Comparative Predictive Performance of BPNN and SVM for Indian Insurance Companies

SVM are introduced into rolling bearings intelligent fault diagnosis due to the fact that it is hard to obtain enough fault samples in practice and the perfect performance of SVM. The two-class classifier performance of SVM is discussed under the different conditions with the combination of wavelet packet denoising, decomposition and SVM. The performance comparison of SVM and RBF neural networks is presented. The multi-class classification performance of SVM is researched with a novel method of PCA and SVM, and feature extracting is discussed. The experiment and analysis results show that SVM have perfect classified performance in only limited training samples and the diagnosis precision is less dependent on the kernel function and the parameter, which is suitable in the engineering applications. SVM also has better performance than RBF networks both in training speed and recognition rate. PCA method can effectively reduce the calculating complexity of the fault classifier and keep high diagnosis precision. The fault diagnosis method based on PCA and SVM can extract rolling bearing fault features effectively and recognize the fault pattern accurately.

The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this research, SVM classification method is used for lithology prediction from petrophysical well logs based on petrographic studies of core lithology in a heterogeneous carbonate reservoir in southwestern Iran. Data preparation including normalization and attribute selection was performed on the data. Well by well data separation technique was used for data partitioning so that the instances of each well were predicted against training the SVM with the other wells. The effect of different kernel functions on the SVM performance was deliberated. The results showed that the SVM performance in the lithology prediction of wells by applying well by well data partitioning technique is good, and that in two data separation cases, radial basis function (RBF) kernel gives a higher lithology misclassification rate compared with polynomial and normalized polynomial kernels. Moreover, the lithology misclassification rate associated with RBF kernel increases with an increasing training set size.

An investigation into the performance of SVM with linear kernel and features ranked by OLS, to discriminate infants with asphyxia from their cries, is presented in this paper. The features of the cry signal were first transformed into MFC coefficients. The input feature set was then used for classification by SVM with linear kernel. The number of coefficients and filter banks were tuned to acquire the optimal input feature set. This is uniquely different from previous works, where empirical values were simply adopted without proof. However, it is found that the performance of the classifier can be improved further by using selective coefficients from the optimal feature set. Hence, the MFC feature coefficients were then ranked in accordance to its error reduction ratio using OLS before submission to the classification stage. From experimental works, it was found that the optimal input feature set for DS-SVM approach is obtained with 20 coefficients, 21 filter banks and regularization parameter of 0.001 while the OLS-SVM approach reduced the MFC coefficients to 14. From performance comparison of both, it can be concluded that the OLS-SVM excelled the DS-SVM approach at classifying infant cry with asphyxia. This is because the OLS-SVM approach yields comparable classification accuracy (92.5%) with lesser support vector number (252.5) and lesser MFC coefficients (14) than the DS-SVM approach, which implicates much reduced computation effort and load.

Insurance companies are in the business of taking risks. Worldwide these companies write policies that deal with specific risks, and in many cases, even underwrite exotic risks. Therefore, obtaining coverage for every insurable risk is being replaced by the risk management concept. Risk management, which includes insurance coverage, is intended to minimise the costs associated with assuming certain types of risk and providing prudent protection. Managing risks is an important factor which insurance companies must attend to, if they are to achieve financial performance. From this perspective, the financial risk management has gained due importance for financial institutions and risk management has become one of the most important practices to be used especially in insurance companies in order to get higher returns. Therefore, this study is endeavoured to ascertain the relationship between financial risk and financial performance of insurance companies in India. The results of the multiple linear regression model reveals that capital management risk, solvency risk, liquidity risk, volume of capital and size of company are most important determinants of financial performance of life insurance companies in India, whereas ROA (proxy measure for financial performance) has statistically insignificant relationship with underwriting risk. The study led to the conclusion that 54.7 percent changes in financial performance of life insurance companies in India could be accounted for by changes in capital management risk, solvency risk, liquidity risk, underwriting risk, size of the company and volume of capital. The study established the fact that risks like capital management risk, solvency risk and underwriting risk are the deterrent factors for the financial performance of life insurance companies in India. The study also led to the conclusion that size of a company, volume of capital and more surprisingly liquidity risk are the pull factors for the financial performance of life insurance companies. On the basis of these findings, the study recommends that there is greater need for life insurance companies in India to manage the risks particularly capital management risk and solvency risk more effectively. The study also recommends that there is a dire need for insurance companies in India to increase their size by enhancing their assets base since it was found that size is an important factor influencing their competitive power and financial performance.

Initiated by that the quality of training data may affect the model selection, this paper presents a method to improve the prediction performance of SVM through pruning the set of SV. That is, using a global comparable noise measure based on neighbor distribution information to identify noisy SVs, and weaken their role in training. The difference of this method from traditional one is that it need not to process noise for every instance in training set, and but only for those in SVs. The experiment result shows that when top noisy SVs are weakened the prediction performance of SVM is better for most categories.

The kernel function in SVM enables linear segmentation in a feature space for a large number of linear inseparable data. The kernel function that is selected directly affects the classification performance of SVM. To improve the applicability and classification prediction effect of SVM in different areas, in this paper, we propose a weighted p-norm distance t kernel SVM classification algorithm based on improved polarization. A t-class kernel function is constructed according to the t distribution probability density function, and its theoretical proof is presented. To find a suitable mapping space, the t-class kernel function is extended to the p-norm distance kernel. The training samples are obtained by stratified sampling, and the affinity matrix is redefined. The improved local kernel polarization is established to obtain the optimal kernel weights and kernel parameters so that different kernel functions are weighted combinations. The cumulative optimal performance rate is constructed to evaluate the overall classification performance of different kernel SVM algorithms, and the significant effects of different p-norms on the classification performance of SVM are verified by 10 times fivefold cross-validation statistical comparison tests. In most cases, the results using 6 real datasets show that compared with the traditional kernel function, the proposed weighted p-norm distance t kernel can improve the classification prediction performance of SVM.

PurposeThe purpose of this article is to evaluate revenue efficiency performance of life insurance companies in India. The study also compares if private or public insurance sector is more “revenue efficient”. Furthermore, the study determines the nature of return to scale (RTS) and identifies the leaders and laggards amongst insurance companies operating in India.Design/methodology/approachRevenue efficiency is calculated by employing data envelopment analysis – a non-parametric approach, on a data set of 24 insurance companies over the period 2013–2014 to 2017–2018.FindingsThe empirical results suggest that life insurance companies in India could generate only 34.4% of revenue, which is very less than what these are expected to generate from the same inputs. Majority of life insurance companies operating in India are operating at decreasing return to scale (DRS). There is a reduction in leaders and the highest proportion of companies is falling in the category of laggards.Originality/valueAs per the best knowledge of researchers, no empirical work has been carried out with respect to measuring the revenue efficiency of Indian insurance companies. The current study appropriately fills the gap by not only calculating the revenue efficiency scores of insurance companies in India but also provides insights into the causes of revenue inefficiencies. It also gives implications for efficient and effective management of insurance companies.

Recently, machine learning algorithms have been extensively utilized in resistance spot welding (RSW) applications to develop non-destructive weldability assessment systems to predict nugget width of RSW joints. However, different predictive models have different prediction performance that can be highly inconsistent. It is critical to compare predictive models and determine the efficient model(s). To the best of our knowledge, a comprehensive analysis and systematic prediction performance comparison of RSW nugget width prediction models have not been performed. This paper presents a statistical performance comparison methodology based on bootstrapping and hypothesis testing techniques to systematically compare the prediction performance of predictive models and determine the efficient model(s). Also, a deep neural net (DNN) nugget width prediction model is developed, analyzed, and compared with prior models. Bootstrapping is applied to generate sampling distributions for each predictive model, and statistical comparison tests are employed to analyze and compare the performance of each predictive model and identify statistically significant performance differences. Results of this analysis indicate that DNN, developed for RSW nugget width prediction in this paper, outperforms previous models.

PurposeThe purpose of this study is to examine the extent to which Indian insurance companies have adapted socially responsible reporting practices, HR disclosures and also to identify areas of corporate social reporting and HR disclosures.Design/methodology/approachThe study adopts longitudinal design and has analyzed qualitative data by using content analysis in 26 insurance companies in India disclosed in annual reports. The paper focuses on annual reports of Indian insurance companies starting from the financial year 2002‐2003 to 2009‐2010, which are analyzed with regard to the nature of their human resource disclosures and social reporting. Finally, a test of legitimacy theory is then conducted.FindingsThe study found that the non‐life insurance companies disclosed significantly less social information than life insurance companies. The study also reveals that public life insurers disclosed significantly more social information than the other life insurance companies. On the other hand, a paired difference t‐test shows private general insurance companies disclose more social information as the difference significant.Research limitations/implicationsThe study has ignored longitudinal variations and the sample organizations comprised 26 government and private insurance organizations, restricting generalizations to the companies examined in the study.Practical implicationsThe study recommends that a checklist of disclosures should be developed jointly by the Insurance Regulatory and Development Authority (IRDA), the Institute of Chartered Accountants of India (ICAI), and the Securities and Exchange Board of India (SEBI).Originality/valueThe study, while providing valuable insights, highlights the dearth of research of corporate social reporting in emerging economies and opens up many avenues for further research.

Deep vein thrombosis (DVT) is one of the common complications after joint replacement, which seriously affects the quality of life of patients. We systematically searched nine databases, a total of eleven studies on prediction models to predict DVT after knee/hip arthroplasty were included, eight prediction models for DVT after knee/hip arthroplasty were chosen and compared. The results of network meta-analysis showed the XGBoost model (SUCRA 100.0%), LASSO (SUCRA 84.8%), ANN (SUCRA 72.1%), SVM (SUCRA 53.0%), ensemble model (SUCRA 40.8%), RF (SUCRA 25.6%), LR (SUCRA 21.8%), GBT (SUCRA 1.1%), and best prediction performance is XGB (SUCRA 100%). Results show that the XGBoost model has the best predictive performance. Our study provides suggestions and directions for future research on the DVT prediction model. In the future, well-designed studies are still needed to validate this model.

The comparative performance of models predicting patient and graft survival after kidney transplantation: A systematic review.

In the telemedicine industry, data security is a prime aspect because medical data is frequently transferred and stored over networks. In such scenario watermarking emerges as an important technique that ensures data security and integrity. In this work, use of SVM and ANN is investigated for a high capacity ECG data hiding technique. Watermarking is done using prediction error expansion (PEE). SVM and ANN are used to predict the ECG values. Two separate regression SVM models with linear kernel and Gaussian kernel are trained. ANN model used is a feed-forward deep neural network with three hidden layers. The proposed method provides a completely reversible watermarking. The patient’s confidential data is used as a watermark. For testing and training of algorithms, signals from MIT-BIH arrhythmia database are used. Performance of both SVM and ANN-based prediction models are evaluated using normalized cross-correlation, signal-to-noise ratio, and percentage residual difference. Linear SVM and deep ANN are comparable in performance and are better than Gaussian SVM.

There are currently, a total of 24 life insurance companies in India. Of these, Life Insurance Corporation of India (LIC) is the only public sector insurance company. All others are private insurance companies. The Life Insurance Corporation of India (LIC) is the largest life insurance company in India and also the country's largest investor. More and more new private insurance companies are coming up year after year. And, these new and private life insurance companies adopt aggressive marketing strategies to introduce their products and to tap the potential policyholders. It is witnessed that new policies like ULIPs are introduced by these new private life insurance companies. It is in this concept this study has been undertaken to assess and analyze the preference of policyholders towards insurance services offered by public and private life insurance companies in Tirunelveli district.

The Servicesector is contributingaround 55 % to India’s Gross Domestic Product (GDP) and, in the process, has become the growth of engine of the economy. The players are in the servicesector include in industries / companies offering intangible services like, Banking,Finance, Insurance, Healthcare,Tourism, Telecommunications, Hospitality, IT, BPO and Real estate. Their relative contribution to the GDP varies depending upon their capabilities, competitive efficiency and management practices. Among the various services, Insurance sectors play a major roleof offering financial protection of individuals as well as companies.To be successful in the market theInsurance companies in India have to adopt well defined managementpractices of integrating the AI powered tools in their strategies. In the broadspectrum of AI -enabled tools will help the Insurance companies to streamline the handling of customer relationship; processing claims in an automate manner; enhance risk assessment matrices;updating marketing trends and optimization of operations. The adoption of AI help to improve in reduction costs. Ultimately, the customers experience a superior service. The present highlight salient features of AI and to explore the degree of adoption of AI tools in the management practices of insurance companies;and to examine the challenges / barriers in the extensive adoption of AI tools in the Indian Insurance Sector. By collecting information and data from the published sources an analysis has been done. The preliminary results revealed the fact that there has been a great scope for the integration of AI enabled management practices by the Insurance Companies in India to improve their efficiency on the one hand and provision of quality services to the customers on the other.

In India, insurance is a national matter in which life insurance and general insurance are yet booming sectors with vast opportunities for different global companies, Life insurance premiums account for 2.5% and general insurance premiums account for 0.65% of India‘s GDP. The Indian insurance sector has gone through several phases and changes, especially after 1999/2000 when the Government of India opened up the insurance sector for private companies to solicit insurance, allowing FDI up to 26%. Since then, the insurance sector in India is considered as a flourishing market amongst global insurance companies. In March 2013, the FDI limit has been raised from 26% to 49%. This long pending step will definitely boost the confidence of global insurers and investors. However, the largest life insurance and general insurance companies in India are still owned by the government. The foreign insurance players are, however, quite capable of grabbing the vast opportunities and successfully meet challenges, if any.