Ghadir Mahdavi
Abstract
This study explores the optimal trajectory of life insurance demand, a crucial financial tool for managing mortality risk and ensuring economic security for family. Various factors, including mortality risk, wealth growth, interest rates, and policyholder preferences, influence insurance decisions. To ...
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This study explores the optimal trajectory of life insurance demand, a crucial financial tool for managing mortality risk and ensuring economic security for family. Various factors, including mortality risk, wealth growth, interest rates, and policyholder preferences, influence insurance decisions. To analyze these dynamics, the study develops two mathematical models. The first is a single-period, state-dependent model that maximizes expected utility under budget constraints, concluding that individuals optimally purchase only partial insurance. To obtain the optimal time path of life insurance coverage, a life-cycle model was solved using optimal control theory. By maximizing expected lifetime utility from consumption and bequests within the wealth accumulation process, the second model derives the trajectory of life insurance demand. The results indicate that individuals with higher risk tolerance experience a greater growth rate in life insurance demand. This growth rate is also positively influenced by mortality rates, loading factors, and interest rates. Conversely, life insurance demand declines as wealth increases, supporting the notion that wealth acts as a substitute for life insurance. Additionally, a higher rate of time preference negatively impacts the growth rate of life insurance demand.
Abbas Raad; Reza Ofoghi; Ghadir Mahdavi
Abstract
This study aims to examine the function of blockchain technology to detect fraud in health insurance. we consider the literature on fraud in health insurance, blockchain, and smart contracts to to test a newly structured software system based on blockchain technology for this purpose. Different blockchain ...
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This study aims to examine the function of blockchain technology to detect fraud in health insurance. we consider the literature on fraud in health insurance, blockchain, and smart contracts to to test a newly structured software system based on blockchain technology for this purpose. Different blockchain platforms, consensus algorithms, and structures have been used to pick the proposed system’s best structure based on blockchain. Eventually, the best techniques to put the system to the test and evaluate the findings were assessed. we propose a standardized system, where blockchain is applied to store data and smart contracts are used to automate insurance policies. Furthermore, a web-based application, which acts as core insurance software, is proposed for all stakeholders to communicate with the blockchain and smart contracts. Therefore, the proposed system comprises a blockchain, web app, and standardized smart contracts. The proposed system mainly focuses on fraud detection in insurance claims while maintaining a standard data storage and transfer structure. The system proved to be thriving once claim data can be created, read, and analyzed (i.e. fraudulent data are caught) effectively in a standard way. The web app consists of a front-end and back-end section. The front-end enables users to interact with the proposed system, and the back-end allows the insurance company to store records on the blockchain and increase the chances of detecting fraud in insurance claims, especially Digital Insurance Claims. Finally, a blockchain-based web application that can be used as core insurance software for any health insurance company is proposed.
Parissa Ghonji; Ghadir Mahdavi; Mitra Ghanbarzadeh
Abstract
Insurance companies regularly estimate loss reserves due to delays in settling claims. These delays depend on the time taken from claim filing to settlement. The study aims to estimate reported loss reserves through cross-sectional regression using cargo insurance market data. The model considers written ...
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Insurance companies regularly estimate loss reserves due to delays in settling claims. These delays depend on the time taken from claim filing to settlement. The study aims to estimate reported loss reserves through cross-sectional regression using cargo insurance market data. The model considers written premiums, paid claims, reinsurance issued premiums, inflation rates, and return on investment. The analysis demonstrates a nonsignificant negative association between inflation rates and loss reserves, as well as a negative correlation between paid claims and loss. While revealing a statistically significant positive relationship between written premiums and loss reserves.
