Automated Actuarial Data Analytics-Based Inflation Adjusted Frequency Severity Loss Reserving Model

In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based Automated Actuarial Loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive Automated Actuarial Loss Reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.

An Inside View of Microcredit in Panama： The Financial Investment in MFIs

The idea of this survey on microfinance in Panama arises from the importance of this financial sector in Latin America, in particular this paper analyzes the system of microcredit and estimate, among other things, the financial needs of the institutions of microcredit as a potential target of international investment. The present paper aims to provide a cognitive framework on microcredit in Panama. It wants to demonstrate how this country can be an area in which international investors can operate considering that this form of credit can improve their yields especially in countries, such as Panama, characterized by high interest rates. Under the methodological point of view, the paper considers the financial variables of microcredit in Panama. In this way, it reaches the goal of the research to identify MFIs （micro finance institutions） as a potential target of investors.

Bangladesh Economic Development Model （BED Model）

Bangladesh economy has experienced some stable growths with regard to major macroeconomic indicators, significant progress in social development indicators, rural infrastructure, investment in industrial agricultural sector, and poverty alleviation since the 1990s. Bangladesh has made major strides in its human development index （HDI）, population growth rate achieved to 1.3%, gross domestic product （GDP） over the last five years achieved more than 5% growth consecutively; as a result, it mooted the Brazil, Russia, India, China （BRIC） successors, otherwise known as the Next-11. The success has come through the miracle activities of micro finance （MF： Grameen Bank and non-governmental organizations （NGOs））, remittance from migrant worker （RMW）, and export of readymade garment （ERMG） products. These three （MF, RMW, and ERMG） as the ＂prime powers＂ or engines have been achieving to ＂bottom up＂ of social economic development. This paper reviews aspects of economic development in Bangladesh, analyzes three prime powers, and tries to find out a development pattern, vis-a-vis construct Bangladesh economic development model or BED model. Finally, this paper examines Bangladesh＇s development experience in light of the BED model. It has recognized MF model for poverty alleviation in the worldwide.