through a deeper understanding o... |
a profound shift in how insurers engage with their clientele. This evolution empowers customers wi th increased access to information regarding their risk exposures, fostering a trend toward greater s elf-reliance in meeting their insurance requirements (Hung 2020). Leveraging digital technologies, platforms, and infra... |
over 71.7% of cars have no access to insurance, and manual selling is more prevalent than techno logy-driven selling. Integrating e-insurance into product distribution channels can benefit the Ni gerian insurance industry significantly. The prospect of an e-insurance-enabled insurance platform is appealing to many in t... |
is that digitalization holds the promise of fundamentally reshaping the value-creation dynamics of the industry. This transformation is anticipated to usher in nov el modes of customer interaction, innovative business processes, fresh risks, and advancements in pro duct technologies (Catlin, Hartmann, Segev, & Tentis, ... |
address the challenges facing the Nigerian insurance industry, particularly in the contex t of third-party motor insurance sales. Despite being a compulsory insurance product, a significant porti on of vehicles in Nigeria remains uninsured, posing financial risks to both vehicle owners and third pa rties. E-insurance h... |
design will enable a holis tic exploration of the research questions, revealing the complex dynamics of e-insurance distribution and its impact on third-party motor insurance sales in Nigeria. 1.9.1 Research Structure Introduction: This section will provide an overview of the research topic, including background inform... |
context. Overall, the literature review will provide a robust body of knowledge that i nforms the research study on the impact of e-insurance as a distribution platform for motor third-party i nsurance in Nigeria. 2.1 Literature Review Strategy A narrative literature review approach was chosen for this stu dy due to it... |
cust omer interactions, irrespective of geographical constraints. 2.1.2 E-Insurance E-insurance encompasses online platforms that offer insurance sales, se rvices, and information. It is a broad term describing the use of the Internet and related inf ormation technologies (IT) in creating and delivering insurance servi... |
inspiring customers to buy more insurance. 2. Assisting in broadening the target market Transitioning into a digital system enables insurance companie s to broaden their reach and enter new markets encompassing diverse cultures, age groups, and social d emographics. 3. Gaining a Competitive Edge: In today's digital age... |
distribution channels. Customer preferences are dynamic, and an effective strategy to cater t o their third-party insurance requirements involves establishing a digital distribution platform . In the research paper titled "The Future of Insurance Intermediation in the Age of the Digital Pl atform Economy," Stricker et ... |
used as a support channel, its adopt ion rate as a distribution channel has been slow. In countries like Nigeria and India, where internet penetration is still low and there are legal issues with online agreements, the insecurity associated with internet tra nsactions remains a significant challenge. For now, the inter... |
383ACTA UNIVERSITATIS AGRICULTURAE ET SILVICULTURAE MENDELIANAE BRUNENSISVolume 62 41 Number 2, 2014GENERALIZED LINEAR MODELS IN VEHICLE INSURANCESilvie Kafková1, Lenka Křivánková21 Masaryk University , Faculty of Economics and Administration, Lipová 41a, 602 00 Brno, Czech Republic2 Masaryk University , Faculty of Sci... |
is calculated from the number of the claims on a contract. They depend on many factors that are believed to have an impact on the expected cost of future claims. Those factors can include the car characteristics (vehicle body , vehicle age) and the profi le of the driver (age, gender, driving history). Based on the idea... |
in context of lapse risk as a mean to understand the relationship between risk factors and to calib rate the lapse risk as accurately as possible. Advantages of the GLMs approach are discussed in Antonio and Beirlant (2007). Furthermore, they presented the usage of generalized linear mixed models in actuarial mathemati... |
to model transformed data. The link function makes a connection between the mean and a linear function of the explanatory variables. A transformation of the mean is modeled as a linear function of explanatory variables.Defi nition 2. The link function g (μ) is a monotonic diff erentiable function of the form g(μ) = x'β,w... |
submodel.Defi nition 6. Consider GLM with design matrix Xn×m and vector of parameters βm. Its submodel , denoted as GLMsub, with design matrix Qn×q and vector of parameters βq satisfi es the following conditions: • it has the same distribution as the proposed GLM, • it has the same link function as the proposed GLM, • th... |
claim frequency on given risk factors. A data set from vehicle insurance will be processed. The data for our case study can be found in (Heller and Jong, 2008). The data set is based on one-year vehicle insurance policies recorded in 2004 or 2005. There are 57 410 policies and 3 913 of them (6.82%) have at least one cl... |
the AIC penalizes the number of parameters, the selected model has smaller AIC than its submodel, for the model 1+agecat+veh_age it is AIC = 127 900 and for the model 1+agecat+veh_age+area it holds AIC = 127 200. Hence, according to AIC, the model is improved. Furthermore, based on an educated guess, signifi cance of th... |
The main part of the paper consists of a case study , where the GLMs are applied in vehicle insurance. We process a data set based on 57 410 one-year vehicle insurance policies. The drivers are divided into groups on the basis of the risk factors. For each group, we model the average number of claims per contract. The ... |
9783642034077.MCCULLAGH, P ., NELDER, J. A., 1989: Generalized Linear Models. London: Chapman and Hall, 532 p. ISBN 0412317605. NELDER, J. A., WEDDERBURN, R. W . M., 1972: Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135, 3: 370–384. ISSN 0035-9238.OHLSSON, E. JOHANSSON, B., ... |
Int. J. Advance Soft Compu. Appl, Vol. 8, No. 3, December 2016 ISSN 2074 -8523 Predictive Modelling for Motor Insurance Claims Using Artificial Neural Networks Zuriahati Mohd Yunos1, Aida Ali2, Siti Mariyam Shamsyuddin2, Noriszura Ismail3, Roselina Sall eh@Sallehuddin1 1Faculty of Computing Universiti Teknologi Malaysi... |
severity , , , , , . Claim frequency is defined as the number of claims per exposure unit, whereas claim severity is defined as the average claim cost per claim . The modelling of claim frequency and claim severity needed an information of exposure, n umber of claims and the amount of the claim (cost). The expected of ... |
has become more apparent. Due to this, actuaries had to solve the problem of finding a model that can explain re alistically the event of risk , and a model that able to handle complex problems in exploiting varying information . The suggestion of ANN approach to motor insurance claim is the use of past experience to t... |
In particular, step 1 to step 4 are carried out on data pre -processing, where the raw data is scaled and norm alized to an appropriate format to facilitate the predicting process. Step 5, which is the step that designs the ANN model, involves the determination of the following variables: i. number of input nodes ii. n... |
mapping between the input and the output variable s. Hence, we applie d suggestion to determine the number of hidden nodes, whether they are “ n", or “ 2n", or “ 2n+1 ”, where “ n” is the number of input nodes. The number of input nodes is predetermined by trial and error in the proposal stage based on the data given b... |
compare Actual output Z. M. Yunos et al. 167 Fig. 1.2: Network structure model with different number of hidden layer 6-6-1 network 6-12-1 network 6-13-1 network 5-5-1 network 5-10-1 network 5-11-1 network 4-4-1 network 4-8-1 network 4-9-1 network 168 Predictive Modelling for Motor Insurance The fin al step is model eva... |
= 0.7 Z. M. Yunos et al. 169 0500100015002000250030003500TPPDOD MSE 0102030405060TPPDOD RMSE 05101520253035TPPDOD MAE 00.050.10.150.20.25TPPDOS MAPE Fig. 1.3: Comparison based on error measurements for claim frequency 010000002000000300000040000005000000600000070000008000000 TPPDOD MSE 050010001500200025003000TPPDOD RM... |
to model complex high -dimensional insurance data , Allgemeines Statistisches Archiv , 88: 375 -397. Chu, F. L. 2009. Forecasting tourism demand with ARMA -based methods, Tourism Management , 30:740 -751. Dalkilic, T. E., Tank, F. and Kula, K. S. 2009. Neural networks approach for determining total claim amounts in ins... |
of unsupervised neural networks in rate making procedure, The General Insurance Conventi on and Astin Colloquium , 2: 550 -567. Pinquet, J. 2012. Experience rating in non -life insurance. Working Papers hal-00677100 , HAL. Rodriguez, J. D., Perez, A. and Lozano, J. A. 2010. Sensitivity Analysis of k -Fold Cross Validat... |
Classifying Risks On Motor Insurance Policies For I FRS 17 Implementation In General Insurance Companies Andiansyah Prima Wardana1, and Danang Teguh Qoyyimi2* 1Actuarial Unit, PT Asuransi Jasaraharja Putera, Jakarta, Indonesia 2Departement of Mathematics, Universitas Gadjah Mada, Yogyakarta, DI Yogyakarta, Indonesi a A... |
are differences in the calculation of liabilities under the current conditions. The difference is that the current profit margin (CSM) component has not been amortized even though IFRS 17 requires amortization of CSM for each risk group . Amortization of CSM requires a more accurate projection of future liabilities. Th... |
Determine k as many clusters as you want to form. Many clusters are determined by researchers or based on statistical methods. Dividing data at random in several partitions with a fixed size. The sample size is minimal (40 + 2k). The number of partitions is determined first. Apply the PAM algorithm to each partition to... |
of areas including web search and ecology ranking. Extreme Gradient Boosting was first introduced by Friedman. The advantage of the XGBoost algorithm is that it can use storage memory efficiently. Extreme Gradient Boosting is a technique in machine learning for regression and classification problems that p roduces pred... |
𝑎=𝑦̂𝑡𝑡−1 ℎ=𝑓𝑡(𝑥𝑖) 𝑓(𝑎)=𝑙(𝑦𝑖,𝑦̂𝑡𝑡−1) Then Equation 3 becomes ℒ(𝑡)=∑ 𝑙(𝑦𝑖,𝑦̂𝑡𝑡−1)𝑛𝑖=1+(𝜕𝑙(𝑦𝑖,𝑦̂𝑡𝑡−1)𝜕(𝑦̂𝑡𝑡−1))𝑓𝑡(𝑥𝑖)+(𝜕2𝑙(𝑦𝑖,𝑦̂𝑡𝑡−1)𝜕(𝑦̂𝑡𝑡−1)2)𝑓𝑡(𝑥𝑖)2+⋯+𝛺(𝑓𝑡) (4) ℒ(𝑡)=∑ +𝛺(𝑓𝑡) (5) ℒ(𝑡)=∑ +𝛺(𝑓𝑡) (6) Suppose 𝑓𝑡 has as many leaf nodes as K, Ij is the jth n... |
maximize the distance between classes of data. 5ITM Web of Conferences 58, 04006 (2024)The 6th IICMA 2023 Initially, SVM was developed for the problem of class classification of two classes and then developed for multiclassical classification . In multiclass classification, hyperplane formed is more than one. One appro... |
is duplicate data, the analysis process is not optimal because the duplicate data is seen as two different data. This will certainly change the accuracy of the model for future analysis. There were 5 data deleted to solve the problem of duplicate data in the data. The next step is 6ITM Web of Conferences 58, 04006 (202... |
Frekuensi Medoid (Rupiah) 1 607 -4.027.000 2 65 -457.917.808 3 255 -28.511.927 4 116 -169.654.795 5 34 -955.621.918 From table 1 it can be seen that the cluster 1 to cluster 5 has a medoid that has not sorted according to the cluster. The author does not sort the clusters of the smallest or largest medoids because sequ... |
parameters namely 0.01; 0.1; and 0.3. The author also uses a maximum of 1000 iterations (nrounds = 1000), the general parameter of the booster is gbtree, and the task parameter of the objective is multi: softprob because it is used for classification with more than two classes. Table 3. Comparison of the accuracy value... |
to predict class 3 data compared to actual class 3 data is 85.60%. Class 4 sensitivity is 0.9816 which means that the ability of the model to predict class 4 data compared to actual class 4 data is 98.16%. Class 5 sensitivity is 1 which means that the ability of the model to predict class 5 data compared to actual clas... |
SVM XGBOOST ADABOOST Akurasi 68,45 91,02 95,88 65,20 93,25 89,57 Sensitivitas kelas 1 77,51 71,22 92,46 70,11 78,23 71,05 Sensitivitas kelas 2 61,29 100 98,92 58,82 100 100 Sensitivitas kelas 3 86,60 85,60 88,40 71,25 89,26 80,00 Sensitivitas kelas 4 84,46 98,16 99,47 75,44 99,15 97,06 Sensitivitas kelas 5 58,71 100 10... |
Extreme Gradient Boosting, Support Vector Machine, and Adaptive Boosting methods can classify vehicle insurance data well. 5. The parameter tuning process in the training data aims to get the right parameters so that a more accurate model is obtained than when no parameter tuning is performed. When the training model d... |
Journal of Scientific & Industrial Research Vol. 83, February 2024, pp. 183-190 DOI: 10.56042/jsir.v83i2.4302 Motor Insurance Policy Selection: A Joint Spherical Fuzzy Analytic Hierarchy Process (SF-AHP) and Combined Compromise Solution (CoCoSo) Approach Mangesh Joshi Shri Ramdeobaba College of Engineering and Ma nagem... |
is mandatory in most jurisdictions and typically includes both bodily injury liability and property damage liability. Collision coverage is designed to provide protection against damage to the insured's vehicle in the event of an accident. This coverage is optional but may be required if the vehicle is financed or leas... |
cost of insurance premiums is another challenge in policy selection. Insurance premiums can vary significantly depending on the insured's driving record, age, vehicle type, and location.4 It can be difficult to determine which policy provides the best value for the price, particularly for individuals who are on a tight... |
judgments or the information that is available. It is relatively easy to implement and computationally efficient. Literature Review The systems are designed to aid decision-makers in addressing intricate decision-making challenges across various sectors. The development of the decision support systems reflects the on... |
are availablein health insurance policy domain. But therequirements of motor insurance are diff erent.2.Another potential knowledge gap for this researchpaper could be the lack of investigation into thepractical application and comparativeeffectiveness of the proposed joint SF-AHP andCoCoSo approach in real-world motor... |
1 Slightly lower importance (SL) (0.4, 0.6, 0.3) 15 Very low importance (VL) (0.2, 0.8, 0.1) 19 providing valuable insights into the relative importance and significance of each criterion within the decision-making framework. The rankings of the alternatives were also obtained. The CoCoSo algorithm is then applied to d... |
Degree of hesitancy C1 0.631 0.360 0.279 17.541 0.178 C2 0.609 0.384 0.282 16.867 0.171 C3 0.470 0.523 0.298 12.623 0.128 C4 0.377 0.608 0.285 9.871 0.100 C5 0.573 0.425 0.279 15.783 0.160 C6 0.472 0.520 0.306 12.631 0.128 C7 0.492 0.507 0.284 13.349 0.135 Table 6 — Normalized input decision matrix Criteria Alternative... |
C5 C6 C7 C1 EI SM SM HI SM SM HI EI SM HI HI SM SM HI EI SM HI HI SM SM HI C2 SL EI SM SM SM SM HI SL EI HI HI HI HI HI SL EI SM HI SM SM HI C3 SL SL EI SM SM SL SM LI LI EI HI LI SL SL LI SL EI HI LI SL SL C4 LI SL SL EI SL SL LI LI LI LI EI LI SL SL LI LI LI EI SL SL LI C5 SL SL SL SM EI HI SM SL LI HI HI EI HI HI SL... |
The consistency of the rankings across different scenarios enhances the confidence in the validity and reliability of the model, reinforcing the trustworthiness of the final decision outcome. Alternative 8 (A8) is consistently the better solution in all the cases of " λ". Consequently, based on the findings and results... |
MOTOR INSURANCE POLICY SELECTION WITH SF-AHP & COCOSO 189approach that combines the SF-AHP and CoCoSo methods, which have been scarcely studied in previous research in insurance policy selection domain. The practical implications are significant for both insurers and customers. Insurers can refine policies, increase cu... |
and Fuzzy Techni ques in Big Data Analytics and Decision Making: Proc INFUS 2019 Conf, Istanbul, Turkey (Springer International Publishing) (2020) 15–23. 22 Kahraman C & Gündogdu F K, Decision making with spherical fuzzy sets, Stud Fuzziness Soft Comput , 392 (2021) 3–25, 45461-6. 23 Joshi M & Deshpande V, Enhancing er... |
Vol-08 Issue 0 7, July -2024 ISSN: 2456 -9348 Impact Factor: 7.936 International Journal of Engineering Technology Research & Management Published By: IJETRM ( ) AWARENESS AND PERCEPTION OF MOTOR INSURANCE AMONG CAR OWNERS: A STUDY IN URBAN BANGALORE Mr. Rajesh K1 Mr. Abrar Hussain2 Dr. Muddasir Ahamed Khan N3 Mr. B N ... |
for various partners . On an individual car owner level , it is a game -changer because this mandate will highly affect their patterns of buying insurance policies and choosing providers . For protection companies, information about the awareness levels and intuitions of their target group can support the addition of t... |
choice within the don utility vehicle (SUV) portion, advertising experiences into buyer inclinations and patterns within the car i ndustry. Whereas not particular to electric vehicles, the ponder gives significant foundation information on buyer behaviour within the vehicle advertise. Jindal et al. (2022) explored two ... |
Bhat (2022) analyzed worldwide electr ic vehicle appropriation patterns and arrangement suggestions for India, advertising ex periences into the challenges and openings related to EV integration into the Indian transportation framework. Their think about highlighted the significance of arrangement back and framework im... |
Package for the Social Sciences (SPSS) software will be utilised for information examination. Qualitative Stage: In-depth Interviews In expansion to the study, in -depth interviews will be conducted with a subset of car owners to pick up more profound experiences into their mindfulness and perceptions of engine protect... |
interviews, this study points to supplying profitable experiences that can advise arrangement intercessions, buyer instruction activities, and industry hones aimed at upgrading engine protection proficiency and shopper assurance within the urban Bangalore setting. Stratum Population Size Margin of Error Confidence Leve... |
discretionary . Table 2: Perceptions of Motor Insurance Perception Percentage Motor insurance is essential 78 Motor insurance is optional 22 Factors Influencing Purchase Decisions: Table 3 shows the variables affecting car owners' buy choices with respect to engine protections. Cost emerged as the foremost noteworthy f... |
as the transcendent calculation affects participants' buy choices, with numerous communicating an inclination for reasonable premiums and deductibles . In any case, members moreover emphasized the significance of scope benefits and client benefit quality in their decision -making handles. Believe in protection supplier... |
the requirement for reasonable and open protection alternatives in the advertising . Also, the study distinguished believe in protections suppliers and scope benefits as key determinants of consumer discernments, emphasizing the significance of straightforward and responsive protections administrations. Moreover, the r... |
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