Risk Assessment and Underwriting Practices in General Term Insurance in Mumbai region.

"A vital part of the nation's financial system, the insurance sector in Sri Lanka is currently negotiating stakeholder expectations, regulatory restrictions, and competitive pressures. Integrating Environmental, Social, and Governance (ESG) principles has become increasingly popular worldwide to enhance financial performance and ensure long-term sustainability. On the other hand, nothing is known about the effects of ESG practices on Sri Lankan insurers' financial results. From 2013 to 2022, this study examines the connection between 26 insurance companies in Sri Lanka and their financial performance as shown by Return on Assets (ROA) and sustainable practices using a quantitative research design. These businesses fall within the general and life insurance categories. In particular, the study investigates how the ESG factors impact these industries' financial results. Results show that social responsibility programs have a significant impact on ROA for both life and general insurance firms, underscoring the crucial role that social practices play in influencing financial performance. On the other hand, it was discovered that environmental or governance issues did not statistically influence ROA. There are apparent sectoral differences: general insurers place more emphasis on governance issues, while life insurers typically give priority to social and environmental norms. Additionally, correlation analysis reveals fewer correlations in general insurance and moderate associations in the life insurance industry. The study emphasises how crucial it is to incorporate social initiatives like employee welfare and community involvement into fundamental business plans to boost financial performance. It suggests enhancing sustainability reporting guidelines and creating industry-specific sustainability guidelines adapted to the unique needs of general and life insurance companies. The study offers valuable insights into how sustainable practices can impact financial outcomes in Sri Lanka's insurance industry, despite its limitations, which include its reliance on secondary data and the use of only one financial performance metric (ROA). These results demonstrate the growing importance of ESG integration in achieving industry competitiveness and long-term viability. Keywords: environmental practices, governance practices, return on assets, social responsibility practices, Sri Lanka’s insurance sector, sustainability practices "

Markets for many types of property and casualty insurance exhibit soft-market periods, where premium rates are stable or falling and coverage is readily available, and subsequent hard-market periods, where premium rates and insurers' reported profits significantly increase and less coverage is available. Conventional wisdom among practitioners and other observers is that soft and hard markets occur in a regular "underwriting cycle." Like price fluctuations in equity markets, fluctuations in insurance premium rates and coverage availability are difficult to explain fully by standard economic models that assume rational agents and few market frictions. The mid-1980s "liability insurance crisis" remains the most infamous hard market in the United States. The dramatic increases in commercial liability insurance premiums and reductions in coverage availability for some sectors received enormous attention, motivating extensive research on those specific problems and on fluctuations in insurance prices and coverage availability more generally. Large losses from natural catastrophes in the United States during the late 1980s and early 1990s spurred further interest in and research on the dynamics of pricing in reinsurance and primary insurance markets following large, industry-wide losses. The hard market for commercial property and casualty insurance that began in late 2000 and accelerated following the destruction of the World Trade Center in September 2001 focused renewed attention on markets for commercial [End Page 97] property, medical liability, general liability, and workers' compensation insurance. With respect to general liability and medical liability insurance, substantial debate has arisen concerning the causes of increases in rates and reductions in coverage availability and the attendant implications for policy, such as tort reform, to reduce the expected value and uncertain costs of liability insurance claims or additional regulation of insurers to control allegedly imprudent underwriting and investment. This paper provides an overview of volatility in premiums, coverage availability, and insurers' reported profits in U.S. commercial general liability insurance and examines its broad relation to the U.S. tort liability system.1 I begin with a synopsis of the perfect markets model of insurance prices (sometimes called the arbitrage model) and its implications for commercial liability insurance. I next describe fluctuations in U.S. general liability insurance premiums, coverage availability, and reported profits during the past two decades and examine whether the perfect markets model is consistent with that evidence. I then summarize other factors that may affect insurance market volatility and provide new evidence concerning one alternative: that aberrant pricing by some firms aggravates soft markets and, by implication, the severity of subsequent hard markets. I conclude with a brief summary of policy implications and areas for future research. Competitive Insurance Premiums with Frictionless Capital Markets With rational insurers and policyholders, competitive insurance markets, and frictionless capital markets, insurance premiums will equal the risk-adjusted discounted value of expected cash outflows for claims, sales expenses, income taxes, and any other costs, including the tax and agency costs of capital. The levels and changes in premium rates will coincide with the levels and changes in discounted expected costs. Because the timing of payouts for claims incurred in a given year, nonclaim expenses, and capital costs should be comparatively stable over time, rate changes will primarily reflect changes in expected (forecast) claim and claim settlement costs and changes in interest rates. [End Page 98] In this perfect markets framework, long-run levels and short-run changes in premium rates for general liability insurance will reflect levels and changes in the following: Expected claim costs (incurred losses) and claim settlement costs, The timing of future claim payments for incurred losses, Interest rates used to discount expected future claim and claim settlement costs, Underwriting expenses (commissions, wages to underwriters, policy issue costs, premium taxes, and so on), Uncertainty about the frequency and severity of claims, including uncertainty about the form and parameters of the relevant probability distributions, which in turn affects the amount of capital that insurers need to hold to protect themselves against...

This study examines the important role of leadership in driving change and innovation in the general insurance sector through a systematic literature review. By analyzing recent studies, we explore how leaders in the general insurance industry can effectively facilitate organizational transformation and foster a culture of innovation amid digital disruption and rapid market change. Through a systematic analysis of 43 scientific articles published between 2014-2024, the study identifies several key themes, namely visionary, adherent, and transformational leadership styles as catalysts for change, the importance of creating a culture of innovation, the use of technology and data analytics in product and service innovation, strategies for managing resistance to change, and developing organizational capabilities to innovate sustainably. We also examined the specific challenges insurance leaders face in managing change, such as technology disruptions, customer expectations, regulatory constraints and employee resistance. The findings show that leaders who successfully adopt a visionary, adaptive and transformational and human-centered leadership approach tend to be more effective in driving innovation and managing organizational change. The study provides insurance executives and policymakers with valuable insights into leadership strategies that can improve the competitiveness and sustainability of general insurers in the digital age. We also identify gaps in the existing literature and suggest directions for future research to better understand the dynamics of leadership in the context of innovation in the general insurance industry.

Predictive risk assessment and risk stratification models based on geodemographic postcode-based consumer classification are widely used in the pension and life insurance industry. However, these are static socio-economic models not directly related to health information. Health information is increasingly used for annuity underwriting in the UK, using health status when the annuity is purchased. In real life, people develop new health conditions and lifestyle habits and can start and stop a certain treatment regime at any time. This requires the ability to dynamically classify clients into time-varying risk profiles based on the presence of evolving health-related conditions, treatments and outcomes. We incorporate landmark analysis of electronic health records (EHR), in combination with the baseline hazards described by Gompertz survival distributions, for dynamic prediction of survival probabilities and life expectancy. We discuss a case-study based on landmark analysis of the survival experience of a cohort of 110,243 healthy participants who reached age 60 between 1990–2000.

With the increasing reliance on web applications for business and personal use, ensuring website security has become a critical concern. Cyber threats such as SQL injection, cross-site scripting (XSS), malware infections, and unauthorized access pose significant risks to websites, leading to data breaches and service disruptions. This project aims to develop a comprehensive website security scanner that systematically identifies vulnerabilities and potential security risks.The proposed system integrates automated vulnerability scanning, penetration testing techniques, and real-time monitoring to detect security loopholes. Using machine learning and heuristic-based analysis, the scanner can identify malicious scripts, outdated software versions, weak authentication mechanisms, and misconfigured security policies. The system also performs network security assessments, analyzing potential DDoS (Distributed Denial-of-Service) attack risks and firewall configurations. The scanner generates detailed security reports, providing actionable insights and recommendations for website owners and administrators to mitigate risks effectively. Designed for continuous monitoring and proactive defense, the tool enhances cybersecurity resilience against evolving threats. This project contributes to web security advancements by offering an intelligent, automated, and scalable solution for safeguarding websites from cyberattacks. Keywords: Website Security | Vulnerability Scanner | Cyber Threats | SQL Injection | Cross-Site Scripting (XSS) | Penetration Testing | Machine Learning | Malware Detection | DDoS Protection | Authentication Security | Firewall Analysis | Web Application Security | Risk Assessment | Cybersecurity Resilience

Cyber security risk assessment for seaports: A case study of a container port

Risk assessment is a crucial element in the life insurance business to classify the applicants. Companies perform underwriting process to make decisions on applications and to price policies accordingly. With the increase in the amount of data and advances in data analytics, the underwriting process can be automated for faster processing of applications. This research aims at providing solutions to enhance risk assessment among life insurance firms using predictive analytics. The real world dataset with over hundred attributes (anonymized) has been used to conduct the analysis. The dimensionality reduction has been performed to choose prominent attributes that can improve the prediction power of the models. The data dimension has been reduced by feature selection techniques and feature extraction namely, Correlation-Based Feature Selection (CFS) and Principal Components Analysis (PCA). Machine learning algorithms, namely Multiple Linear Regression, Artificial Neural Network, REPTree and Random Tree classifiers were implemented on the dataset to predict the risk level of applicants. Findings revealed that REPTree algorithm showed the highest performance with the lowest mean absolute error (MAE) value of 1.5285 and lowest root-mean-squared error (RMSE) value of 2.027 for the CFS method, whereas Multiple Linear Regression showed the best performance for the PCA with the lowest MAE and RMSE values of 1.6396 and 2.0659, respectively, as compared to the other models.

Introduction Jensen (1986) defines free cash flow as cash in excess of that required to fund all positive net present value projects. Free cash flow tempts managers to expand the scope of operations and the size of the firm, thus increasing managers' control and personal remuneration, by investing free resources in projects that have zero or negative net present values. These unprofitable investments are an aspect of the basic conflict of interest between owners and managers. Jensen argues that some industries are particularly susceptible to the generation of free cash flow, and we posit that life insurers constitute a low-growth industry that is likely to generate such excessive cash flow. We argue that, in the life insurance industry, wasteful uses of free cash flow occur to the detriment of the firm's owners and policyholders. Managerial abuses of free cash flow are inconsistent with the goal of owner wealth maximization. Expenditures wasted by management instead could have been distributed to the owners of stock insurers as cash dividends or to the policyholders of mutual or stock firms in the form of lower premiums, higher policy dividends, or higher investment returns.(1) While regulators primarily focus on insurer solvency, they also are concerned with maintaining premium rates that are not excessive. To the extent that regulators monitor life and health insurance rates, dividends, and surrender values, free cash flow should be of concern to them. Mayers and Smith (1981) predict more severe owner-manager conflicts in mutual insurers than in stock insurers, and we test their prediction using data from the life insurance industry. Specifically, we test for differences in free cash flow between stock and mutual insurers in the U.S. life insurance industry.(2) Focusing on the life insurance industry allows us to isolate firms having similar investment opportunity sets and differing organizational forms, reduce measurement error in our proxy for free cash flow because of the limited variation in accounting techniques used across the industry, and control for the confounding effects between the investment opportunity set and free cash flow that arise in cross-industry studies. The majority of existing evidence on the free cash flow hypothesis focuses on intertemporal changes in financial structure. Jensen posits that leveraged buyout activities are one way of controlling free cash flow because the debt incurred in such transactions forces managers to disgorge excess cash. Evidence supporting the free cash flow motivation for financial restructuring has been provided by many authors, including Loh (1992), Gupta and Rosenthal (1991), Lehn and Poulsen (1989), Gibbs (1993), Griffin (1988), and Moore, Christensen, and Roenfeldt (1989). Byrd (1988) examines the cross-sectional relation between free cash flow and ownership structure and finds some evidence that organizational forms specific to the oil industry (corporations, master limited partnerships, and royalty trusts) have different agency costs of free cash flow. Specifically, the agency costs of free cash flow are lower in royalty trusts and master limited partnerships than in corporations. Although previous empirical work is generally consistent with the free cash flow hypothesis, the financial industry has been ignored in most studies beo cause of its unique regulatory environment. The insurance industry, in particular, has distinctly different organizational forms not found in other industries, thus providing a natural laboratory for examining cross-sectional differences in organizational form (Mayers and Smith, 1990).(3) In this study, we test for differences in free cash flow between stock and mutual insurers in the U.S. life insurance industry. Our purpose is to examine whether organizational form affects managerial behavior with respect to the holding of free cash flow. Our results also offer insight into whether contractual limitations of managerial discretion fully compensate for the losses of debt market bonding and the market for corporate control. …

FedChain-Hunter: A reliable and privacy-preserving aggregation for federated threat hunting framework in SDN-based IIoT

Cybersecurity risk assessment in banking is the process of identifying, analyzing, and evaluating the cyber threats and vulnerabilities that may affect the confidentiality, integrity, and availability of the information systems and data of banks and their customers. Cybersecurity risk assessment in banking helps banks to prioritize and implement appropriate controls and measures to mitigate the cyber risks and to comply with the relevant regulations and standards. This study focusses on identifying effective risk assessment strategies, highlighting how they can be adapted and applied in various banking environments, especially in developing economies like Nigeria. As the banking industry continues to evolve in the digital era, the significance of robust cybersecurity measures cannot be overstated. This paper delves into the critical domain of Cybersecurity Risk Assessment in Banking, exploring various methodologies and best practices employed to safeguard financial institutions against evolving cyber threats. The dynamic landscape of cyber risks faced by banks, ranging from sophisticated malware and phishing attacks to insider threats and system vulnerabilities are examined. The paper provides an in-depth analysis of established and emerging methodologies for conducting effective cybersecurity risk assessments in the banking sector. It explores quantitative and qualitative risk assessment approaches, threat modeling, and scenario analysis, shedding light on their respective strengths and limitations. Moreover, the document highlights the importance of aligning risk assessment methodologies with industry regulations and compliance standards to ensure a comprehensive and regulatory-compliant cybersecurity framework. Best practices for cybersecurity risk management in banking are scrutinized, emphasizing the integration of proactive threat intelligence, continuous monitoring, and incident response planning. The role of advanced technologies, including artificial intelligence and machine learning, in enhancing the efficiency of risk assessment processes is also discussed. Furthermore, the paper addresses the human element in cybersecurity, emphasizing the significance of training and awareness programs to mitigate risks associated with human error and social engineering attacks. By synthesizing insights from industry practices, regulatory guidelines, and technological advancements, this paper offers a comprehensive guide for banking professionals, cybersecurity practitioners, and policymakers involved in fortifying the resilience of financial institutions against cyber threats. Ultimately, the research aims to contribute to the ongoing discourse on cybersecurity risk assessment in banking, providing actionable insights to navigate the complex landscape of digital risks and ensuring the continued trust and security of the financial ecosystem. Keywords: Cybersecurity; Risk Assessment; Banking; Methodologies; Cyber Threat; Artificial Intelligence; Best Practices

Although actuaries have been connected with life insurance business for more than 100 years, it is only more recently that actuarial activities in the non-life branches have taken place.The problem of calculating level premiums for the steadily increasing risk in life insurance immediately calls for mathematical ability. The need for mathematically trained specialists is not so easily seen when the problem is to calculate premium rates for fire and casualty insurances. The premium rates in this field have to a great extent been fixed by persons with none or very little knowledge in mathematics and mathematical statistics. When the market conditions allow for considerable margins this arrangement works out very well. The statistical investigation needed later on for controlling the premium rates is not very complicated, and the premium rates can if necessary be changed on short notice. A mistake made in the premium stipulation, will generally not have a lasting effect. In life insurance on the other hand, the contracts often stretch over many years and a mistake in the premium stipulation cannot easily be corrected. These circumstances explain the reason why actuaries have been more often engaged in life insurance than in the fire and casualty branches.But a closer study shows that from a mathematical point of view, the proper fixing of premium rates is often more complicated in the fire and casualty branches than in life. In the fire and casualty field a steady changing of market conditions seems to take place. The competition grows keener and keener, and the margins are cut down. Sooner or later we reach a point where it is of vital importance to know as exactly as possible where we stand. This means that we have to study the risk premium, the security loading and the expense loading separately, and to estimate these quantities with the highest efficiency.

Background and objective: Hypertension and Cardiovascular Diseases (CVD) are major global public health issues, leading to a large impact on illness and death rates. Prompt and precise identification of these diseases is essential for efficient management and avoidance of complication. This study aims to examine the significance of clinical characteristics in the diagnosis of cardiovascular diseases and hypertension. Identification and categorization of risk are crucial for prompt interventions and efficient management of these widespread and influential situations. The diagnostic techniques are based on clinical aspects, which include blood pressure measures, lipid profiles, family history, lifestyle choices, and comorbidities. Methods: The review evaluates the validation and calibration of clinical characteristics, drawing from robust data obtained through extensive cohort studies, risk prediction models, and adherence to defined criteria. It examines the role of measuring blood pressure, lipid profiles, and lifestyle habits in efficiently identifying individuals at risk of developing hypertension and CVD. Additionally, the review explores the incorporation of technology innovations, such as wearable devices, mobile apps, and artificial intelligence, in improving the diagnostic process. Results: This study shows that measuring blood pressure, lipid profiles, and lifestyle habits may efficiently detect individuals who are susceptible to develop hypertension and CVD. It is crucial to calibrate clinical characteristics in order to guarantee their precision and dependability in predicting the probability of hypertension and cardiovascular disease. This method entails enhancing risk assessment systems to include population-specific attributes and dynamic disease patterns. The incorporation of technology innovations, such as wearable devices, mobile apps, and artificial intelligence, has significantly improved the process of diagnosing clinical features, and the accuracy of predicting clinical parameters, making it easier to measure individualized risk and diagnose hypertension and cardiovascular disease at an early stage. Conclusion: Incorporating verified clinical characteristics into risk assessment tools and prediction models, together with advancements in technology, has the capacity to enhance the early identification and individualized treatment of these illnesses. Ongoing research and innovation in this sector are crucial to improve diagnostic methods and increase the accuracy of clinical feature-based diagnosis for hypertension and cardiovascular disease.

Life and general insurance are essential element for every individual today. Risk is a constant element in this everchanging environment. The facets of risk change with the changing scenario but the factor that the human life and economic activities are always subject to risk remains constant. Insurance is required to manage risk confronting every activity associated with life. Indian insurance industry has seen tremendous change over a period time. Many amendments have been made to the existing Insurance Act to enhance the quality of services and provide better risk coverage to the policy holders. However, many more of these reforms are required as insurance industry still has a long mile to cover. The contribution of Insurance sector at various levels has been growing. Let it be personal or commercial, the insurance sector and its contribution has been playing a pivotal role to the growth of GDP and in micro and macro-economic development of the nation. The Indian Insurance sector penetration is 3.7 % of the GDP. The Life Insurance industry has recorded a growth of 11 – 12% p.a., growth for the previous year, while general insurance has recorded a growth of 18% p.a. On the global front, the world insurance sector penetration is 6.31 % to world GDP, hence Insurance experts are of view that better Government initiatives can reduce the gap and can make insurance more affordable to all

In an increasingly interconnected world, cybercrime and data privacy challenges have escalated into critical global issues, threatening governments, organizations, and individuals alike. The proliferation of sophisticated cyberattacks, including ransomware, data breaches, and nation-state hacking, underscores the urgent need for robust cybersecurity governance. Compounding these threats are evolving regulatory landscapes and a lack of harmonized international frameworks, leaving gaps in addressing cross-border cybercrimes and ensuring data privacy. This article explores the imperative of strengthening cybersecurity policy frameworks to combat global cybercrime and safeguard sensitive data. It begins with an overview of the current cybersecurity governance landscape, highlighting gaps and inconsistencies in policy enforcement. Emphasis is placed on the integration of adaptive regulatory mechanisms, public-private partnerships, and standardized global practices to ensure a unified response to cyber threats. Key strategies discussed include the adoption of proactive risk assessment methodologies, the implementation of privacy-by-design principles, and the enhancement of international cooperation for intelligence sharing and joint cyber defense initiatives. The article also delves into case studies illustrating the effectiveness of comprehensive policy frameworks in mitigating cyber risks and fostering organizational resilience. As cyber threats continue to evolve, addressing these challenges requires a coordinated and forward-looking approach that balances innovation with security. By advancing cybersecurity governance, stakeholders can strengthen trust in digital ecosystems, safeguard privacy, and ensure the continuity of global digital operations.

Impact of Decision-Support On Clinicians' Assessment of Febrile Neutropenia Risk.