Explainable AI and Interpretable Model for Insurance Premium Prediction

Traditional machine learning metrics(TMLMs)are quite useful for the current research work precision,recall,accuracy,MSE and RMSE.Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%–92%.We included in the prediction process,machine learning models(MLMs)with greater than 99%accuracy with a sensitivity of 95%–98%and specifically in the database.We need to explain the model to domain specialists through the MLMs.Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model.This is achieved by creating a model-independent,locally accurate explanation set that makes it better than the primary model.As we know that human interaction with machine learning systems on this model’s interpretability is more crucial.For supporting set validations in model selection insurance premium prediction.In this study,we proposed the use of the(LIME and SHAP)approach to understand research properly and explain a model developed using random forest regression to predict insurance premiums.The SHAP algorithm’s drawback,as seen in our experiments,is its lengthy computing time—to produce the findings,it must compute every possible combination.In addition,the experiments conducted were intended to focus on the model’s interpretability and explain its ability using LIME and SHAP,not the insurance premium charge prediction.Three experiments were conducted through experiment,one was to interpret the random forest regression model using LIME techniques.In experiment 2,we used the SHAP technique to interpret the model insurance premium prediction(IPP).

Cyberattack Ramifications, The Hidden Cost of a Security Breach

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

Insurance ratemaking method for risk of construction diversion project

Based on analyzing risk factors of diversion project,synthetic risk rate and engineering insurance period,the frequency and distribution law of loss are researched on the grounds that foundation pit is submerged after diversion project ceases to be effective.And then,the standpoint that these total loss is subject to non-homogeneous compound Poisson processes is put forward.Furthermore,the collective risk model of the total loss about engineering insurance is established on the basis of construction diversion project risk.Ultimately,insurance ratemaking method for construction engineering risk and its mathematical expression are presented,which provides theoretical method for the insurance ratemaking of hydropower engineering to some extent.

Based on the human capital perspective to explore the social insurance premium is influence on the behavior of enterprises to participate in social insurance

To participate in social insurance is a major social responsibility of enterprises, even it is a mandatory social policy, companies still escaped pay society insurance. The serious consequences of this phenomenon become increasingly prominent.it not only damaged the justice of labor market, but also violated the staffs interest. Various factors influence enterprise to participate in social insurance behavior .Human capital is the impact of insurance companies. This paper is mainly focusing on the theory analysis of different human capital participating behavior of enterprises. From the perspective of human capital, using the cost-benefit theory and rational choice theory in economics to explore participating behavior, enterprise employees of different human capital differences in attitude to attend social insurance. No matter enterprises with higher or lower human capital, enterprises and employees choose to participate in social insurance act consistently. But, it can improve participate of low human capital enterprise behavior when payment rate reduced. So, it is suggested that establishing a lower level of social insurance system is helpful to expand coverage, it can through e^tahllgh the sunnlementarv insurance system meet the needs of more people.

How Do Rising Labor Costs Affect China’s Industrialization?

China’s uninterrupted high-speed economic growth depends on promoting the process of industrialization.Based on provincial data during 2003-2016,this paper empirically estimates the impact of labor costs on China’s industrialization and examines its mechanism and path.The results show that the rising labor costs such as wages and social security premiums may lead to China’s“de-industrialization”.With endogeneity excluded,the conclusion still holds.The following policy suggestions are proposed:increasing the effective labor supply in the industrial sector to slow down the increase in wages,implementing social insurance premium reform to reduce social security premium rate,reversing the situation of investment“fl eeing from the real economy and into the virtual economy”in a multi-pronged manner,and optimizing the combination of labor,capital and technology in various ways.